| 1. |
- Abbas, Muhammad, et al.
(författare)
-
Automated Reuse Recommendation of Product Line Assets Based on Natural Language Requirements
- 2020
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Ingår i: Lecture Notes in Computer Science. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030646936 ; , s. 173-189, s. 173-189, s. 173-189
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Konferensbidrag (refereegranskat)abstract
- Software product lines (SPLs) are based on reuse rationale to aid quick and quality delivery of complex products at scale. Deriving a new product from a product line requires reuse analysis to avoid redundancy and support a high degree of assets reuse. In this paper, we propose and evaluate automated support for recommending SPL assets that can be reused to realize new customer requirements. Using the existing customer requirements as input, the approach applies natural language processing and clustering to generate reuse recommendations for unseen customer requirements in new projects. The approach is evaluated both quantitatively and qualitatively in the railway industry. Results show that our approach can recommend reuse with 74% accuracy and 57.4% exact match. The evaluation further indicates that the recommendations are relevant to engineers and can support the product derivation and feasibility analysis phase of the projects. The results encourage further study on automated reuse analysis on other levels of abstractions.
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| 2. |
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| 3. |
- Abbas, Muhammad, et al.
(författare)
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Is Requirements Similarity a Good Proxy for Software Similarity? : An Empirical Investigation in Industry
- 2021
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Ingår i: <em>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) </em>27th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2021, 12 April 2021 - 15 April 2021. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030731274 ; , s. 3-18, s. 3-18
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Konferensbidrag (refereegranskat)abstract
- [Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman’s rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and categorization.
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| 4. |
- Abbas, Muhammad, et al.
(författare)
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Keywords-based test categorization for Extra-Functional Properties
- 2020
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Ingår i: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). - : IEEE. - 9781728110752 ; , s. 153-156
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Konferensbidrag (refereegranskat)abstract
- Categorizing existing test specifications can provide insights on coverage of the test suite to extra-functional properties. Manual approaches for test categorization can be time-consuming and prone to error. In this short paper, we propose a semi-automated approach for semantic keywords-based textual test categorization for extra-functional properties. The approach is the first step towards coverage-based test case selection based on extra-functional properties. We report a preliminary evaluation of industrial data for test categorization for safety aspects. Results show that keyword-based approaches can be used to categorize tests for extra-functional properties and can be improved by considering contextual information of keywords.
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| 5. |
- Abbas, Muhammad, et al.
(författare)
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Making Sense of Failure Logs in an Industrial DevOps Environment
- 2023
-
Ingår i: Advances in Intelligent Systems and Computing book series (AISC,volume 1445). - : Springer International Publishing. ; , s. 217-226
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Konferensbidrag (refereegranskat)abstract
- Processing and reviewing nightly test execution failure logs for large industrial systems is a tedious activity. Furthermore, multiple failures might share one root/common cause during test execution sessions, and the review might therefore require redundant efforts. This paper presents the LogGrouper approach for automated grouping of failure logs to aid root/common cause analysis and for enabling the processing of each log group as a batch. LogGrouper uses state-of-art natural language processing and clustering approaches to achieve meaningful log grouping. The approach is evaluated in an industrial setting in both a qualitative and quantitative manner. Results show that LogGrouper produces good quality groupings in terms of our two evaluation metrics (Silhouette Coefficient and Calinski-Harabasz Index) for clustering quality. The qualitative evaluation shows that experts perceive the groups as useful, and the groups are seen as an initial pointer for root cause analysis and failure assignment.
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| 6. |
- Abbas, Muhammad, et al.
(författare)
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MBRP : Model-Based Requirements Prioritization Using PageRank Algorithm
- 2019
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Ingår i: 2019 26th Asia-Pacific Software Engineering Conference (APSEC). - : IEEE conference proceedings. - 9781728146485 ; , s. 31-38
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Konferensbidrag (refereegranskat)abstract
- Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm's efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.
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| 7. |
- Abbas, Muhammad, et al.
(författare)
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On the relationship between similar requirements and similar software : A case study in the railway domain
- 2023
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Ingår i: Requirements Engineering. - : Springer Science and Business Media Deutschland GmbH. - 0947-3602 .- 1432-010X. ; 28, s. 23-47
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Tidskriftsartikel (refereegranskat)abstract
- Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.
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| 8. |
- Abbas, Muhammad, et al.
(författare)
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Product line adoption in industry : an experience report from the railway domain
- 2020
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Ingår i: SPLC '20: Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A. - New York, NY, USA : Association for Computing Machinery. - 9781450375696 ; , s. 130-141, s. 130-141
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Konferensbidrag (refereegranskat)abstract
- The software system controlling a train is typically deployed on various hardware architectures and must process various signals across those deployments. The increase of such customization scenarios and the needed adherence of the software to various safety standards in different application domains has led to the adoption of product line engineering within the railway domain. This paper explores the current state-of-practice of software product line development within a team developing industrial embedded software for a train propulsion control system. Evidence is collected using a focus group session with several engineers and through inspection of archival data. We report several benefits and challenges experienced during product line adoption and deployment. Furthermore, we identify and discuss improvement opportunities, focusing mainly on product line evolution and test automation.
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| 9. |
- Abbas, Muhammad, et al.
(författare)
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Requirements dependencies-based test case prioritization for extra-functional properties
- 2019
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Ingår i: Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728108889 ; , s. 159-163
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Konferensbidrag (refereegranskat)abstract
- The use of requirements' information in testing is a well-recognized practice in the software development life cycle. Literature reveals that existing tests prioritization and selection approaches neglected vital factors affecting tests priorities, like interdependencies between requirement specifications. We believe that models may play a positive role in specifying these inter-dependencies and prioritizing tests based on these inter-dependencies. However, till date, few studies can be found that make use of requirements inter-dependencies for test case prioritization. This paper uses a meta-model to aid modeling requirements, their related tests, and inter-dependencies between them. The instance of this meta-model is then processed by our modified PageRank algorithm to prioritize the requirements. The requirement priorities are then propagated to related test cases in the test model and test cases are selected based on coverage of extra-functional properties. We have demonstrated the applicability of our proposed approach on a small example case.
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| 10. |
- Abbas, Muhammad, et al.
