Mutation analysis using mutant schemata
ISSTA '93 Proceedings of the 1993 ACM SIGSOFT international symposium on Software testing and analysis
An experimental determination of sufficient mutant operators
ACM Transactions on Software Engineering and Methodology (TOSEM)
Efficient mutation testing by checking invariant violations
Proceedings of the eighteenth international symposium on Software testing and analysis
Mutation-driven generation of unit tests and oracles
Proceedings of the 19th international symposium on Software testing and analysis
Mutation analysis vs. code coverage in automated assessment of students' testing skills
Proceedings of the ACM international conference companion on Object oriented programming systems languages and applications companion
Testing container classes: random or systematic?
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
Automatic test suite evolution
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Mutation at the multi-class and system levels
Science of Computer Programming
Comparing non-adequate test suites using coverage criteria
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Faster mutation testing inspired by test prioritization and reduction
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Injecting mechanical faults to localize developer faults for evolving software
Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications
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To assess the quality of a test suite, one can use mutation testing - seeding artificial defects (mutations) into the program and checking whether the test suite finds them. Javalanche is an open source framework for mutation testing Java programs with a special focus on automation, efficiency, and effectiveness. In particular, Javalanche assesses the impact of individual mutations to effectively weed out equivalent mutants; it has been demonstrated to work on programs with up to 100,000 lines of code.