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Testing self-adapti...
Testing self-adaptive software with probabilistic guarantees on performance metrics
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- Mandrioli, Claudio (author)
- Lund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH
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- Maggio, Martina (author)
- Lund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH,Saarland University
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Devanbu, Prem (editor)
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Cohen, Myra (editor)
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Zimmermann, Thomas (editor)
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(creator_code:org_t)
- 2020-11-07
- 2020
- English 13 s.
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In: ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering. - New York, NY, USA : ACM. - 9781450370431 ; , s. 1002-1014
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Abstract
Subject headings
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- This paper discusses the problem of testing the performance of the adaptation layer in a self-adaptive system. The problem is notoriously hard, due to the high degree of uncertainty and variability inherent in an adaptive software application. In particular, providing any type of formal guarantee for this problem is extremely difficult. In this paper we propose the use of a rigorous probabilistic approach to overcome the mentioned difficulties and provide probabilistic guarantees on the software performance. We describe the set up needed for the application of a probabilistic approach. We then discuss the traditional tools from statistics that could be applied to analyse the results, highlighting their limitations and motivating why they are unsuitable for the given problem. We propose the use of a novel tool - the scenario theory - to overcome said limitations. We conclude the paper with a thorough empirical evaluation of the proposed approach, using two adaptive software applications: the Tele-Assistance Service and the Self-Adaptive Video Encoder. With the first, we empirically expose the trade-off between data collection and confidence in the testing campaign. With the second, we demonstrate how to compare different adaptation strategies.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
Keyword
- Autonomous Systems
- Self-Adaptive Software
- Testing
Publication and Content Type
- kon (subject category)
- ref (subject category)
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