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Strong higher order mutation-based test data generation
| Content Provider | CiteSeerX |
|---|---|
| Author | Harman, Mark Jia, Yue Langdon, William B. |
| Description | This paper introduces SHOM, a mutation-based test data generation approach that combines Dynamic Symbolic Ex-ecution and Search Based Software Testing. SHOM targets strong mutation adequacy and is capable of killing both first and higher order mutants. We report the results of an em-pirical study using 17 programs, including production indus-trial code from ABB and Daimler and open source code as well as previously studied subjects. SHOM achieved higher strong mutation adequacy than two recent mutation-based test data generation approaches, killing between 8 % and 38 % of those mutants left unkilled by the best performing previous approach. |
| File Format | |
| Language | English |
| Publisher | ACM |
| Publisher Institution | In 8th European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE ’11 |
| Access Restriction | Open |
| Subject Keyword | Software Testing Open Source Code Mutation-based Test Data Generation Approach Order Mutation-based Test Data Generation Em-pirical Study Performing Previous Approach Strong Mutation Adequacy Dynamic Symbolic Ex-ecution Production Indus-trial Code Order Mutant |
| Content Type | Text |
| Resource Type | Article |