(författare)
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Requirements-Driven Reuse Recommendation
- 2021
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Ingår i: Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A. - New York, NY, USA : Association for Computing Machinery.
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Konferensbidrag (refereegranskat)abstract
- This tutorial explores requirements-based reuse recommendation for product line assets in the context of clone-and-own product lines.
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| 11. |
- Abbas, Muhammad
(författare)
-
Requirements-Level Reuse Recommendation and Prioritization of Product Line Assets
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Software systems often target a variety of different market segments. Targeting varying customer requirements requires a product-focused development process. Software Product Line (SPL) engineering is one possible approach based on reuse rationale to aid quick delivery of quality product variants at scale. SPLs reuse common features across derived products while still providing varying configuration options. The common features, in most cases, are realized by reusable assets. In practice, the assets are reused in a clone-and-own manner to reduce the upfront cost of systematic reuse. Besides, the assets are implemented in increments, and requirements prioritization also has to be done. In this context, the manual reuse analysis and prioritization process become impractical when the number of derived products grows. Besides, the manual reuse analysis process is time-consuming and heavily dependent on the experience of engineers.In this licentiate thesis, we study requirements-level reuse recommendation and prioritization for SPL assets in industrial settings. We first identify challenges and opportunities in SPLs where reuse is done in a clone-and-own manner. We then focus on one of the identified challenges: requirements-based SPL assets reuse and provide automated support for identifying reuse opportunities for SPL assets based on requirements. Finally, we provide automated support for requirements prioritization in the presence of dependencies resulting from reuse.
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| 12. |
- Abbaspour Asadollah, Sara, et al.
(författare)
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A Model for Systematic Monitoring and Debugging of Starvation Bugs in Multicore Software
- 2016
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Ingår i: Proceedings of the 1st International Workshop on Specification, Comprehension, Testing, and Debugging of Concurrent Programs (SCTDCP 2016). - New York, NY, USA : ACM. - 9781450345101 ; , s. 7-11
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Konferensbidrag (refereegranskat)abstract
- With the development of multicore hardware, concurrent, parallel and multicore software are becoming increasingly popular. Software companies are spending a huge amount of time and resources to nd and debug the bugs. Among all types of software bugs, concurrency bugs are also important and troublesome. This type of bugs is increasingly becoming an issue particularly due to the growing prevalence of multicore hardware. In this position paper, we propose a model for monitoring and debugging Starvation bugs as a type of concurrency bugs in multicore software. The model is composed into three phases: monitoring, detecting and debugging. The monitoring phase can support detecting phase by storing collected data from the system execution. The detecting phase can support debugging phase by comparing the stored data with starvation bug's properties, and the debugging phase can help in reproducing and removing the Starvation bug from multicore software. Our intention is that our model is the basis for developing tool(s) to enable solving Starvation bugs in software for multicore platforms.
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| 13. |
- Asadi, Nima, et al.
(författare)
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Run-Time Monitoring of Timing Constraints : A Survey of Methods and Tools
- 2013
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Konferensbidrag (refereegranskat)abstract
- Despite the availability of static analysis methods to achieve a correct-by-construction design for different systems in terms of timing behavior, violations of timing constraints can still occur at run-time due to different reasons. The aim of monitoring of system performance with respect to the timing constraints is to detect the violations of timing specifications, or to predict them based on the current system performance data. Considerable work has been dedicated to suggesting efficient performance monitoring approaches during the past years. This paper presents a survey and classification of those approaches in order to help researchers gain a better view over different methods and developments in monitoring of timing behavior of systems. Classifications of the mentioned approaches are given based on different items that are seen as important in developing a monitoring system, i.e. the use of additional hardware, the data collection approach, etc. Moreover, a description of how these different methods work is presented in this paper along with the advantages and downsides of each of them.
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| 14. |
- Bashir, Sarmad, et al.
(författare)
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Requirement or Not, That is the Question : A Case from the Railway Industry
- 2023
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Ingår i: <em>Lecture Notes in Computer Science. </em>Volume 13975. Pages 105 - 121 2023. - : Springer Science and Business Media Deutschland GmbH. - 9783031297854 ; , s. 105-121
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Konferensbidrag (refereegranskat)abstract
- Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data. There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area. © 2023, The Author(s)
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| 15. |
- Bashir, Sarmad, et al.
(författare)
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Requirements Classification for Smart Allocation : A Case Study in the Railway Industry
- 2023
-
Ingår i: 31st IEEE International Requirements Engineering Conference. - Hannover, Germany : IEEE.
-
Konferensbidrag (refereegranskat)abstract
- Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.
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| 16. |
- Bradbury, Jeremy, et al.
(författare)
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ToCaMS – Workshop on Testing of Configurable and Multi-variant Systems
- 2020
-
Ingår i: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).
-
Konferensbidrag (refereegranskat)abstract
- Due to increasing market diversification and customer demand, more and more software-based products and services are customizable or are designed in the form of many different variants. This brings about new challenges for the software quality assurance processes: How shall the variability be modelled in order to make sure that all features are being tested? Is it better to test selected variants on a concrete level, or can the generic software and baseline be tested abstractly? Can knowledge-based AI techniques be used to identify and prioritize test cases? How can the quality of a generic test suite be assessed? What are appropriate coverage criteria for configurable modules? If it is impossible to test all possible variants, which products and test cases should be selected for test execution? Can security-testing methods be leveraged to an abstract level?
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| 17. |
- Bucaioni, Alessio, 1987-, et al.
(författare)
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Model-based Automation of Test Script Generation Across Product Variants: a Railway Perspective
- 2021
-
Ingår i: 2nd ACM/IEEE International Conference on Automation of Software Test AST 2021. - 9781665435673 ; , s. 20-29
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Konferensbidrag (refereegranskat)abstract
- In this work, we report on our experience indefining and applying a model-based approach for the automaticgeneration of test scripts for product variants in software productlines. The proposed approach is the result of an effort leveragingthe experiences and results from the technology transfer activitieswith our industrial partner Bombardier Transportation. Theproposed approach employs metamodelling and model transfor-mations for representing different testing artefacts and makingtheir generation automatic. We demonstrate the industrial ap-plicability and efficiency of the proposed approach using theBombardier Transportation Aventra software product line. Weobserve that the proposed approach mitigates the developmenteffort, time consumption and consistency drawbacks typical oftraditional strategies.
