History-Based Automated Program Repair

软件的可靠性和安全性是信息技术领域的重大而基础的研究挑战。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。软件漏洞严重损害软件的可靠性和安全性。它们每年使我们的经济损失数十亿美元。许多bug，即使是那些已知的bug，也会在成熟的软件中保留很长一段时间，因为缺乏开发资源来修复它们。因此，帮助开发人员修复bug的技术需求很高。自动修复bug可以：（1）透过节省程序员在诊断及修复错误方面的时间及精力，减低软件开发成本;及（2）透过及早修复更多错误，提高软件的可靠性及安全性。 拟议的研究计划的长期目标是开发新的程序修复技术，自动生成修复现实世界的错误，以降低软件开发成本，提高软件的可靠性和安全性。在接下来的五年里，我们的目标是自动生成一种主要类型的修复程序--重复修复程序。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）从过去的修复中自动学习修复模式，以生成部分重复出现的修复，以及（3）通过组合更细粒度的修复来自动生成完整的重复出现的修复，以形成更大的完整修复。 用于重复修复生成的现有方法仅适用于具有相同上下文的重复修复。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自动学习和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，从而能够自动生成当前无法通过现有技术实现的修复。 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。将语义信息融入到程序自动修复中，将为程序自动修复开辟一个新的研究方向。该研究计划将降低软件开发成本，提高加拿大1,300多家软件公司的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。它将培训高素质人员（HQP）在软件工程和软件可靠性领域工作。HQP将获得跨学科知识的独特融合，并应用这些知识来提高软件的可靠性和安全性。

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.