Simple Fault Localization using Execution Traces

Abstract

Traditional spectrum-based fault localization (SBFL) exploits differences in a program's coverage spectrum when run on passing and failing test cases. However, such runs can provide a wealth of additional information beyond mere coverage. Working with thousands of execution traces of short programs submitted to competitive programming contests and leveraging machine learning and additional runtime, control-flow and lexical features, we present simple ways to improve SBFL. We also propose a simple trick to integrate context information. Our approach outperforms SBFL formulae such as Ochiai on our evaluation set as well as QuixBugs and requires neither a GPU nor any form of advanced program analysis. Existing SBFL solutions could possibly be improved with reasonable effort by adopting some of the proposed ideas.