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| 18. |
- Bucaioni, Alessio, 1987-, et al.
(författare)
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Model-based generation of test scripts across product variants : An experience report from the railway industry
- 2022
-
Ingår i: Journal of Software. - : John Wiley and Sons Ltd. - 2047-7473 .- 2047-7481. ; 34:11
-
Tidskriftsartikel (refereegranskat)abstract
- Software product line engineering emerged as an effective approach for the development of families of software-intensive systems in several industries. Although its use has been widely discussed and researched, there are still several open challenges for its industrial adoption and application. One of these is how to efficiently develop and reuse shared software artifacts, which have dependencies on the underlying electrical and hardware systems of products in a family. In this work, we report on our experience in tackling such a challenge in the railway industry and present a model-based approach for the automatic generation of test scripts for product variants in software product lines. The proposed approach is the result of an effort leveraging the experiences and results from the technology transfer activities with our industrial partner Alstom SA in Sweden. We applied and evaluated the proposed approach on the Aventra software product line from Alstom SA. The evaluation showed that the proposed approach mitigates the development effort, development time, and consistency drawbacks associated with the traditional, manual creation of test scripts. We performed an online survey involving 37 engineers from Alstom SA for collecting feedback on the approach. The result of the survey further confirms the aforementioned benefits. © 2022 The Authors.
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| 19. |
- Bucaioni, Alessio, 1987-, et al.
(författare)
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Reference architectures modelling and compliance checking
- 2022
-
Ingår i: Journal of Software. - : WILEY. - 2047-7473 .- 2047-7481.
-
Tidskriftsartikel (refereegranskat)abstract
- Reference architectures (RAs) are successfully used to represent families of concrete software architectures in several domains such as automotive, banking, and the Internet of Things. RAs inspire architects when designing concrete architectures, and they help to guarantee compliance with architectural decisions, regulatory requirements, as well as architectural qualities. Despite their importance, reference architectures still suffer from a number of open technical issues, including (i) the lack of a common interpretation, a precise notation for their representation and documentation, and (ii) the lack of conformance mechanisms for checking the compliance of concrete architectures to their related reference architecture, architectural decisions, regulatory requirements, etc. This paper addresses these two issues by introducing a model-driven approach that leverages (i) a domain-independent metamodel for the representation of reference architectures and (ii) the combination of model transformation and weaving techniques for the automatic conformance checking of concrete architectures. We evaluate the applicability, effectiveness, and generalizability of our approach using illustrative examples from the web browsers and automotive domains, including an assessment from an independent practitioner.
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| 20. |
- Campeanu, Gabriel, et al.
(författare)
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A 2-Layer Component-based Architecture for Heterogeneous CPU-GPU Embedded Systems
- 2016. - 14
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Ingår i: Information Technology: New Generations. - Cham : Springer International Publishing. - 9783319324661 ; , s. 629-639
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Konferensbidrag (refereegranskat)abstract
- Traditional embedded systems are evolving into heterogeneous systems in order to address new and more demanding software requirements. Modern embedded systems are constructed by combining different computation units, such as traditional CPUs, with Graphics Processing Units (GPUs). Adding GPUs to conventional CPU-based embedded systems enhances the computation power but also increases the complexity in developing software applications. A method that can help to tackle and address the software complexity issue of heterogeneous systems is component-based development. The allocation of the software application onto the appropriate computation node is greatly influenced by the system information load. The allocation process is increased in difficulty when we use, instead of common CPU-based systems, complex CPU-GPU systems. This paper presents a 2-layer component-based architecture for heterogeneous embedded systems, which has the purpose to ease the software-to-hardware allocation process. The solution abstracts the CPU-GPU detailed component-based design into single software components in order to decrease the amount of information delivered to the allocator. The last part of the paper describes how the allocation process may be modified using our proposed solution, when applied on a real system demonstrator.
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| 21. |
- Campeanu, Gabriel, et al.
(författare)
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A Two-Layer Component-Based Allocation for Embedded Systems with GPUs
- 2019
-
Ingår i: designs. - Switzerland : MDPI AG. - 2411-9660. ; 3:1, s. 1-14
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Component-based development is a software engineering paradigm that can facilitate the construction of embedded systems and tackle its complexities. The modern embedded systems have more and more demanding requirements. One way to cope with such a versatile and growing set of requirements is to employ heterogeneous processing power, i.e., CPU–GPU architectures. The new CPU–GPU embedded boards deliver an increased performance but also introduce additional complexity and challenges. In this work, we address the component-to-hardware allocation for CPU–GPU embedded systems. The allocation for such systems is much complex due to the increased amount of GPU-related information. For example, while in traditional embedded systems the allocation mechanism may consider only the CPU memory usage of components to find an appropriate allocation scheme, in heterogeneous systems, the GPU memory usage needs also to be taken into account in the allocation process. This paper aims at decreasing the component-to-hardware allocation complexity by introducing a two-layer component-based architecture for heterogeneous embedded systems. The detailed CPU–GPU information of the system is abstracted at a high-layer by compacting connected components into single units that behave as regular components. The allocator, based on the compacted information received from the high-level layer, computes, with a decreased complexity, feasible allocation schemes. In the last part of the paper, the two-layer allocation method is evaluated using an existing embedded system demonstrator; namely, an underwater robot.
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| 22. |
- Campeanu, Gabriel, et al.
(författare)
-
Run-time component allocation in CPU-GPU embedded systems
- 2017
-
Ingår i: Proceedings of the ACM Symposium on Applied Computing. - New York, NY, USA : ACM. - 9781450344869 ; , s. 1259-1265
-
Konferensbidrag (refereegranskat)abstract
- Nowadays, many of the modern embedded applications such as vehicles and robots, interact with the environment and receive huge amount of data through various sensors such as cameras and radars. The challenge of processing large amount of data, within an acceptable performance, is solved by employing embedded systems that incorporate complementary attributes of CPUs and Graphics Processing Units (GPUs), i.e., sequential and parallel execution models.component-based development (CBD) is a software engineering methodology that augments the applications development through reuse of software blocks known as components. In developing a CPU-GPU embedded application using CBD, allocation of components to different processing units of the platform is an important activity which can affect the overall performance of the system. In this context, there is also often the need to support and achieve run-time component allocation due to various factors and situations that can happen during system execution, such as switching off parts of the system for energy saving. In this paper, we provide a solution that dynamically allocates components using various system information such as the available resources (e.g., available GPU memory) and the software behavior (e.g., in terms of GPU memory usage). The novelty of our work is a formal allocation model that considers GPU system characteristics computed on-the-fly through software monitoring solutions. For the presentation and validation of our solution, we utilize an existing underwater robot demonstrator.
