CAREER:Program Analyses for Improving Reliability of Probabilistic Software

许多新兴的应用程序在嘈杂的数据上运行，并在不确定的情况下做出决策。概率编程语言将这样的计算表示为对随机变量和概率分布进行操作的程序。虽然现有的语言甚至为具有有限统计知识的程序员打开了强大的概率推理的世界，但需要开发新的技术来提高程序员的生产力并简化概率软件的调试。该项目调查了静态程序分析的假设，其健全和丰富的符号推理，是这些技术的坚实基础。这个项目将导致新的自动化工具来帮助科学家、工程师和软件开发人员构建可靠和健壮的概率软件。该项目将通过开发基于新思想的课程来整合研究和教育，目标是使未来的软件工程师具备扎实的定量推理技能。该项目将研究概率计算的自动关系分析的基础和概率分析的实际应用，以帮助应用程序编程人员和概率编程系统的开发人员。该项目将研究概率程序的两种有影响力的关系分析：敏感性分析和语义差异。该项目将开发一个技术生态系统，利用这些分析来识别概率编程系统中的错误，通过程序转换提高概率计算的鲁棒性，并优化在噪声数据上运行的应用程序的性能。该方法的优点和关键组件（包括灵活的抽象、转换和解决机制）将扩展到具有固有随机性的各种应用程序领域。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many emerging applications operate on noisy data and make decisions under uncertainty. Probabilistic programming languages represent such computations as programs that operate on random variables and probability distributions. While the existing languages open the world of powerful probabilistic inference even to programmers with limited knowledge of statistics, new techniques need to be developed to improve programmer productivity and simplify debugging of probabilistic software. This project investigates the hypothesis that static program analysis, with its sound and rich symbolic reasoning, is a solid foundation for these techniques. This project will lead to new automated tools to help scientists, engineers, and software developers build reliable and robust probabilistic software. The project will integrate research and education by developing courses based on newly developed ideas, with the goal of empowering future software engineers with solid quantitative reasoning skills.The project will investigate both the foundations of automated relational analysis for probabilistic computations and the practical application of probabilistic analysis to help application programmers and developers of probabilistic programming systems. The project will investigate two impactful relational analyses for probabilistic programs: sensitivity analysis and semantic differencing. The project will develop an ecosystem of techniques that leverage these analyses to identify errors in probabilistic programming systems, improve robustness of probabilistic computations through program transformations, and optimize the performance of applications that operate on noisy data. The benefits and key components of the approach (including flexible abstractions, transformations, and solving mechanisms) will extend to various application domains with inherent randomness.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.