| 1. |
- 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
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Ingår i: Journal of Software. - : John Wiley and Sons Ltd. - 2047-7473 .- 2047-7481. ; 34:11
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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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| 2. |
- Helali Moghadam, Mahshid, et al.
(författare)
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An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning
- 2022
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Ingår i: Software quality journal. - : Springer. - 0963-9314 .- 1573-1367. ; , s. 127-159
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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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| 3. |
- 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
- 2022
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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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| 4. |
- Mousavirad, S. J., et al.
(författare)
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RWS-L-SHADE : An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation
- 2022
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Ingår i: Lecture Notes in Computer Science, vol. 13324. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031024610 ; , s. 255-268
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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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| 5. |
- Saadatmand, Mehrdad, 1980-, et al.
(författare)
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Message from the ITEQS 2022 Workshop Chairs
- 2022
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Ingår i: 14th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2022. - : Institute of Electrical and Electronics Engineers Inc..
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Tidskriftsartikel (refereegranskat)
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| 6. |
- Saadatmand, Mehrdad, PhD, 1980-, et al.
(författare)
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SmartDelta : Automated Quality Assurance and Optimization in Incremental Industrial Software Systems Development
- 2022
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Ingår i: Proceedings - 2022 25th Euromicro Conference on Digital System Design, DSD 2022. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665474047 ; , s. 754-760
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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.
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