Fast Automated Abstract Machine Repair Using Simultaneous Modifications and Refactoring

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Author(s)
Cai, Cheng-Hao
Sun, Jing
Dobbie, Gillian
Hóu, Zhé
Bride, Hadrien
Dong, Jin Song
Lee, Scott Uk-Jin
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2022
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Abstract

Automated model repair techniques enable machines to synthesise patches that ensure models meet given requirements. B-repair, which is an existing model repair approach, assists users in repairing erroneous models in the B formal method, but repairing large models is inefficient due to successive applications of repair. In this work, we improve the performance of B-repair using simultaneous modifications, repair refactoring, and better classifiers. The simultaneous modifications can eliminate multiple invariant violations at a time so the average time to repair each fault can be reduced. Further, the modifications can be refactored to reduce the length of repair. The purpose of using better classifiers is to perform more accurate and general repairs and avoid inefficient brute-force searches. We conducted an empirical study to demonstrate that the improved implementation leads to the entire model process achieving higher accuracy, generality, and efficiency.

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Formal Aspects of Computing

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34

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2

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Mechanical engineering

Software engineering

Theory of computation

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Cai, C-H; Sun, J; Dobbie, G; Hóu, Z; Bride, H; Dong, JS; Lee, SU-J, Fast Automated Abstract Machine Repair Using Simultaneous Modifications and Refactoring, Formal Aspects of Computing, 2022, 34 (2), pp. 8

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