Probability-based semantic interpretation of mutants

TY - GEN

T1 - Probability-based semantic interpretation of mutants

AU - Patrick, Matthew

AU - Alexander, Rob

AU - Oriol, Manuel

AU - Clark, John A.

PY - 2014

Y1 - 2014

N2 - Mutation analysis is a stringent and powerful technique for evaluating the ability of a test suite to find faults. It generates a large number of mutants and applies the test suite to them one at a time. As mutation analysis is computationally expensive, it is usually performed on a subset of mutants. The competent programmer hypothesis suggests that experienced software developers are more likely to make small mistakes. It is prudent therefore to focus on semantically small mutants that represent mistakes developers are likely to make. We previously introduced a technique to assess mutant semantics using static analysis by comparing the numerical range of their symbolic output expressions. This paper extends our previous work by considering the probability the output of a mutant is the same as the original program. We show how probability-based semantic interpretation can be used to select mutants that are semantically more similar than those selected by our previous technique. In addition to numerical outputs, it also provides support for modelling the semantics of Boolean variables, strings and composite objects.

AB - Mutation analysis is a stringent and powerful technique for evaluating the ability of a test suite to find faults. It generates a large number of mutants and applies the test suite to them one at a time. As mutation analysis is computationally expensive, it is usually performed on a subset of mutants. The competent programmer hypothesis suggests that experienced software developers are more likely to make small mistakes. It is prudent therefore to focus on semantically small mutants that represent mistakes developers are likely to make. We previously introduced a technique to assess mutant semantics using static analysis by comparing the numerical range of their symbolic output expressions. This paper extends our previous work by considering the probability the output of a mutant is the same as the original program. We show how probability-based semantic interpretation can be used to select mutants that are semantically more similar than those selected by our previous technique. In addition to numerical outputs, it also provides support for modelling the semantics of Boolean variables, strings and composite objects.

KW - mutant selection

KW - mutation analysis

KW - semantic interpretation

KW - static analysis

KW - symbolic execution

UR - http://www.scopus.com/inward/record.url?scp=84903579277&partnerID=8YFLogxK

U2 - 10.1109/ICSTW.2014.18

DO - 10.1109/ICSTW.2014.18

M3 - Conference contribution

AN - SCOPUS:84903579277

SN - 9780769551944

SP - 186

EP - 195

BT - Proceedings - IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops

PB - IEEE Computer Society

T2 - 7th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2014

Y2 - 31 March 2014 through 4 April 2014

ER -