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Helali Moghadam, Mahshid
Publikasjoner (5 av 5) Visa alla publikasjoner
Helali Moghadam, M., Saadatmand, M., Borg, M., Bohlin, M. & Lisper, B. (2019). Machine Learning to Guide Performance Testing: An Autonomous Test Framework. In: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19: . Paper presented at ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 22 Apr 2019, Xi’an, China (pp. 164-167).
Åpne denne publikasjonen i ny fane eller vindu >>Machine Learning to Guide Performance Testing: An Autonomous Test Framework
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2019 (engelsk)Inngår i: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 2019, s. 164-167Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

Emneord
performance requirements, performance testing, test case generation, reinforcement learning, autonomous testing
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-43918 (URN)10.1109/ICSTW.2019.00046 (DOI)000477742600022 ()2-s2.0-85068406208 (Scopus ID)
Konferanse
ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 22 Apr 2019, Xi’an, China
Tilgjengelig fra: 2019-06-14 Laget: 2019-06-14 Sist oppdatert: 2019-10-11bibliografisk kontrollert
Helali Moghadam, M. (2019). Machine Learning-Assisted Performance Testing. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering: . Paper presented at ESEC/FSE ACM Student Research Competition ESEC/FSE SRC'19, 28 Aug 2019, Tallinn, Estonia (pp. 1187-1189).
Åpne denne publikasjonen i ny fane eller vindu >>Machine Learning-Assisted Performance Testing
2019 (engelsk)Inngår i: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2019, s. 1187-1189Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Automated testing activities like automated test case generation imply a reduction in human effort and cost, with the potential to impact the test coverage positively. If the optimal policy, i.e., the course of actions adopted, for performing the intended test activity could be learnt by the testing system, i.e., a smart tester agent, then the learnt policy could be reused in analogous situations which leads to even more efficiency in terms of required efforts. Performance testing under stress execution conditions, i.e., stress testing, which involves providing extreme test conditions to find the performance breaking points, remains a challenge, particularly for complex software systems. Some common approaches for generating stress test conditions are based on source code or system model analysis, or use-case based design approaches. However, source code or precise system models might not be easily available for testing. Moreover, drawing a precise performance model is often difficult, particularly for complex systems. In this research, I have used model-free reinforcement learning to build a self-adaptive autonomous stress testing framework which is able to learn the optimal policy for stress test case generation without having a model of the system under test. The conducted experimental analysis shows that the proposed smart framework is able to generate the stress test conditions for different software systems efficiently and adaptively without access to performance models.

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-45044 (URN)10.1145/3338906.3342484 (DOI)000485629300124 ()2-s2.0-85071935558 (Scopus ID)978-1-4503-5572-8 (ISBN)
Konferanse
ESEC/FSE ACM Student Research Competition ESEC/FSE SRC'19, 28 Aug 2019, Tallinn, Estonia
Tilgjengelig fra: 2019-08-23 Laget: 2019-08-23 Sist oppdatert: 2019-10-03bibliografisk kontrollert
Helali Moghadam, M., Saadatmand, M., Bohlin, M., Lisper, B. & Borg, M. (2018). Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning. In: 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: . Paper presented at 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems SEAMS 18, 28 May 2018, Gothenburg, Sweden (pp. 217-223). , 28 May
Åpne denne publikasjonen i ny fane eller vindu >>Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning
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2018 (engelsk)Inngår 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, 2018, Vol. 28 May, s. 217-223Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

Serie
Proceedings - International Conference on Software Engineering, ISSN 0270-5257
Emneord
Adaptive response time control, PLC-based real-time programs, Runtime monitoring, Reinforcement learning
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-38955 (URN)10.1145/3194133.3194153 (DOI)000458799600029 ()2-s2.0-85051555083 (Scopus ID)
Konferanse
13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems SEAMS 18, 28 May 2018, Gothenburg, Sweden
Tilgjengelig fra: 2018-05-15 Laget: 2018-05-15 Sist oppdatert: 2019-03-07bibliografisk kontrollert
Helali Moghadam, M., Saadatmand, M., Borg, M., Bohlin, M. & Lisper, B. (2018). Learning-based Response Time Analysis in Real-Time Embedded Systems: A Simulation-based Approach. In: 1st International Workshop on Software Qualities and their Dependencies, located at the International Conference of Software Engineering (ICSE) 2018 SQUADE'18: . Paper presented at 1st International Workshop on Software Qualities and their Dependencies, located at the International Conference of Software Engineering (ICSE) 2018 SQUADE'18, 27 May 2018, Gothenburg, Sweden (pp. 21-24).
Åpne denne publikasjonen i ny fane eller vindu >>Learning-based Response Time Analysis in Real-Time Embedded Systems: A Simulation-based Approach
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2018 (engelsk)Inngår i: 1st International Workshop on Software Qualities and their Dependencies, located at the International Conference of Software Engineering (ICSE) 2018 SQUADE'18, 2018, s. 21-24Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

Serie
Proceedings - International Conference on Software Engineering, ISSN 0270-5257
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-38956 (URN)10.1145/3194095.3194097 (DOI)000449622800004 ()2-s2.0-85051494988 (Scopus ID)9781450357371 (ISBN)
Konferanse
1st International Workshop on Software Qualities and their Dependencies, located at the International Conference of Software Engineering (ICSE) 2018 SQUADE'18, 27 May 2018, Gothenburg, Sweden
Tilgjengelig fra: 2018-05-15 Laget: 2018-05-15 Sist oppdatert: 2019-01-16bibliografisk kontrollert
Helali Moghadam, M., Saadatmand, M., Borg, M., Bohlin, M. & Lisper, B. (2018). Learning-Based Self-Adaptive Assurance of Timing Properties in a Real-Time Embedded System. In: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'18: . Paper presented at ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'18, 09 Apr 2018, Västerås, Sweden (pp. 77-80).
Åpne denne publikasjonen i ny fane eller vindu >>Learning-Based Self-Adaptive Assurance of Timing Properties in a Real-Time Embedded System
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2018 (engelsk)Inngår i: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'18, 2018, s. 77-80Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

Emneord
Timing properties, self-adaptive performance assurance, real-time embedded systems, reinforcement learning
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-38954 (URN)10.1109/ICSTW.2018.00031 (DOI)000492760300011 ()2-s2.0-85050958526 (Scopus ID)9781538663523 (ISBN)
Konferanse
ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'18, 09 Apr 2018, Västerås, Sweden
Tilgjengelig fra: 2018-05-15 Laget: 2018-05-15 Sist oppdatert: 2019-11-14bibliografisk kontrollert
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