用数据挖掘定位软件缺陷，支持多缺陷和图形界面测试。
Data Mining-Based Techniques for Software Fault Localization
- 基于形式概念分析与关联规则分析测试结果
- 可处理多个缺陷同时存在的情况
- 适用于图形界面事件序列的故障定位
本章介绍利用数据挖掘技术进行软件缺陷定位的基本概念，以Trityp程序为例说明通用方法。形式概念分析和关联规则是符号数据挖掘中两种经典方法，最初均针对对象-属性表形式的数据。本文考虑程序在不同测试用例下的调试过程，其中两个属性PASS和FAIL表示测试用例是否通过。章节扩展了数据挖掘在多缺陷场景下的应用，并探讨如何将该方法进一步应用于图形用户界面（GUI）组件的缺陷定位。不同于传统软件，GUI测试用例通常为事件序列，每个事件对应唯一的事件处理器。
原文摘要 · Abstract (English)
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.
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