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1.	Abbas, Muhammad, et al. (författare) Automated Reuse Recommendation of Product Line Assets Based on Natural Language Requirements 2020 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 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.
2.	Abbas, Muhammad, et al. (författare) Is Requirements Similarity a Good Proxy for Software Similarity? : An Empirical Investigation in Industry 2021 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 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.
3.	Abbas, Muhammad, et al. (författare) Keywords-based test categorization for Extra-Functional Properties 2020 Ingår i: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). - : IEEE. - 9781728110752 ; , s. 153-156 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.
4.	Abbas, Muhammad, et al. (författare) 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 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.
5.	Abbas, Muhammad, et al. (författare) On the relationship between similar requirements and similar software : A case study in the railway domain 2023 Ingår i: Requirements Engineering. - : Springer Science and Business Media Deutschland GmbH. - 0947-3602 .- 1432-010X. ; 28, s. 23-47 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.
6.	Abbas, Muhammad, et al. (författare) Product line adoption in industry : an experience report from the railway domain 2020 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 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.
7.	Abbas, Muhammad, et al. (författare) Requirements-Driven Reuse Recommendation 2021 Ingår i: Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A. - New York, NY, USA : Association for Computing Machinery. Konferensbidrag (refereegranskat)abstract This tutorial explores requirements-based reuse recommendation for product line assets in the context of clone-and-own product lines.
8.	Abbas, Muhammad (författare) Requirements-Level Reuse Recommendation and Prioritization of Product Line Assets 2021 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.
9.	Bashir, Sarmad, et al. (författare) Requirement or Not, That is the Question : A Case from the Railway Industry 2023 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 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)
10.	Bashir, Sarmad, et al. (författare) 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.
11.	Bradbury, Jeremy, et al. (författare) 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?
12.	Bucaioni, Alessio, 1987-, et al. (författare) 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 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.
13.	Bucaioni, Alessio, 1987-, et al. (författare) 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.
14.	Ferrari, Fabiano C., et al. (författare) 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.
15.	Helali Moghadam, Mahshid, et al. (författare) 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 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.
16.	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).
17.	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.
18.	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.
19.	Helali Moghadam, Mahshid, et al. (författare) 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.
20.	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.
21.	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.
22.	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.
23.	Moravvej, S. V., et al. (författare) 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.
24.	Mousavirad, Seyed, et al. (författare) 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.
25.	Mousavirad, S. J., et al. (författare) 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.
26.	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.
27.	Saadatmand, Mehrdad, 1980-, et al. (författare) Inadequate Risk Analysis Might Jeopardize The Functional Safety of Modern Systems 2016 Rapport (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.
28.	Saadatmand, Mehrdad, 1980-, et al. (författare) Message from the ITEQS 2022 Workshop Chairs 2022 Ingår i: 14th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2022. - : Institute of Electrical and Electronics Engineers Inc.. Tidskriftsartikel (refereegranskat)
29.	Saadatmand, Mehrdad, 1980- (författare) Preservation of Extra-Functional Properties in Embedded Systems Development 2015 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract The interaction of embedded systems with their environments and their resource limitations make it important to take into account properties such as timing, security, and resource consumption in designing such systems. These so-called Extra-Functional Properties (EFPs) capture and describe the quality and characteristics of a system, and they need to be taken into account from early phases of development and throughout the system's lifecycle. An important challenge in this context is to ensure that the EFPs that are defined at early design phases are actually preserved throughout detailed design phases as well as during the execution of the system on its platform. In this thesis, we provide solutions to help with the preservation of EFPs; targeting both system design phases and system execution on the platform. Starting from requirements, which form the constraints of EFPs, we propose an approach for modeling Non-Functional Requirements (NFRs) and evaluating different design alternatives with respect to the satisfaction of the NFRs. Considering the relationship and trade-off among EFPs, an approach for balancing timing versus security properties is introduced. Our approach enables balancing in two ways: in a static way resulting in a fixed set of components in the design model that are analyzed and thus verified to be balanced with respect to the timing and security properties, and also in a dynamic way during the execution of the system through runtime adaptation. Considering the role of the platform in preservation of EFPs and mitigating possible violations of them, an approach is suggested to enrich the platform with necessary mechanisms to enable monitoring and enforcement of timing properties. In the thesis, we also identify and demonstrate the issues related to accuracy in monitoring EFPs, how accuracy can affect the decisions that are made based on the collected information, and propose a technique to tackle this problem. As another contribution, we also show how runtime monitoring information collected about EFPs can be used to fine-tune design models until a desired set of EFPs are achieved. We have also developed a testing framework which enables automatic generation of test cases in order verify the actual behavior of a system against its desired behavior. On a high level, the contributions of the thesis are thus twofold: proposing methods and techniques to 1) improve maintenance of EFPs within their correct range of values during system design, 2) identify and mitigate possible violations of EFPs at runtime.
