Empirical Evaluation of Conditional Operators in GP Based Fault Localization

Abstract

Genetic Programming has been successfully applied to learn to rank program elements according to their likelihood of containing faults. However, all GP-evolved formulae that have been studied in the fault localization literature up to now are single expressions that only use a small set of basic functions. Based on recent theoretical analysis that different formulae may be more effective against different classes of faults, we evaluate the impact of allowing ternary conditional operators in GP-evolved fault localization by extending our fault localization tool called FLUCCS. An empirical study based on 210 real world Java faults suggests that the simple inclusion of ternary conditional operator can help fault localization by placing up to 11% more faults at the top compared to our baseline, FLUCCS, which in itself can already rank 50% more faults at the top compared to the state-of-the-art SBFL formulae.