Research Of Software Multiple Fault Localization Based On Evolution Algorithm

Spectrum-Based fault localization techniques are attractive for their effectiveness,and previous works have demonstrated that they can assist programmers to locate faults automatically.However,most of them can only work better when there is single bug than multiple bugs.Other approaches,although partially successful on multiple faults problem,are complex and need more human intervention.To better address these problems,this paper proposes a new spectrum-based fault localization technique based on genetic algorithm,called GAMFal,which can locate multiple bugs effectively with less human intervention.First,the multiple bugs' localization is converted into a search based model and a candidate expression for multiple bugs' location is encoded as an individual binary string.Then,the new approach extends the Ochiai coefficient to calculate the suspiciousness value used by genetic algorithm as a fitness function to search for a best population composed by optimal fault location candidates with highest suspiciousness value,and converts the ranking list of candidates to a checking order of program entities.According to this order,programmers finally examine program entities to locate faults.An empirical study on Siemens suites and three Linux programs(gzip,grep and sed)is conducted to compare GAMFal with other spectrum-based approaches.The Friedman test and Least Significance Difference method are then carried out to investigate the statistical significance of any differences observed in the experiments.The result suggests that the proposed method outperforms other related techniques in some respects and is feasible with respect to running time.Then,a flexible framework called FSMFL is proposed for localizing multiple faults based upon the genetic algorithms.Many optimizations is made on this framework in preprocess phase and relization phase.And this framework can easily extend fitness functions for new researchers.The experiment results show that FSMFL is more efficient than GAMFal about ten times.