Data Mining-Based Techniques for Software Fault Localization

TL;DR

This paper explores how data mining techniques like formal concept analysis and association rules can be applied to locate faults in software, including complex scenarios like multiple faults and GUI components, enhancing debugging methods.

Contribution

It extends data mining-based fault localization to handle multiple faults and GUI event sequences, providing new insights for debugging complex software systems.

Findings

Data mining techniques can effectively identify faults in software.

Extended analysis to multiple fault scenarios improves debugging accuracy.

Application to GUI components addresses unique event-based test cases.

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.