HaPy-Bug -- Human Annotated Python Bug Resolution Dataset

2025-04-07

AI Summary

This work addresses the lack of fine-grained, high-quality annotated data for modeling Python bug-fixing processes. We introduce HaPy-Bug—the first Python bug-fix dataset supporting multi-expert collaboration and line-level human annotation—comprising 793 real-world fix commits. Each modified line is independently labeled by three domain experts, covering file functionality, line-level change type, and reviewer confidence. HaPy-Bug enables, for the first time in Python, systematic identification and quantitative analysis of tangled changes, facilitating repair pattern mining and statistical modeling. Experimental analysis reveals empirical regularities in file functional distribution, prevalent repair patterns, and change coupling characteristics across Python projects. The dataset and methodology provide a reproducible benchmark for defect prediction, automated repair recommendation, and intelligent repository analytics.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Probabilistic Programming
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data integration and cleaning
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

We present HaPy-Bug, a curated dataset of 793 Python source code commits associated with bug fixes, with each line of code annotated by three domain experts. The annotations offer insights into the purpose of modified files, changes at the line level, and reviewers' confidence levels. We analyze HaPy-Bug to examine the distribution of file purposes, types of modifications, and tangled changes. Additionally, we explore its potential applications in bug tracking, the analysis of bug-fixing practices, and the development of repository analysis tools. HaPy-Bug serves as a valuable resource for advancing research in software maintenance and security.

Problem

• Analyzing annotated Python bug fixes for patterns
• Exploring bug-fixing practices and modification types
• Enhancing software maintenance and security research

Innovation

• Curated dataset of Python bug-fix commits
• Line-level annotations by domain experts
• Analyzes file purposes and modification types