Extending Delta Debugging Minimization for Spectrum-Based Fault Localization

TL;DR

This paper presents DDMIN-LOC, a novel technique that combines Delta Debugging Minimization with Spectrum-Based Fault Localization to efficiently identify faulty statements in programs with minimal failing inputs.

Contribution

It introduces DDMIN-LOC, which leverages minimal failure-inducing inputs and SBFL to improve fault localization accuracy for string-input programs.

Findings

Fewer than 20% lines examined to locate faults in most cases

Faulty statements ranked within top 3 positions in test suites

Jaccard algorithm performs best among tested SBFL methods

Abstract

This paper introduces DDMIN-LOC, a technique that combines Delta Debugging Minimization (DDMIN) with Spectrum-Based Fault Localization (SBFL). It can be applied to programs taking string inputs, even when only a single failure-inducing input is available. DDMIN is an algorithm that systematically explores the minimal failure-inducing input that exposes a bug, given an initial failing input. However, it does not provide information about the faulty statements responsible for the failure. DDMIN-LOC addresses this limitation by collecting the passing and failing inputs generated during the DDMIN process and computing suspiciousness scores for program statements and predicates using SBFL algorithms. These scores are then combined to rank statements according to their likelihood of being faulty. DDMIN-LOC requires only one failing input of the buggy program, although it can be applied only to programs that take string inputs. DDMIN-LOC was evaluated on 136 programs selected from the QuixBugs and Codeflaws benchmarks using the SBFL algorithms Tarantula, Ochiai, GenProg, Jaccard and DStar. Experimental results show that DDMIN-LOC performs best with Jaccard: in most subjects, fewer than 20% executable lines need to be examined to locate the faulty statements. Moreover, in most subjects, faulty statements are ranked within the top 3 positions in all the generated test suites derived from different failing inputs.