Research On Mutation Reduction Strategy For Mutation Based Fault Localization

How to efficiently and accurately locate faults in programs during software debugging process is taken up as a matter of common concern for software developers.MBFL is a fault localization technique based on mutation analysis,which precisely identifies the root cause of fault through contrasting the similarities of actual fault with artificial implant fault,and using the corresponding suspiciousness formulas to calculating the probability that program code entity may fail.MBFL helps the developers or testers to locating the real bug but incurs a high execution cost,since it needs to execute the test suite on a large amount of mutants.For decreasing the execution cost of MBFL,this paper presents a statement-oriented mutant reduction strategy(SOME)which selects a certain proportion set of mutants generated by statements covered by failed tests,according to the previous execution information of test suite.SOME selects the complete mutant operators and applys the sampling strategy,then it can keep the completeness of mutation types and reduce the amount of mutants.Further more,this paper combines SOME with DMES which contains the statement-oriented mutants execution method and test cases execution optimization and presents the basic framework of MBFL combing SOME and DMES which we call it DS for short.For evaluating the validity of SOME in locating faults of MBFL,empirical studies are conducted on 140 faulty versions from 8 program packages which aimed at comparing the mutation execution cost of original MBFL,MBFL with Mutant Sampling Strategy(SAMPLING)and MBFL with SOME applying different suspiciousness formulas.Results indicate that this strategy can reduce 74.13%?80.24%mutation execution cost under the case of keeping high fault location precision compared with MBFL with SAMPLING and also demonstrated that MBFL combined with DS can reduce 78.80%?87.41%mutation execution cost keeping high fault location precision.The results help MBFL being widly used in real software fault localization.