30.	Saadatmand, Mehrdad, PhD, 1980-, et al. (författare) SmartDelta : Automated Quality Assurance and Optimization in Incremental Industrial Software Systems Development 2022 Ingår i: Proceedings - 2022 25th Euromicro Conference on Digital System Design, DSD 2022. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665474047 ; , s. 754-760 Konferensbidrag (refereegranskat)abstract A common phenomenon in software development is that as a system is being built and incremented with new features, certain quality aspects of the system begin to deteriorate. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a system. To address this topic, the multinational SmartDelta project develops automated solutions for quality assessment of product deltas in a continuous engineering environment. The project will provide smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for next builds.
31.	Saadatmand, Mehrdad, PhD, 1980-, et al. (författare) SmartDelta project : Automated quality assurance and optimization across product versions and variants 2023 Ingår i: Microprocessors and microsystems. - : Elsevier. - 0141-9331 .- 1872-9436. ; 103 Tidskriftsartikel (refereegranskat)abstract Software systems are often built in increments with additional features or enhancements on top of existing products. This incremental development may result in the deterioration of certain quality aspects. In other words, the software can be considered an evolving entity emanating different quality characteristics as it gets updated over time with new features or deployed in different operational environments. Approaching software development with this mindset and awareness regarding quality evolution over time can be a key factor for the long-term success of a company in today's highly competitive market of industrial software-intensive products. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a software system. To address this challenge, the multinational SmartDelta project develops automated solutions for the quality assessment of product deltas in a continuous engineering environment. The project provides smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for the next builds. This paper presents the challenges in incremental software development tackled in the scope of the SmartDelta project, and the solutions that are produced and planned in the project, along with the industrial impact of the project for software-intensive industrial systems.
32.	Schlingloff, H., et al. (författare) Excellence in variant testing 2020 Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : Association for Computing Machinery. - 9781450375016 Konferensbidrag (refereegranskat)abstract In this short paper, we report on the motivation, background and ambition of the ITEA3 project XIVT - excellence in variant testing. We describe a work flow and tool chain for testing of configurable and highly-variant embedded systems in various domains. © 2020 Copyright is held by the owner/author(s).
33.	Sedaghatbaf, Ali, et al. (författare) Automated Performance Testing Based on Active Deep Learning 2021 Ingår i: 2021 IEEE/ACM International Conference on Automation of Software Test (AST). ; , s. 11-19 Konferensbidrag (refereegranskat)abstract Generating tests that can reveal performance issues in large and complex software systems within a reasonable amount of time is a challenging task. On one hand, there are numerous combinations of input data values to explore. On the other hand, we have a limited test budget to execute tests. What makes this task even more difficult is the lack of access to source code and the internal details of these systems. In this paper, we present an automated test generation method called ACTA for black-box performance testing. ACTA is based on active learning, which means that it does not require a large set of historical test data to learn about the performance characteristics of the system under test. Instead, it dynamically chooses the tests to execute using uncertainty sampling. ACTA relies on a conditional variant of generative adversarial networks, and facilitates specifying performance requirements in terms of conditions and generating tests that address those conditions. We have evaluated ACTA on a benchmark web application, and the experimental results indicate that this method is comparable with random testing, and two other machine learning methods, i.e. PerfXRL and DN.
34.	Sirjani, Marjan, et al. (författare) Towards a Verification-Driven Iterative Development of Software for Safety-Critical Cyber-Physical Systems 2021 Ingår i: Journal of Internet Services and Applications. - : Springer Science and Business Media Deutschland GmbH. - 1867-4828 .- 1869-0238. ; 12:1 Tidskriftsartikel (refereegranskat)abstract Software systems are complicated, and the scientific and engineering methodologies for software development are relatively young. Cyber-physical systems are now in every corner of our lives, and we need robust methods for handling the ever-increasing complexity of their software systems. Model-Driven Development is a promising approach to tackle the complexity of systems through the concept of abstraction, enabling analysis at earlier phases of development. In this paper, we propose a model-driven approach with a focus on guaranteeing safety using formal verification. Cyber-physical systems are distributed, concurrent, asynchronous and event-based reactive systems with timing constraints. The actor-based textual modeling language, Rebeca, with model checking support is used for formal verification. Starting from structured requirements and system architecture design the behavioral models, including Rebeca models, are built. Properties of interest are also derived from the structured requirements, and then model checking is used to formally verify the properties. This process can be performed in iterations until satisfaction of desired properties are ensured, and possible ambiguities and inconsistencies in requirements are resolved. The formally verified models can then be used to develop the executable code. The Rebeca models include the details of the signals and messages that are passed at the network level including the timing, and this facilitates the generation of executable code. The natural mappings among the models for requirements, the formal models, and the executable code improve the effectiveness and efficiency of the approach. © 2021, The Author(s).

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