Simple Fault Localization using Execution Traces
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.
Tue 29 AprDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | APR Session 4APR at 210 Chair(s): Tegawendé F. Bissyandé University of Luxembourg, Chao Peng ByteDance | ||
16:00 20mTalk | Simple Fault Localization using Execution Traces APR | ||
16:20 20mTalk | Studying and Understanding the Effectiveness and Failures of Conversational LLM-Based Repair APR Aolin Chen Wuhan University, Haojun Wu Wuhan University, Qi Xin Wuhan University, Steven P. Reiss Brown University, Jifeng Xuan Wuhan University | ||
16:40 20mTalk | Towards Unveiling Vulnerability Remediation Tactics from OSS Community APR Lyuye Zhang Nanyang Technological University, Wu Jiahui , Chengwei Liu Nanyang Technological University, Kaixuan Li East China Normal University, Sen Chen Nankai University, Yang Liu Nanyang Technological University | ||
17:00 20mTalk | Which Inputs Trigger my Patch? APR Martin Eberlein Humboldt-Universtität zu Berlin, Moeketsi Raselimo Humboldt-Universität zu Berlin, Germany and Stellenbosch University, South Africa, Lars Grunske Humboldt-Universität zu Berlin |