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

修复软件错误是一个困难且耗时的过程，占一个典型程序生命周期成本的90%。由于缺陷的数量超过了修复它们的可用资源，大多数软件都带有已知和未知的错误。所有以前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，从而减少软件维护的一些成本。这项研究将广泛适用，目标是现成的、没有程序注释或特殊编码实践的遗留应用程序。这项工作的技术重点是使用进化算法的自动修复方法。程序变体使用类似的生物过程（如突变）进行进化，直到发现其中一个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编码程序需求。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：自动程序修复方法和免费提供的工具；程序分析进展；进化算法的进展；在外联和教育方面作出重大努力；以及结果的传播。

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.