| 1. |
- 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.
|
|
| 2. |
- 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.
|
|
| 3. |
- 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.
|
|
| 4. |
- 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.
|
|
| 5. |
- 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.
|
|
| 6. |
- 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.
|
|
| 7. |
- 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.
|
|
| 8. |
- 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.
|
|
| 9. |
- 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.
|
|
| 10. |
- 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).
|
|
