SHF: Small: Find and Fix Similar Software Bugs
SHF: Small: Find and Fix Similar Software Bugs
批准号:
1018600
负责人:
Tien Nguyen
金额:
$48.33万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-08-15 至 2014-07-31
中文摘要
在开发可靠和高质量软件的过程中,发现和修复错误是至关重要的。软件开发人员可以根据他们自己的程序经验,或者通过参考过去相同或不同系统中其他人的类似错误和修复来有效地找到错误。然而,软件工程的知识体系对于这种反复出现的错误的性质、原因和影响以及特征仍然非常有限。从先前已知的错误中学习的过程仍然是特别的、手动的和非系统的。在这个项目中,引入了一种全面的方法来获取先前错误和相应修复的知识,并利用这些知识来构建自动化工具来检测相同或不同系统中其他位置的潜在重复错误代码。这样的工具将有助于在开发过程的早期发现bug,从而导致更高质量的软件,并在bug修复实践中提高软件开发人员的生产力。在这个项目中,将进行一项实证研究,以收集、分析和理解在一个和多个系统中重复出现的和类似的bug的性质和特征。该项目有望在理论基础、概念、实用技术和自动化工具方面推进软件工程知识,以(1)捕获特征并测量先前已知修复错误所涉及的代码单元的相似性,(2)识别潜在错误单元的位置,并通过将它们与已知错误的相关对等代码单元进行匹配,得出修复它们的指导方针,以及(3)支持类似的错误检测和修复过程。本项目的教学模块和验证工作将涉及学生和专业人员,促进教学和培训软件的质量保证。
英文摘要
Finding and fixing bugs are crucial in the process of developing reliable and high-quality software. Software developers could base on their own experience with their programs, or effectively find bugs by consulting the similar bugs and fixes from others in the past for the same or different systems. However, the body of knowledge in software engineering is still very limited on the nature, the causes and effects, and the characteristics of such recurring bugs. The learning process from prior known bugs is still ad-hoc, manually, and un-systematically. In this project, a comprehensive approach is introduced to capture the knowledge of prior bugs and corresponding fixes, and to leverage such knowledge to build automated tools to detect potential recurring buggy code at other locations in the same or different systems. Such tools will help to detect bugs early in the development process, leading to higher-quality software and the improvement in productivity of software developers in the bug fixingpractice.In this project, an empirical study will be conducted to collect, analyze, and understand the nature and characteristics of recurring and similar bugs within one and across multiple systems. This project is expected to advance software engineering knowledge on the theoretical foundation, concepts, practical techniques, and automated tools to (1) capture the characteristics and measure the similarity of code units involved in prior known fixed bugs, (2) identify the locations of potential buggy units and derive the guidelines to fix them by matching them to the relevant peer code units of the known bugs, and (3) support the similar bug detection and fixing process. The teaching modules and validation efforts in this project will involve students and professionals, promoting teaching and training software quality assurance.
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