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| 23. |
- Ciccozzi, Federico, et al.
(författare)
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An automated round-trip support towards deployment assessment in component-based embedded systems
- 2013
-
Ingår i: CBSE 2013 - Proceedings of the 16th ACM SIGSOFT Symposium on Component Based Software Engineering, 2013. - New York, NY, USA : ACM. - 9781450321228 ; , s. 179-188
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Konferensbidrag (refereegranskat)abstract
- Synergies between model-driven and component-based software engineering have been indicated as promising to mitigate complexity in development of embedded systems. In this work we evaluate the usefulness of a model-driven round-trip approach to aid deployment optimization in the development of embedded component-based systems. The round-trip approach is composed of the following steps: modelling the system, generation of full code from the models, execution and monitoring the code execution, and finally back-propagation of monitored values to the models. We illustrate the usefulness of the round-trip approach exploiting an industrial case-study from the telecom-domain. We use a code-generator that can realise different deployment strategies, as well as special monitoring code injected into the generated code, and monitoring primitives defined at operating system level. Given this infrastructure we can evaluate extra-functional properties of the system and thus compare different deployment strategies.
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| 24. |
- Corcoran, Diarmuid, 1969-, et al.
(författare)
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Toward a Tailored Modeling of Non-Functional Requirements for Telecommunication Systems
- 2011
-
Ingår i: 2011 Eighth International Conference on Information Technology: New Generations. - 9781612844275
-
Konferensbidrag (refereegranskat)abstract
- Addressing non-functional requirements in Real-Time Embedded Systems (RTES) is of critical importance. Proper functionality of the whole system is heavily dependent on satisfying these requirements. In model-based approaches for development of the systems in RTES domain, there are several methods and languages for modeling and analysis of non-functional requirements. However, in this domain there are different types of systems that have different sets of non-functional requirements. The problem is that the general modeling approaches for RTES may not cover all the needs of these sub domains such as telecommunication. In this poster paper, we suggest an approach to complement and apply general RTES modeling languages to better cover different non-functional requirements of telecommunication systems.
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| 25. |
- Ferrari, Fabiano C., et al.
(författare)
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On transforming model-based tests into code : A systematic literature review
- 2023
-
Ingår i: Software testing, verification & reliability. - : John Wiley & Sons. - 0960-0833 .- 1099-1689. ; 33:8
-
Forskningsöversikt (refereegranskat)abstract
- Model-based test design is increasingly being applied in practice and studied in research. Model-based testing (MBT) exploits abstract models of the software behaviour to generate abstract tests, which are then transformed into concrete tests ready to run on the code. Given that abstract tests are designed to cover models but are run on code (after transformation), the effectiveness of MBT is dependent on whether model coverage also ensures coverage of key functional code. In this article, we investigate how MBT approaches generate tests from model specifications and how the coverage of tests designed strictly based on the model translates to code coverage. We used snowballing to conduct a systematic literature review. We started with three primary studies, which we refer to as the initial seeds. At the end of our search iterations, we analysed 30 studies that helped answer our research questions. More specifically, this article characterizes how test sets generated at the model level are mapped and applied to the source code level, discusses how tests are generated from the model specifications, analyses how the test coverage of models relates to the test coverage of the code when the same test set is executed and identifies the technologies and software development tasks that are on focus in the selected studies. Finally, we identify common characteristics and limitations that impact the research and practice of MBT: (i) some studies did not fully describe how tools transform abstract tests into concrete tests, (ii) some studies overlooked the computational cost of model-based approaches and (iii) some studies found evidence that bears out a robust correlation between decision coverage at the model level and branch coverage at the code level. We also noted that most primary studies omitted essential details about the experiments.
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| 26. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning
- 2018
-
Ingår i: ACM/IEEE 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2018, , co-located with International Conference on Software Engineering, ICSE 2018; Gothenburg; Sweden; 28 May 2018 through 29 May 2018; Code 138312. - New York, NY, USA : ACM. ; , s. 217-223
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Konferensbidrag (refereegranskat)abstract
- Timing requirements such as constraints on response time are key characteristics of real-time systems and violations of these requirements might cause a total failure, particularly in hard real-time systems. Runtime monitoring of the system properties is of great importance to detect and mitigate such failures. Thus, a runtime control to preserve the system properties could improve the robustness of the system with respect to timing violations. Common control approaches may require a precise analytical model of the system which is difficult to be provided at design time. Reinforcement learning is a promising technique to provide adaptive model-free control when the environment is stochastic, and the control problem could be formulated as a Markov Decision Process. In this paper, we propose an adaptive runtime control using reinforcement learning for real-time programs based on Programmable Logic Controllers (PLCs), to meet the response time requirements. We demonstrate through multiple experiments that our approach could control the response time efficiently to satisfy the timing requirements.
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| 27. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning
- 2022
-
Ingår i: Software quality journal. - : Springer. - 0963-9314 .- 1573-1367. ; , s. 127-159
-
Tidskriftsartikel (refereegranskat)abstract
- Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case-based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments in a simulated performance testing setup, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process and performs adaptively without access to source code and performance models. © 2021, The Author(s).
