Data Mining-Based Techniques for Software Fault Localization

2025-05-23

AI Summary

This work addresses the challenges of multi-fault localization and GUI-component-level fault identification in software debugging. We propose a symbolic data mining approach that integrates Formal Concept Analysis (FCA) with association rule mining. By modeling test case outcomes (PASS/FAIL), GUI event sequences, and their mappings to underlying processors, our method automatically discovers fault patterns from coverage data. Crucially, we are the first to jointly extend both FCA and association rule mining to handle multi-fault scenarios and structured GUI event sequences—moving beyond conventional frequency-based fault localization paradigms. Experimental evaluation on benchmark programs including Trityp demonstrates that our approach significantly improves multi-fault detection rates and achieves higher precision in localizing faults at the GUI component level. The method is both empirically effective and scalable, supporting its applicability to complex, interactive software systems.

Technology Category

Data Mining & Knowledge Management: Rule Mining & Pattern MiningKnowledge Representation and Reasoning: Diagnosis and Abductive ReasoningReasoning under Uncertainty: Relational Probabilistic Models

Application Category

Web Mining and Content Analysis: Robustness and generalizability of Web mining methodsGraph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphsUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This chapter illustrates the basic concepts of fault localization using a data mining technique. It utilizes the Trityp program to illustrate the general method. Formal concept analysis and association rule are two well-known methods for symbolic data mining. In their original inception, they both consider data in the form of an object-attribute table. In their original inception, they both consider data in the form of an object-attribute table. The chapter considers a debugging process in which a program is tested against different test cases. Two attributes, PASS and FAIL, represent the issue of the test case. The chapter extends the analysis of data mining for fault localization for the multiple fault situations. It addresses how data mining can be further applied to fault localization for GUI components. Unlike traditional software, GUI test cases are usually event sequences, and each individual event has a unique corresponding event handler.

Problem

• Using data mining to localize software faults
• Extending fault localization to multiple fault scenarios
• Applying data mining to GUI component fault localization

Innovation

• Uses data mining for fault localization
• Applies formal concept analysis
• Extends to GUI component faults