History-Based Automated Program Repair

软件可靠性和安全性是信息技术领域的重大基础性研究挑战。生产可靠和安全的软件对加拿大的经济和公共安全也至关重要。软件错误会严重损害软件的可靠性和安全性。它们每年给我们的经济造成数十亿美元的损失。由于缺乏修复它们的开发资源，许多错误，甚至那些已知的错误，都会在成熟的软件中保留很长一段时间。因此，帮助开发人员修复错误的技术需求很高。自动修复错误可以：(1)通过节省程序员诊断和修复错误的时间和精力来降低软件开发的成本；(2)通过更早地修复更多的错误来提高软件的可靠性和安全性。*拟议研究计划的长期目标是开发新的程序修复技术，以自动为真实世界的错误生成修复程序，以降低软件开发成本并提高软件可靠性和安全性。在接下来的五年中，我们的目标是自动生成一种主要类型的修复-重复修复。重复修复是指与另一个修复相同或相似的修复。研究表明，高达45%的修复是重复性的。具体目标是(1)深入了解重复出现的错误修复，(2)自动从过去的修复中学习修复模式以生成部分重复修复，以及(3)通过组合更细粒度的修复以形成更大的完整修复来自动生成完整的重复修复。*现有的重复修复程序生成方法仅适用于具有相同上下文的重复修复程序。然而，PI在这一领域的初步工作表明，大多数重复出现的修复程序具有不同的上下文，这表明需要新的方法来学习和自动生成修复程序，以容忍不同的上下文。PI将提出一种新的上下文匹配算法来解决这个问题，使现有技术无法实现的修复程序的自动生成成为可能。*该研究计划将创造高质量的研究能力。它将扩大大量广泛使用的错误预测、检测和诊断技术的影响。这将为将语义信息融入程序自动修复开辟一个新的研究方向。这项研究计划将为加拿大1300多家软件公司降低软件开发成本，提高软件可靠性和安全性，并提高加拿大在这一重要领域的竞争力。它将培养在软件工程和软件可靠性领域工作的高素质人才(HQP)。HQP将获得跨学科知识的独特组合，并将这些知识应用于提高软件可靠性和安全性。**

Software reliability and security are grand and fundamental research challenges in information technology. Producing reliable and secure software is also critically important to Canada's economy and public safety. Software bugs significantly impair software reliability and security. They cost our economy billions annually. Many bugs, even those that are known, remain in mature software for a long time due to the lack of the development resources to fix them. Therefore, techniques to help developers fix bugs are in high demand. Automatically fixing bugs could: (1) reduce the cost of software development through saving programmers' time and effort in diagnosing and fixing bugs, and (2) improve software reliability and security through fixing more bugs earlier. ******The long-term goal of the proposed research program is to develop novel program repair techniques that generate fixes for real-world bugs automatically to reduce the cost of software development and improve software reliability and security. In the next five years, the goal is to generate one major type of fixes---recurring fixes---automatically. Recurring fixes are fixes that are identical or similar to another fix. Studies have shown that up to 45% of fixes are the recurring type. The specific objectives are (1) to obtain a deep understanding of recurring bug fixes, (2) to learn fix patterns from past fixes automatically to generate partial recurring fixes, and (3) to generate complete recurring fixes automatically by combining finer-grained fixes to form larger complete fixes. ******Existing approaches for recurring fix generation work for recurring fixes with identical contexts only. However, the PI's preliminary work in this area has shown that the majority of recurring fixes have different contexts, which suggests that new approaches are needed to learn and generate fixes automatically to tolerate different contexts. The PI will propose a new context matching algorithm to address this issue, enabling automated generation of fixes that are currently unachievable with existing techniques. ******This research program will create high-quality research capabilities. It will broaden the impact of the large amount of widely-used bug prediction, detection, and diagnosis techniques. It will open a new research direction of incorporating semantic information into automated program repair. This research program will reduce the cost of software development and improve the software reliability and security for the more than 1,300 software companies in Canada and improve Canada's competitiveness in this vital sector. It will train Highly Qualified Personnel (HQP) to work in the field of software engineering and software reliability. The HQP will acquire a unique blend of interdisciplinary knowledge and apply that knowledge to improve software reliability and security. **