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| 28. |
- Helali Moghadam, Mahshid
(författare)
-
Intelligence-Driven Software Performance Assurance
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Software performance assurance is of great importance for the success of software products, which are nowadays involved in many parts of our life. Performance evaluation approaches such as performance modeling, testing, as well as runtime performance control methods, all can contribute to the realization of software performance assurance. Many of the common approaches to tackle challenges in this area involve relying on performance models or using system models and source code. Although modeling provides a deep insight into the system behavior, developing a detailed model is challenging. Furthermore, software artifacts such as models and source code might not be readily available at all times in the development lifecycle. This thesis focuses on leveraging the potential of machine learning (ML) and evolutionary search-based techniques to provide viable solutions for addressing the challenges in different aspects of software performance assurance efficiently and effectively.In this thesis, we first investigate the capabilities of model-free reinforcement learning to address the objectives in robustness testing problems. We develop two self-adaptive reinforcement learning-driven test agents called SaFReL and RELOAD. They generate effective platform-based test scenarios and test workloads, respectively. The output scenarios and workloads help testers and software engineers meet their objectives efficiently without relying on models or source code. SaFReL and RELOAD learn the optimal policies (ways) to meet the test objectives and can reuse the learned policies adaptively in other testing settings. Policy reuse can lead to higher test efficiency and cost savings, for example, when testing similar test objectives or software systems with comparable performance sensitivity.Next, we leverage the potential of evolutionary computation algorithms, i.e., genetic algorithms, evolution strategies, and particle swarm optimization, to generate failure-revealing test scenarios for robustness testing of AI systems. In this part, we choose autonomous driving systems as a prevailing example of contemporary AI systems. We study the efficacy of the proposed evolutionary search-based test generation techniques and evaluate primarily to what extent they can trigger failures. Moreover, we investigate the diversity of those failures and compare them to existing baseline solutions. Finally, we again use the potential of model-free reinforcement learning to develop adaptive ML-driven runtime performance control approaches. We present a response time preservation method for a sample type of industrial applications and a resource allocation technique for dynamic workloads in a data grid application. The proposed ML-driven techniques learn how to adjust the tunable parameters and resource configuration at runtime to keep the performance continually compliant with the requirements and to further optimize the runtime performance. We evaluate the efficacy of the approaches and show how effectively they can improve the performance and keep the performance requirements satisfied under varying conditions such as dynamic workloads and the occurrence of runtime events that lead to substantial response time deviations.
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| 29. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
Intelligent Load Testing: Self-adaptive Reinforcement Learning-driven Load Runner
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Load testing with the aim of generating an effective workload to identify performance issues is a time-consuming and complex challenge, particularly for evolving software systems. Current automated approaches mainly rely on analyzing system models and source code, or modeling of the real system usage. However, that information might not be available all the time or obtaining it might require considerable effort. On the other hand, if the optimal policy for generating the proper test workload resulting in meeting the objectives of the testing can be learned by the testing system, testing would be possible without access to system models or source code. We propose a self-adaptive reinforcement learning-driven load testing agent that learns the optimal policy for test workload generation. The agent can reuse the learned policy in subsequent testing activities such as meeting different types of testing targets. It generates an efficient test workload resulting in meeting the objective of the testing adaptively without access to system models or source code. Our experimental evaluation shows that the proposed self-adaptive intelligent load testing can reach the testing objective with lower cost in terms of the workload size, i.e. the number of generated users, compared to a typical load testing process, and results in productivity benefits in terms of higher efficiency.
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| 30. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
Learning-based Response Time Analysis in Real-Time Embedded Systems : A Simulation-based Approach
- 2018
-
Ingår i: 1st International Workshop on Software Qualities and their Dependencies, located at the International Conference of Software Engineering (ICSE) 2018 SQUADE'18. - New York, NY, USA : ACM. - 9781450357371 ; , s. 21-24
-
Konferensbidrag (refereegranskat)abstract
- Response time analysis is an essential task to verify the behavior of real-time systems. Several response time analysis methods have been proposed to address this challenge, particularly for real-time systems with different levels of complexity. Static analysis is a popular approach in this context, but its practical applicability is limited due to the high complexity of the industrial real-time systems, as well as many unpredictable runtime events in these systems. In this work-in-progress paper, we propose a simulationbased response time analysis approach using reinforcement learning to find the execution scenarios leading to the worst-case response time. The approach learns how to provide a practical estimation of the worst-case response time through simulating the program without performing static analysis. Our initial study suggests that the proposed approach could be applicable in the simulation environments of the industrial real-time control systems to provide a practical estimation of the execution scenarios leading to the worst-case response time.
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| 31. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
Learning-Based Self-Adaptive Assurance of Timing Properties in a Real-Time Embedded System
- 2018
-
Ingår i: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'18. - 9781538663523 ; , s. 77-80
-
Konferensbidrag (refereegranskat)abstract
- Providing an adaptive runtime assurance technique to meet the performance requirements of a real-time system without the need for a precise model could be a challenge. Adaptive performance assurance based on monitoring the status of timing properties can bring more robustness to the underlying platform. At the same time, the results or the achieved policy of this adaptive procedure could be used as feedback to update the initial model, and consequently for producing proper test cases. Reinforcement-learning has been considered as a promising adaptive technique for assuring the satisfaction of the performance properties of software-intensive systems in recent years. In this work-in-progress paper, we propose an adaptive runtime timing assurance procedure based on reinforcement learning to satisfy the performance requirements in terms of response time. The timing control problem is formulated as a Markov Decision Process and the details of applying the proposed learning-based timing assurance technique are described.
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| 32. |
- Helali Moghadam, Mahshid
(författare)
-
Machine Learning-Assisted Performance Assurance
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With the growing involvement of software systems in our life, assurance of performance, as an important quality characteristic, rises to prominence for the success of software products. Performance testing, preservation, and improvement all contribute to the realization of performance assurance. Common approaches to tackle challenges in testing, preservation, and improvement of performance mainly involve techniques relying on performance models or using system models or source code. Although modeling provides a deep insight into the system behavior, drawing a well-detailed model is challenging. On the other hand, those artifacts such as models and source code might not be available all the time. These issues are the motivations for using model-free machine learning techniques such as model-free reinforcement learning to address the related challenges in performance assurance.Reinforcement learning implies that if the optimal policy (way) for achieving the intended objective in a performance assurance process could instead be learnt by the acting system (e.g., the tester system), then the intended objective could be accomplished without advanced performance models. Furthermore, the learnt policy could later be reused in similar situations, which leads to efficiency improvement by saving computation time while reducing the dependency on the models and source code.In this thesis, our research goal is to develop adaptive and efficient performance assurance techniques meeting the intended objectives without access to models and source code. We propose three model-free learning-based approaches to tackle the challenges; efficient generation of performance test cases, runtime performance (response time) preservation, and performance improvement in terms of makespan (completion time) reduction. We demonstrate the efficiency and adaptivity of our approaches based on experimental evaluations conducted on the research prototype tools, i.e. simulation environments that we developed or tailored for our problems, in different application areas.
