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  1. Proceedings of the 9th International Workshop on Search-Based Software Testing (SBST '16)
  2. Evolutionary testing for crash reproduction
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Evolutionary testing for crash reproduction
Unit testing tool competition: round four
Extending search-based software testing techniques to big data applications
On the diffusion of test smells in automatically generated test code: an empirical study
Budget-aware random testing with T3: benchmarking at the SBST2016 testing tool contest
Automated search for good coverage criteria: moving from code coverage to fault coverage through search-based software engineering
A preliminary empirical assessment of similarity for combinatorial interaction testing of software product lines
EvoSuite at the SBST 2016 tool competition
Strong mutation-based test data generation using hill climbing
JTExpert at the fourth unit testing tool competition
Hitchhikers need free vehicles!: shared repositories for statistical analysis in SBST

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Evolutionary testing for crash reproduction

Content Provider ACM Digital Library
Author Panichella, Annibale van Deursen, Arie Soltani, Mozhan
Abstract Manual crash reproduction is a labor-intensive and time-consuming task. Therefore, several solutions have been proposed in literature for automatic crash reproduction, including generating unit tests via symbolic execution and mutation analysis. However, various limitations adversely affect the capabilities of the existing solutions in covering a wider range of crashes because generating helpful tests that trigger specific execution paths is particularly challenging. In this paper, we propose a new solution for automatic crash reproduction based on evolutionary unit test generation techniques. The proposed solution exploits crash data from collected stack traces to guide search-based algorithms toward the generation of unit test cases that can reproduce the original crashes. Results from our preliminary study on real crashes from Apache Commons libraries show that our solution can successfully reproduce crashes which are not reproducible by two other state-of-art techniques.
Starting Page 1
Ending Page 4
Page Count 4
File Format PDF
ISBN 9781450341660
DOI 10.1145/2897010.2897015
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-05-14
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Crash reproduction Genetic algorithm Search-based software testing Test case generation
Content Type Text
Resource Type Article
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