SHF: Medium: Collaborative Research: Fixing Real Bugs in Real Programs Using Evolutionary Algorithms

SHF：媒介：协作研究：使用进化算法修复实际程序中的实际错误

• 批准号: 0905236
• 负责人: Stephanie Forrest
• 金额: $ 30万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905236&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Fixing

Fixing software bugs is a difficult and time-consuming process, accounting for up to 90% of the lifetime cost of a typical program. Because the number of defects outstrips the resources available for repairing them, most software is shipped with both known and unknown bugs. All previous approaches to debugging multiple types of defects have been manual. This research will develop a fully automated method for repairing bugs in existing software, thus reducing some of the cost of software maintenance.The research will be broadly applicable, targeting off-the-shelf, legacy applications created without program annotations or special coding practices. The technical focus of the work is an automated repair approach using evolutionary algorithms. Program variants are evolved, using analogues of biological processes such as mutation, until one is found that both retains required functionality and avoids the defect. Standard software test cases are used to represent the bug and to encode program requirements. Generated repairs can be presented to developers or applied to the program directly. Significant potential outcomes include: an automated program repair methodology and freely available tools; advances in program analyses; advances in evolutionary algorithms; significant efforts in outreach and education; and dissemination of the results.

修复软件错误是一个困难和耗时的过程，占典型程序生命周期成本的90%。因为缺陷的数量超过了可用于修复它们的资源，所以大多数软件都附带了已知和未知的错误。以前所有调试多种类型缺陷的方法都是手动的。这项研究将开发一种完全自动化的方法来修复现有软件中的错误，从而降低一些软件维护成本。这项研究将广泛适用，目标是没有程序注释或特殊编码实践的现成、遗留应用程序。这项工作的技术重点是使用进化算法的自动修复方法。使用生物过程的类似物，如突变，进化程序变体，直到发现既保留了必需的功能又避免了缺陷的程序变体。标准软件测试用例用于表示错误并对程序需求进行编码。生成的修复可以呈现给开发人员，也可以直接应用于程序。重要的潜在成果包括：自动程序修复方法和免费可用的工具；程序分析方面的进展；进化算法方面的进展；外联和教育方面的重大努力；以及结果的传播。