Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2017

Abstract

Debugging is difficult. Recent studies show that automatic bug localization techniques have limited usefulness. One of the reasons is that programmers typically have to understand why the program fails before fixing it. In this work, we aim to help programmers understand a bug by automatically generating likely invariants which are violated in the failed tests. Given a program with an initial assertion and at least one test case failing the assertion, we first generate random test cases, identify potential bug locations through bug localization, and then generate program state mutation based on active learning techniques to identify a predicate 'explaining' the cause of the bug. The predicate is a classifier for the passed test cases and failed test cases. Our main contribution is the application of invariant learning for bug explanation, as well as a novel approach to overcome the problem of lack of test cases in practice. We apply our method to real-world bugs and show the generated invariants are often correlated to the actual bug fixes.

Keywords

Active learning, Debugging, Invariant

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

ICECCS 2017: 22nd International Conference on Engineering of Complex Computer Systems: Fukuoka, Japan, November 5-8: Proceedings

First Page

70

Last Page

79

ISBN

9781538624319

Identifier

10.1109/ICECCS.2017.12

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/ICECCS.2017.12

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