Evolving human competitive spectra-based fault localisation techniques

Abstract

Spectra-Based Fault Localisation (SBFL) aims to assist debugging by applying risk evaluation formulæ (sometimes called suspiciousness metrics) to program spectra and ranking statements according to the predicted risk. Designing a risk evaluation formula is often an intuitive process done by human software engineer. This paper presents a Genetic Programming (GP) approach for evolving risk assessment formulæ. The empirical evaluation using 92 faults from four Unix utilities produces promising results. Equations evolved by Genetic Programming can consistently outperform many of the human-designed formulæ, such as Tarantula, Ochiai, Jaccard, Ample, and Wong1/2, up to 6 times. More importantly, they can perform equally as well as Op2, which was recently proved to be optimal against If-Then-Else-2 (ITE2) structure, or even outperform it against other program structures. © 2012 Springer-Verlag.