Reference : Mining Assumptions for Software Components using Machine Learning
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/43473
Mining Assumptions for Software Components using Machine Learning
English
Gaaloul, Khouloud [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > >]
Menghi, Claudio mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Nejati, Shiva [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Wolfe, David [QRA, Corp]
In press
Proceedings of the The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)
Yes
The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)
From 8-11-2020 to 29-11-202
[en] Cyber-Physical Systems ; Assumptions ; Synthesis ; Mining Assumptions
[en] Software verification approaches aim to check a software component under analysis for all possible environments. In reality, however, components are expected to operate within a larger system and are required to satisfy their requirements only when their inputs are constrained by environment assumptions. In this paper, we propose EPIcuRus, an approach to automatically synthesize environment assumptions for a component under analysis (i.e., conditions on the component inputs under which the component is guaranteed to satisfy its requirements). EPIcuRus combines search-based testing, machine learning and model checking. The core of EPIcuRus is a decision tree algorithm that infers environment assumptions from a set of test results including test cases and their verdicts. The test cases are generated using search-based testing, and the assumptions inferred by decision trees are validated through model checking. In order to improve the efficiency and effectiveness of the assumption generation process, we propose a novel test case generation technique, namely Important Features Boundary Test (IFBT), that guides the test generation based on the feedback produced by machine learning. We evaluated EPIcuRus by assessing its effectiveness in computing assumptions on a set of study subjects that include 18 requirements of four industrial models. We show that, for each of the 18 requirements, EPIcuRus was able to compute an assumption to ensure the satisfaction of that requirement, and further, ≈78% of these assumptions were computed in one hour.
http://hdl.handle.net/10993/43473
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
FnR ; FNR12632261 > Mehrdad Sabetzadeh > EQUACS > Early QUality Assurance of Critical Systems > 01/01/2019 > 31/12/2021 > 2018

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