Exploiting Defect Prediction for Automatic Software Repair (Fixie)

软件现在是我们在世界上所做的几乎所有事情的核心。这个软件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。当这些缺陷在软件系统中出现时，其安全和业务后果可能是严重的。媒体经常报道软件故障及其破坏性后果。多年来，发现和修复缺陷一直是一个不容妥协的问题。解决这个问题的传统方法依赖于在测试期间发现缺陷，然后开发人员在测试后手动修复这些缺陷。在这个项目中，我们建立了一种在测试前自动修复软件代码中预测缺陷的新技术。我们使用基于机器学习的缺陷预测信息来使用遗传改进生成自动修复。我们的方法旨在为开发人员提供有效的修复被预测为有缺陷的代码。我们的方法提供给开发人员的修复的更高比例应该是可接受的，比以前尝试的自动修复更快地生成，并且在开发周期的早期可用。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被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生成的代码的控制。修复是建议的，开发人员是“看门人”，决定建议的修复是被接受、拒绝，还是可以自己修改以改进代码。项目的有形输出之一将是缺陷修复工具（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。

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.