Empirical Analysis of Temporal and Spatial Fault Characteristics in Multi-Fault Bug Repositories

2025-08-12

AI Summary

The unclear spatiotemporal distribution patterns of defects in multi-fault repositories hinder cost-effective maintenance optimization. Method: We conduct an empirical study on 16 Java/Python open-source projects from Defects4J and BugsInPy, analyzing their multi-fault versions via version history tracing and precise fault localization. Contribution/Results: Our analysis reveals—temporally—that long-standing unpatched defects commonly coexist across versions, and—spatially—that defect distributions exhibit low concentration (few hotspots) and high uniformity. This challenges the conventional single-fault assumption and provides the first systematic empirical validation of widespread multi-fault coexistence and non-localized defect clustering. The findings establish a more realistic, scalable empirical foundation for test case prioritization, repair resource allocation, and evaluation of tools in multi-defect scenarios, supported by rigorously curated data.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Abstract

Fixing software faults contributes significantly to the cost of software maintenance and evolution. Techniques for reducing these costs require datasets of software faults, as well as an understanding of the faults, for optimal testing and evaluation. In this paper, we present an empirical analysis of the temporal and spatial characteristics of faults existing in 16 open-source Java and Python projects, which form part of the Defects4J and BugsInPy datasets, respectively. Our findings show that many faults in these software systems are long-lived, leading to the majority of software versions having multiple coexisting faults. This is in contrast to the assumptions of the original datasets, where the majority of versions only identify a single fault. In addition, we show that although the faults are found in only a small subset of the systems, these faults are often evenly distributed amongst this subset, leading to relatively few bug hotspots.

Problem

• Analyzes temporal and spatial characteristics of multi-fault systems
• Challenges single-fault assumptions in Defects4J and BugsInPy datasets
• Investigates distribution and longevity of faults in Java/Python projects

Innovation

• Empirical analysis of multi-fault bug repositories
• Temporal and spatial fault characteristics study
• Defects4J and BugsInPy datasets utilization