Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Dynamically discovering likely program invariants to support program evolution
Proceedings of the 21st international conference on Software engineering
Quickly detecting relevant program invariants
Proceedings of the 22nd international conference on Software engineering
Global optimization by suppression of partial redundancies
Communications of the ACM
Guarded commands, nondeterminacy and formal derivation of programs
Communications of the ACM
Dynamically Discovering Likely Program Invariants to Support Program Evolution
IEEE Transactions on Software Engineering - Special issue on 1999 international conference on software engineering
The Science of Programming
Computer
Dynamically discovering likely program invariants
Dynamically discovering likely program invariants
MuJava: an automated class mutation system: Research Articles
Software Testing, Verification & Reliability
Evolutionary unit testing of object-oriented software using strongly-typed genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A genetic programming approach to automated software repair
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Efficient mutation testing by checking invariant violations
Proceedings of the eighteenth international symposium on Software testing and analysis
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
An Analysis and Survey of the Development of Mutation Testing
IEEE Transactions on Software Engineering
Evolutionary Improvement of Programs
IEEE Transactions on Evolutionary Computation
JSART: javascript assertion-based regression testing
ICWE'12 Proceedings of the 12th international conference on Web Engineering
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Invariants are concise and useful descriptions of a program's behaviour. As most programs are not annotated with invariants, previous research has attempted to automatically generate them from source code. In this paper, we propose a new approach to invariant generation using search. We reuse the trace generation front-end of existing tool Daikon and integrate it with genetic programming and a mutation testing tool. We demonstrate that our system can find the same invariants through search that Daikon produces via template instantiation, and we also find useful invariants that Daikon does not. We then present a method of ranking invariants such that we can identify those that are most interesting, through a novel application of program mutation.