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| 33. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
Machine Learning Testing in an ADAS Case Study Using Simulation-Integrated Bio-Inspired Search-Based Testing
- 2022
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (μ+ λ) and (μ,λ) evolution strategies(ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.
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| 34. |
- Helali Moghadam, Mahshid, et al.
(författare)
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Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing
- 2024
-
Ingår i: Journal of Software. - : John Wiley and Sons Ltd. - 2047-7473 .- 2047-7481. ; :5
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints.
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| 35. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
Machine Learning to Guide Performance Testing : An Autonomous Test Framework
- 2019
-
Ingår i: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 2019.
-
Konferensbidrag (refereegranskat)abstract
- Satisfying performance requirements is of great importance for performance-critical software systems. Performance analysis to provide an estimation of performance indices and ascertain whether the requirements are met is essential for achieving this target. Model-based analysis as a common approach might provide useful information but inferring a precise performance model is challenging, especially for complex systems. Performance testing is considered as a dynamic approach for doing performance analysis. In this work-in-progress paper, we propose a self-adaptive learning-based test framework which learns how to apply stress testing as one aspect of performance testing on various software systems to find the performance breaking point. It learns the optimal policy of generating stress test cases for different types of software systems, then replays the learned policy to generate the test cases with less required effort. Our study indicates that the proposed learning-based framework could be applied to different types of software systems and guides towards autonomous performance testing.
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| 36. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent
- 2021
-
Ingår i: 2021 IEEE Congress on Evolutionary Computation (CEC). - 9781728183930 ; , s. 2385-2394
-
Konferensbidrag (refereegranskat)abstract
- Performance testing with the aim of generating an efficient and effective workload to identify performance issues is challenging. Many of the automated approaches mainly rely on analyzing system models, source code, or extracting the usage pattern of the system during the execution. However, such information and artifacts are not always available. Moreover, all the transactions within a generated workload do not impact the performance of the system the same way, a finely tuned workload could accomplish the test objective in an efficient way. Model-free reinforcement learning is widely used for finding the optimal behavior to accomplish an objective in many decision-making problems without relying on a model of the system. This paper proposes that if the optimal policy (way) for generating test workload to meet a test objective can be learned by a test agent, then efficient test automation would be possible without relying on system models or source code. We present a self-adaptive reinforcement learning-driven load testing agent, RELOAD, that learns the optimal policy for test workload generation and generates an effective workload efficiently to meet the test objective. Once the agent learns the optimal policy, it can reuse the learned policy in subsequent testing activities. Our experiments show that the proposed intelligent load test agent can accomplish the test objective with lower test cost compared to common load testing procedures, and results in higher test efficiency.
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| 37. |
- Helali Moghadam, Mahshid, et al.
(författare)
-
Poster : Performance Testing Driven by Reinforcement Learning
- 2020
-
Ingår i: 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728157771 ; , s. 402-405
-
Konferensbidrag (refereegranskat)abstract
- Performance testing remains a challenge, particularly for complex systems. Different application-, platform- and workload-based factors can influence the performance of software under test. Common approaches for generating platform- and workload-based test conditions are often based on system model or source code analysis, real usage modeling and use-case based design techniques. Nonetheless, creating a detailed performance model is often difficult, and also those artifacts might not be always available during the testing. On the other hand, test automation solutions such as automated test case generation can enable effort and cost reduction with the potential to improve the intended test criteria coverage. Furthermore, if the optimal way (policy) to generate test cases can be learnt by testing system, then the learnt policy can be reused in further testing situations such as testing variants, evolved versions of software, and different testing scenarios. This capability can lead to additional cost and computation time saving in the testing process. In this research, we present an autonomous performance testing framework which uses a model-free reinforcement learning augmented by fuzzy logic and self-adaptive strategies. It is able to learn the optimal policy to generate platform- and workload-based test conditions which result in meeting the intended testing objective without access to system model and source code. The use of fuzzy logic and self-adaptive strategy helps to tackle the issue of uncertainty and improve the accuracy and adaptivity of the proposed learning. Our evaluation experiments show that the proposed autonomous performance testing framework is able to generate the test conditions efficiently and in a way adaptive to varying testing situations.
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| 38. |
- Hänninen, Kaj, 1970-, et al.
(författare)
-
Inadequate risk analysis might jeopardize the functional safety of modern systems
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In the early 90s, researchers began to focus on security as an important property to address in combination with safety. Over the years, researchers have proposed approaches to harmonize activities within the safety and security disciplines. Despite the academic efforts to identify interdependencies and to propose combined approaches for safety and security, there is still a lack of integration between safety and security practices in the industrial context, as they have separate standards and independent processes often addressed and assessed by different organizational teams and authorities. Specifically, security concerns are generally not covered in any detail in safety standards potentially resulting in successfully safety-certified systems that still are open for security threats from e.g., malicious intents from internal and external personnel and hackers that may jeopardize safety. In recent years security has again received an increasing attention of being an important issue also in safety assurance, as the open interconnected nature of emerging systems makes them susceptible to security threats at a much higher degree than existing more confined products.This article presents initial ideas on how to extend safety work to include aspects of security during the context establishment and initial risk assessment procedures. The ambition of our proposal is to improve safety and increase efficiency and effectiveness of the safety work within the frames of the current safety standards, i.e., raised security awareness in compliance with the current safety standards. We believe that our proposal is useful to raise the security awareness in industrial contexts, although it is not a complete harmonization of safety and security disciplines, as it merely provides applicable guidance to increase security awareness in a safety context
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| 39. |
- Kiss, Akos, et al.
(författare)
-
13th Workshop on Automating Test Case Design, Selection and Evaluation (A-TEST 2022) Co-Located with ESEC/FSE Conference
- 2023
-
Ingår i: Software Engineering Notes. - : Association for Computing Machinery. - 0163-5948 .- 1943-5843. ; 48:1, s. 76-78
-
Tidskriftsartikel (refereegranskat)abstract
- The Workshop on Automating Test Case Design, Selection and Evaluation (A-TEST) has provided a venue for researchers and industry members alike to exchange and discuss trending views, ideas, state of the art, work in progress, and scientific results on automated testing. Up until now it has run 13 editions since 2009. The 13th edition of the A-TEST workshop has been performed as an in-person workshop in Singapore during 17 to 18 of November, 2022. This edition of the A-TEST workshop was co-located with ESEC/FSE 2022 conference.
