Exploiting Defect Prediction for Automatic Software Repair (Fixie)

利用缺陷预测进行自动软件修复 (Fixie)

• 批准号: EP/S005730/1
• 负责人: Tracy Hall
• 金额: $ 46.79万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS005730%2F1
• 关键词: Exploiting; Defect; Prediction; Automatic; Software

Software is now at the heart of almost everything we do in the world. This software remains largely handmade, and as such, is prone to defects. Testing detects only a sub-set of software defects with the rest laying dormant, sometimes for years. When these defects emerge in software systems the safety and business consequences can be severe. Software failures and their damaging consequences are regularly reported in the press. Finding and fixing defects has been an intransigent problem over many years. The traditional approach to this problem relies on finding defects during testing then developers manually fixing those defects afterwards.In this project we establish a new technique to automatically fix predicted defects in software code before testing. We use machine learning-based defect prediction information to generate automatic fixes using Genetic Improvement. Our approach aims to offer developers effective fixes to code which is predicted as defective. A higher proportion of the fixes our approach offers to developers should be acceptable, generated quicker and available earlier in the development cycle than previous attempts at automated repair. Importantly, our approach targets a wider pool of defects as it specifically includes targeting those dormant defects which are not identified by testing. Using our approach the developer will always remain in control of the code produced. Fixes are suggested, and the developer is the 'gate-keeper', deciding if a suggested fix is accepted, rejected, or can itself be modified to improve the code. One of the tangible outputs of the project will be a defect fixing tool (Fixie), which will provide support to developers in their daily coding activities. The tool will be developed in collaboration with several industrial partners and will be empirically evaluated throughout the project.

软件现在几乎是我们在世界上所做的一切的核心。这种软件在很大程度上仍然是手工制作的，因此很容易出现缺陷。测试只检测软件缺陷的一个子集，其余的则处于休眠状态，有时长达数年。当这些缺陷出现在软件系统中时，安全和业务后果可能是严重的。软件故障及其破坏性后果经常在新闻中报道。多年来，查找和修复缺陷一直是一个难以解决的问题。传统的解决方法是在测试过程中发现缺陷，然后由开发人员手动修复这些缺陷，本项目提出了一种新的技术，在测试前自动修复软件代码中的预测缺陷。我们使用基于机器学习的缺陷预测信息来生成使用遗传改进的自动修复。我们的方法旨在为开发人员提供有效的修复程序，以预测有缺陷的代码。与之前的自动修复尝试相比，我们的方法向开发人员提供的修复比例更高，应该是可以接受的，生成速度更快，并且在开发周期中更早可用。重要的是，我们的方法针对更广泛的缺陷池，因为它特别包括针对那些未通过测试识别的休眠缺陷。使用我们的方法，开发人员将始终保持对所生成代码的控制。建议修复，开发人员是“守门人”，决定是否接受，拒绝建议的修复，或者可以修改自己以改进代码。该项目的有形产出之一将是一个缺陷修复工具（Fixie），它将为开发人员的日常编码活动提供支持。该工具将与几个工业伙伴合作开发，并将在整个项目中进行经验评估。

项目成果

Fault-insertion and fault-fixing behavioural patterns in Apache Software Foundation Projects

• DOI: 10.1016/j.infsof.2023.107187
• 发表时间: 2023-02
• 期刊: Inf. Softw. Technol.
• 作者: Marco Ortu;Giuseppe Destefanis;T. Hall;David Bowes

How Software Developers Mitigate Their Errors When Developing Code

软件开发人员在开发代码时如何减少错误

• DOI: 10.1109/tse.2020.3040554
• 发表时间: 2022
• 期刊: IEEE Transactions on Software Engineering
• 影响因子: 7.4
• 作者: Nagaria B

An 80-20 Analysis of Buggy and Non-buggy Refactorings in Open-Source Commits

对开源提交中的错误和无错误重构的 80-20 分析

• DOI: 10.1109/seaa56994.2022.00038
• 发表时间: 2022
• 作者: Counsell S

Reducing Software Developer Human Errors by Improving Situation Awareness

通过提高态势感知来减少软件开发人员的人为错误

• DOI: 10.1109/ms.2020.3014223
• 发表时间: 2020
• 期刊: IEEE Software
• 影响因子: 3.3
• 作者: Nagaria B

On The Introduction of Automatic Program Repair in Bloomberg

浅谈Bloomberg自动程序修复功能的介绍

• DOI: 10.1109/ms.2021.3071086
• 发表时间: 2021
• 期刊: IEEE Software
• 影响因子: 3.3
• 作者: Kirbas S