基于反模式和缺陷修复模式的软件缺陷结构影响因素分析

项目名称： 基于反模式和缺陷修复模式的软件缺陷结构影响因素分析

项目编号： No.61202032

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 何璐璐

作者单位： 武汉大学

项目金额： 23万元

中文摘要： 软件缺陷预测是节省测试成本、保障软件可靠性的重要手段之一。现有研究致力于寻找最优的缺陷预测指标和建模方法以提高缺陷预测效果，但存在孤立地使用度量指标、缺乏理论模型支持、对缺陷类型不加区分等问题，不能很好地满足软件过程改进的需要。本项目围绕"什么是影响软件缺陷的关键结构因素"这一关键问题展开研究，以反模式为经验型知识的外化载体，以缺陷修复模式为实证线索，将经验推导和实证溯因相结合，分析导致软件缺陷的关键结构因素，建立软件结构与软件缺陷的因果关系模型，从模型基础和预测精度等方面改进现有的缺陷预测方法，为提高软件质量、改进设计方法及软件过程提供理论指导和工具支持。

中文关键词： 反模式；软件缺陷预测；缺陷修复模式；缺陷原因分析；结构影响因

英文摘要： Software defect prediction is important for software quality assurance and reducing test efforts. Current work focuses on finding the appropriate predictors and modeling techniques to improve the performance of the prediction models. However, many of these models fit data without understanding the underlying principles, which makes it difficult to apply them to other data sets. In addition, individual measures used in isolation as predictors do not provide relevant clues regarding the cause of a defect. Furthermore, these models treat various types of defects no differently, impeding the accuracy of the prediction results. To address these problems, this project investigates how the structural factors affect and cause software defects by analyzing the antipatterns and bug-fix patterns as well as the relationship between them. Antipatterns embody the expertise's knowledge of the "poor" design and implementation solutions that could cause defects, while bug-fix patterns provide the empirical evidence of what the real problems are in the remedy's perspective. This project combines the two and analyzes the relationship between them at the conceptual level as well as the instance level with the aid of data mining techniques. The goal is to identify the key structural factors causing software defects and to improve th

英文关键词： antipattern；bug-fix pattern；defect prediction；defect causal analysis；structural factor