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Information and Software Technology
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B-Refactoring: Automatic test code refactoring to improve dynamic analysis

Authors: Jifeng Xuan; Benoit Cornu; Matias Martinez; Benoit Baudry; Lionel Seinturier; Martin Monperrus;

B-Refactoring: Automatic test code refactoring to improve dynamic analysis

Abstract

Context: Developers design test suites to verify that software meets its expected behaviors. Many dynamic analysis techniques are performed on the exploitation of execution traces from test cases. In practice, one test case may imply various behaviors. However, the execution of a test case only yields one trace, which can hide the others.Objective: In this article, we propose a new technique of test code refactoring, called B-Refactoring. The idea behind B-Refactoring is to split a test case into small test fragments, which cover a simpler part of the control flow to provide better support for dynamic analysis.Method: For a given dynamic analysis technique, B-Refactoring monitors the execution of test cases and constructs small test cases without loss of the testability. We apply B-Refactoring to assist two existing analysis tasks: automatic repair of if-condition bugs and automatic analysis of exception contracts.Results: Experimental results show that B-Refactoring can effectively improve the execution traces of the test suite. Real-world bugs that could not be previously fixed with the original test suites are fixed after applying B-Refactoring; meanwhile, exception contracts are better verified via applying B-Refactoring to original test suites.Conclusions: We conclude that applying B-Refactoring improves the execution traces of test cases for dynamic analysis. This improvement can enhance existing dynamic analysis tasks.

Country
France
Keywords

[INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE]

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    30
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
30
Top 10%
Top 10%
Top 10%
Green
bronze