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| 40. |
- Lisper, Björn, et al.
(författare)
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Targeted Mutation : Efficient Mutation Analysis for Testing Non-Functional Properties
- 2017
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Ingår i: Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017. - : Institute of Electrical and Electronics Engineers Inc.. - 9781509066766 - 9781509066773 ; , s. 65-68
-
Konferensbidrag (refereegranskat)abstract
- Mutation analysis has proven to be a strong technique for software testing. Unfortunately, it is also computationally expensive and researchers have therefore proposed several different approaches to reduce the effort. None of these reduction techniques however, focuses on non-functional properties. Given that our goal is to create a strong test suite for testing a certain non-functional property, which mutants should be used? In this paper, we introduce the concept of targeted mutation, which focuses mutation effort to those parts of the code where a change can make a difference with respect to the targeted non-functional property. We show how targeted mutation can be applied to derive efficient test suites for estimating the Worst-Case Execution Time (WCET). We use program slicing to direct the mutations to the parts of the code that are likely to have the strongest influence on execution time. Finally, we outline an experimental procedure for how to evaluate the technique.
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| 41. |
- Marinescu, Raluca, et al.
(författare)
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A Model-Based Testing Framework for Automotive Embedded Systems
- 2014
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Ingår i: The 40th Euromicro Conference on Software Engineering and Advanced Applications SEAA 2014. - Verona, Italy.
-
Konferensbidrag (refereegranskat)abstract
- Architectural models, such as those described in the EAST-ADL language, represent convenient abstractions to reason about automotive embedded software systems. To enjoy the fully-fledged advantages of reasoning, EAST-ADL models could benefit from a component-aware analysis framework that provides, ideally, both verification and model-based test-case generation capabilities. While different verification techniques have been developed for architectural models, only a few target EAST-ADL. In this paper, we present a methodology for code validation, starting from EAST-ADL artifacts. The methodology relies on: (i) automated model-based test-case generation for functional requirements criteria based on the EAST-ADL model extended with timed automata semantics, and (ii) validation of system implementation by generating Python test scripts based on the abstract test-cases, which represent concrete test-cases that are executable on the system implementation. We apply our methodology to analyze the ABS function implementation of a Brake-by-Wire system prototype.
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| 42. |
- Marinescu, Raluca, et al.
(författare)
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EAST-ADL Tailored Testing : From System Models to Executable Test Cases
- 2013
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Architectural models, such as those described in the EAST-ADL language, represent convenient abstractions to reason about embedded software systems. To enjoy the fully-fledged advantages of reasoning, EAST-ADL models require a component-aware analysis framework that provide, ideally, both verification and model-based test-case generation capabilities. In this paper, we extend ViTAL, our recently developed tool-supported framework for model-checking EAST-ADL models in Uppaal Port, with automated model-based test-case generation for functional requirements criteria. To validate the actual system implementation and exercise the feasibility of the abstract test-cases, we also show how to generate Python test scripts, from the ViTAL generated abstract test-cases. The scripts define the concrete test-cases that are executable on the system implementation, within the Farkle testing environment. Tool interoperability between ViTAL and Farkle is ensured by implementing a corresponding interface, compliant with the Open Services for Lifecycle collaboration (OSLC) standard. We apply our methodology to validate the ABS function implementation of a Brake-by-Wire system prototype.
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| 43. |
- Moravvej, S. V., et al.
(författare)
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An LSTM-Based Plagiarism Detection via Attention Mechanism and a Population-Based Approach for Pre-training Parameters with Imbalanced Classes
- 2021
-
Ingår i: Lect. Notes Comput. Sci.. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030922375 ; , s. 690-701
-
Konferensbidrag (refereegranskat)abstract
- Plagiarism is one of the leading problems in academic and industrial environments, which its goal is to find the similar items in a typical document or source code. This paper proposes an architecture based on a Long Short-Term Memory (LSTM) and attention mechanism called LSTM-AM-ABC boosted by a population-based approach for parameter initialization. Gradient-based optimization algorithms such as back-propagation (BP) are widely used in the literature for learning process in LSTM, attention mechanism, and feed-forward neural network, while they suffer from some problems such as getting stuck in local optima. To tackle this problem, population-based metaheuristic (PBMH) algorithms can be used. To this end, this paper employs a PBMH algorithm, artificial bee colony (ABC), to moderate the problem. Our proposed algorithm can find the initial values for model learning in all LSTM, attention mechanism, and feed-forward neural network, simultaneously. In other words, ABC algorithm finds a promising point for starting BP algorithm. For evaluation, we compare our proposed algorithm with both conventional and population-based methods. The results clearly show that the proposed method can provide competitive performance.
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| 44. |
- Mousavirad, Seyed, et al.
(författare)
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A population-based automatic clustering algorithm for image segmentation
- 2021
-
Ingår i: GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450383516 ; , s. 1931-1936
-
Konferensbidrag (refereegranskat)abstract
- Clustering is one of the prominent approaches for image segmentation. Conventional algorithms such as k-means, while extensively used for image segmentation, suffer from problems such as sensitivity to initialisation and getting stuck in local optima. To overcome these, population-based metaheuristic algorithms can be employed. This paper proposes a novel clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a powerful population-based algorithm to tackle optimisation problems. One of the advantages of our proposed algorithm is that it does not require any information about the number of clusters. To verify the effectiveness of our proposed algorithm, we present a set of experiments based on objective function evaluation and image segmentation criteria to show that our proposed algorithm outperforms existing approaches.
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| 45. |
- Mousavirad, S. J., et al.
