Back to the Future! Studying Data Cleanness in Defects4J and its Impact on Fault Localization

TL;DR

This study investigates the impact of developer knowledge embedded in tests within the Defects4J dataset on fault localization effectiveness, revealing significant performance degradation when such knowledge is absent.

Contribution

It analyzes the timeline and modifications of fault-triggering tests in Defects4J, highlighting the influence of developer knowledge on SBFL techniques and providing a dataset for unbiased evaluations.

Findings

55% of fault-triggering tests added to replicate bugs

22% of tests modified after bug reports, containing developer knowledge

SBFL performance drops up to 415% without developer insights

Abstract

For software testing research, Defects4J stands out as the primary benchmark dataset, offering a controlled environment to study real bugs from prominent open-source systems. However, prior research indicates that Defects4J might include tests added post-bug report, embedding developer knowledge and affecting fault localization efficacy. In this paper, we examine Defects4J's fault-triggering tests, emphasizing the implications of developer knowledge of SBFL techniques. We study the timelines of changes made to these tests concerning bug report creation. Then, we study the effectiveness of SBFL techniques without developer knowledge in the tests. We found that 1) 55% of the fault-triggering tests were newly added to replicate the bug or to test for regression; 2) 22% of the fault-triggering tests were modified after the bug reports were created, containing developer knowledge of the bug; 3) developers often modify the tests to include new assertions or change the test code to reflect the changes in the source code; and 4) the performance of SBFL techniques degrades significantly (up to --415% for Mean First Rank) when evaluated on the bugs without developer knowledge. We provide a dataset of bugs without developer insights, aiding future SBFL evaluations in Defects4J and informing considerations for future bug benchmarks.