TY - GEN
T1 - Automatic mutation test input data generation via ant colony
AU - Ayari, Kamel
AU - Bouktif, Salah
AU - Antoniol, Giuliano
PY - 2007
Y1 - 2007
N2 - Fault-based testing is often advocated to overcome limitations ofother testing approaches; however it is also recognized as beingexpensive. On the other hand, evolutionary algorithms have beenproved suitable for reducing the cost of data generation in the contextof coverage based testing. In this paper, we propose a newevolutionary approach based on ant colony optimization for automatictest input data generation in the context of mutation testingto reduce the cost of such a test strategy. In our approach the antcolony optimization algorithm is enhanced by a probability densityestimation technique. We compare our proposal with otherevolutionary algorithms, e.g., Genetic Algorithm. Our preliminaryresults on JAVA testbeds show that our approach performed significantlybetter than other alternatives.
AB - Fault-based testing is often advocated to overcome limitations ofother testing approaches; however it is also recognized as beingexpensive. On the other hand, evolutionary algorithms have beenproved suitable for reducing the cost of data generation in the contextof coverage based testing. In this paper, we propose a newevolutionary approach based on ant colony optimization for automatictest input data generation in the context of mutation testingto reduce the cost of such a test strategy. In our approach the antcolony optimization algorithm is enhanced by a probability densityestimation technique. We compare our proposal with otherevolutionary algorithms, e.g., Genetic Algorithm. Our preliminaryresults on JAVA testbeds show that our approach performed significantlybetter than other alternatives.
KW - Ant colony optimization
KW - Mutation testing
KW - Search based testing
KW - Test input data generation
UR - http://www.scopus.com/inward/record.url?scp=34548096218&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548096218&partnerID=8YFLogxK
U2 - 10.1145/1276958.1277172
DO - 10.1145/1276958.1277172
M3 - Conference contribution
AN - SCOPUS:34548096218
SN - 1595936971
SN - 9781595936974
T3 - Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference
SP - 1074
EP - 1081
BT - Proceedings of GECCO 2007
T2 - 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
Y2 - 7 July 2007 through 11 July 2007
ER -