Learning-aided Program Reasoning

软件构成了我们现代数字世界的基石。然而，开发可靠的软件是出了名的具有挑战性。一个无意的编程错误(例如，心脏出血错误)可能会使数百万的Web服务器易受攻击。为了系统地消除软件漏洞，研究界和工业实验室开发了许多程序推理工具。不幸的是，为了获得可用的准确性和可扩展性，这些程序推理工具必须针对每个代码库进行仔细的定制，这需要大量的专业知识，限制了普通软件开发人员的采用。培训(或简单地招募)高技能程序员，换句话说，“人类学习”，是缓解这些问题的传统和行业方法，然而，这是缓慢、昂贵和不可扩展的。拟议的计划旨在开发一种经济、可扩展和易于访问的方法，可以同时帮助数十万现实世界的程序员。关键的洞察力是让编程环境本身主动地从过去的执行、错误、补丁、历史版本和其他类似的软件库中学习。该项目的主要目标是调查机器学习，或者更广泛地说，人工智能(AI)如何在软件开发的不同阶段帮助改进编程推理。我们将设计和构建一个智能编程助手，它从大型软件库中学习，捕捉各种错误，并实时建议成语和补丁，并用逐渐适应给定代码库的可学习组件取代手动设计的启发式规则或用于分析和测试的规则。具体而言，该方案将专注于三个目标：1)通过挖掘习语和规范来提高句法级别的推理；2)通过学习规则和放松带数值权重的规则来改进静态推理；3)通过学习有效的神经策略来指导动态符号执行来改进动态推理。拟议的研究将极大地推进最先进的编程推理技术，由此产生的工具链将免费提供，普通软件开发人员将很容易获得。该项目将培养10名高素质人才(HQP)，包括2名博士、3名硕士和5名本科生，涉及编程语言、软件工程和机器学习等跨学科领域。HQP将在以下方面获得实践经验：建立“大代码”处理管道，设计程序分析和综合算法以及机器学习模型，开发实用的软件分析和测试工具链，以及进行用户研究和大规模评估。这些技能为HQP在跨学科研究领域和软件行业取得巨大成功奠定了坚实的基础。

Software forms the cornerstone of our modern digital world. However, developing reliable software is notoriously challenging. An inadvertent programming mistake (e.g. Heartbleed bug) could make millions of web servers vulnerable. To systematically eliminate software vulnerabilities, many program reasoning tools have been developed by research communities and industrial labs. Unfortunately, in order to achieve a usable accuracy and scalability, these program reasoning tools have to be carefully customized for each codebase, which requires non-trivial expertise, limiting their adoption by average software developers. Training (or simply recruiting) highly-skilled programmers, in other words, "human learning", is the conventional and industrial way to mitigate these issues, which is however slow, expensive, and non-scalable. The proposed program aims to develop an economical, scalable and easily accessible approach, which can assist hundreds of thousands of real-world programmers at once. The key insight is to make the programming environment itself actively learn from past executions, mistakes, patches, historical versions, and other similar software repositories. The main goal of this program is to investigate how machine learning, or more generally artificial intelligence (AI), can help to improve programming reasoning in various stages of software development. We will design and build an intelligent programming assistant, which learns from large software repositories, catches various mistakes and suggests idioms and patches on the fly, and which replaces manually designed heuristics or rules for analysis and testing with learnable components that gradually adapt to given codebase overtime. Particularly, the proposed program will focus on three objectives: 1) improving syntactic-level reasoning by mining idioms and specifications, 2) improving static reasoning by learning rules and relaxing rules with numerical weights; 3) improving dynamic reasoning by learning an effective neural-policy guiding dynamic symbolic execution. The proposed research will significantly advance state-of-the-art programming reasoning techniques and the resulting toolchain will be freely available and easily accessible to average software developers. This program will train 10 Highly Qualified Personnel (HQP), including 2 PhDs, 3 MSc and 5 undergraduate students, in the interdisciplinary areas of programming languages, software engineering, and machine learning. HQP will gain hands-on experience of building a "big code" processing pipeline, designing program analysis and synthesis algorithms and machine learning models, developing practical software analysis and testing toolchain, and conducting user studies and large-scale evaluations. These skills form a solid background for HQP to achieve great successes in the interdisciplinary area of research as well as software industry.