(författare)
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HMS-OS : Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space
- 2021
-
Ingår i: 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728190488
-
Konferensbidrag (refereegranskat)abstract
- The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems. It is based on three main operators: mental search, grouping, and movement. In the original HMS algorithm, a clustering algorithm is used to group the current population in order to identify a promising region in search space, while candidate solutions then move towards the best candidate solution in the promising region. In this paper, we propose a novel HMS algorithm, HMS-OS, which is based on clustering in both objective and search space, where clustering in objective space finds a set of best candidate solutions whose centroid is then also used in updating the population. For further improvement, HMS-OS benefits from an adaptive selection of the number of mental processes in the mental search operator. Experimental results on CEC-2017 benchmark functions with dimensionalities of 50 and 100, and in comparison to other optimisation algorithms, indicate that HMS-OS yields excellent performance, superior to those of other methods.
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| 46. |
- Mousavirad, S. J., et al.
(författare)
-
RWS-L-SHADE : An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation
- 2022
-
Ingår i: Lecture Notes in Computer Science, vol. 13324. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031024610 ; , s. 255-268
-
Konferensbidrag (refereegranskat)abstract
- Differential evolution (DE) is widely used for global optimisation problems due to its simplicity and efficiency. L-SHADE is a state-of-the-art variant of DE algorithm that incorporates external archive, success-history-based parameter adaptation, and linear population size reduction. L-SHADE uses a current-to-pbest/1/bin strategy for mutation operator, while all individuals have the same probability to be selected. In this paper, we propose a novel L-SHADE algorithm, RWS-L-SHADE, based on a roulette wheel selection strategy so that better individuals have a higher priority and worse individuals are less likely to be selected. Our extensive experiments on the CEC-2017 benchmark functions and dimensionalities of 30, 50 and 100 indicate that RWS-L-SHADE outperforms L-SHADE.
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| 47. |
- Muellner, Nils, et al.
(författare)
-
Simulation-Based Safety Testing Brake-by-Wire
- 2017
-
Ingår i: Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017. - 9781509066766 ; , s. 61-64
-
Konferensbidrag (refereegranskat)abstract
- Mechanical systems in cars are replaced by electronic equivalents. To be authorized for the road, validation that the replacements are at least as good as the old systems is required. For electronic braking systems (brake-by-wire), this goodness translates to safety in terms of maintaining timing constraints. Yet, in the future, the safety of braking maneuvres will depend, not only, on electronic brakes, but also on cooperative driving maneuvres orchestrated among many cars. Connecting both brake-by-wire on the microscopic level with cooperative braking on the macroscopic level allows for determining safety on a broader scale, as both systems feed from the same resource: Time. This paper discusses work-in-progress, introducing and combining two threads: electronic brakes and cooperative braking. Discussing safety on two levels simultaneously motivates connecting a Simulink model of an electronic brake-by-wire system with the traffic simulator SUMO for conducting the required combined validation. How safe is a car in relation to a given maximal braking distance? What is the optimal distribution of reaction time between electronic brakes and cooperative braking? The validation focuses on non-functional safety limited by temporal constraints (translated to braking distance). It can be exploited in an early validation approach to help reduce costs of more expensive real world experimentation. It can also determine the boundaries at which sufficient safety can be guaranteed. © 2017 IEEE.
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| 48. |
- Saadatmand, Mehrdad, et al.
(författare)
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A Fuzzy Decision Support Approach for Model-Based Tradeoff Analysis of Non-Functional Requirements
- 2015
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Ingår i: 12th International Conference on Information Technology. - Las Vegas, United States. - 9781479988273 ; , s. 112-121, s. 112-121
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Konferensbidrag (refereegranskat)abstract
- One of the main challenges in addressing Non-Functional Requirements (NFRs) in designing systems is to take into account their interdependencies and mutual impacts. For this reason, they cannot be considered in isolation and a careful balance and tradeoff among them should be established. This makes it a difficult task to select design decisions and features that lead to the satisfaction of all different NFRs in the system, which becomes even more difficult when the complexity of a system grows. In this paper, we introduce an approach based on fuzzy logic and decision support systems that helps to identify different design alternatives that lead to higher overall satisfaction of NFRs in the system. This is achieved by constructing a model of the NFRs and then performing analysis on the model. To build the model, we use a modified version of the NFR UML profile which we have introduced in our previous works, and using model transformation techniques we automate the analysis of the model.
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| 49. |
- Saadatmand, Mehrdad, et al.
(författare)
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A methodology for designing energy-aware secure embedded systems
- 2011
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Ingår i: SIES 2011 - 6th IEEE International Symposium on Industrial Embedded Systems, Conference Proceedings. - 9781612848204 ; , s. 87-90
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Konferensbidrag (refereegranskat)abstract
- Bringing security aspects in earlier phases of development is one of the major shifts in software development trend. Model-driven development which helps with raising the abstraction level and facilitating earlier analysis and verification is a promising approach in this regard and there have been several efforts on modeling security aspects. However, the issue is that when it comes to embedded systems, non-functional requirements such as security are so interconnected that in order to satisfy one, trade-off analysis with other ones are necessary. Energy consumption is one of these requirements which is of great importance in embedded systems domain due to resource limitations that these systems have. In this paper, focusing on security and energy consumptions we propose a new methodology for model-driven design of embedded systems to bring energy measurements and estimations earlier in development phases and thus identify security design decisions that cause violations of specified energy requirements.
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| 50. |
- Saadatmand, Mehrdad, et al.
(författare)
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Design of Adaptive Security Mechanisms for Real-Time Embedded Systems
- 2012
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Ingår i: Lecture Notes in Computer Science, vol. 7159. - Eindhoven, The Netherlands : Springer. - 9783642281655 ; , s. 121-134
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Bokkapitel (refereegranskat)abstract
- Introducing security features in a system is not free and brings along its costs and impacts. Considering this fact is essential in the design of real-time embedded systems which have limited resources. To ensure correct design of these systems, it is important to also take into account impacts of security features on other non-functional requirements, such as performance and energy consumption. Therefore, it is necessary to perform trade-off analysis among non-functional requirements to establish balance among them. In this paper, we target the timing requirements of real-time embedded systems, and introduce an approach for choosing appropriate encryption algorithms at runtime, to achieve satisfaction of timing requirements in an adaptive way, by monitoring and keeping a log of their behaviors. The approach enables the system to adopt a less or more time consuming (but presumably stronger) encryption algorithm, based on the feedback on previous executions of encryption processes. This is particularly important for systems with high degree of complexity which are hard to analyze statistically.
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