Learning fault localisation for both humans and machines using multi-objective GP

Abstract

Genetic Programming has been successfully applied to fault localisation to learn ranking models that place the faulty program element as near the top as possible. However, it is also known that, when localisation results are used by Automatic Program Repair (APR) techniques, higher rankings of faulty program elements do not necessarily result in better repair effectiveness. Since APR techniques tend to use localisation scores as weights for program mutation, lower scores for non-faulty program elements are as important as high scores for faulty program elements. We formulate a multi-objective version of GP based fault localisation to learn ranking models that not only aim to place the faulty program element higher in the ranking, but also aim to assign as low scores as possible to non-faulty program elements. The results show minor improvements in the suspiciousness score distribution. However, surprisingly, the multi-objective formulation also results in more accurate fault localisation ranking-wise, placing 155 out of 386 faulty methods at the top, compared to 135 placed at the top by the single objective formulation.