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: 0905373
• 负责人: Westley Weimer
• 金额: $ 30万
• 依托单位: University of Virginia Main Campus
• 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 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905373&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且耗时的过程，占典型程序生命周期成本的 90%。 由于缺陷的数量超出了可用于修复它们的资源，因此大多数软件都带有已知和未知的错误。 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，从而降低部分软件维护成本。这项研究将具有广泛的适用性，针对无需程序注释或特殊编码实践而创建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（例如突变）来进化程序变体，直到发现一种程序变体既保留了所需的功能又避免了缺陷。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或直接应用于程序。 重要的潜在成果包括：自动化程序修复方法和免费提供的工具；计划分析方面的进展；进化算法的进步；在外展和教育方面作出重大努力；和结果的传播。