Fit2Crash: Specialising Fitness Functions for Crash Reproduction

Master thesis (2020)

Authors

S. Xiang Electrical Engineering, Mathematics and Computer Science

Contributors

A.E. Zaidman Software Engineering - EEMCS (supervisor 1)
A. Panichella Software Engineering - EEMCS (supervisor 2)
J.G.H. Cockx Programming Languages - EEMCS (supervisor 2)
Xavier Devroey Software Engineering - EEMCS (supervisor 1)
P. Derakhshanfar Software Engineering - EEMCS (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 Shang Xiang
Search-Based Software Testing Genetic Algorithms Search-Based Crash Reproduction Fitness Function
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Published Date

19-03-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Software applications inevitably crash, and it is time-consuming to recreate the crash conditions for debugging. Recently, researchers have developed frameworks relying on genetic algorithms, e.g. Botsing, for automated crash reproduction. However, the existing approaches process exceptions of different types as if they were the same. In this thesis, we study how the four most common types of Java exceptions are thrown and define specialised fitness functions for them. We have extended Botsing and carried out an evaluation against 52 real-world crashes from seven various open-source software applications. Our results show that our proposed fitness functions influence both the effectiveness and efficiency, negatively or positively depending on the type of the target exception. This thesis demonstrates how tailoring the fitness functions according to the exception type can improve search-based crash reproduction.