CAREER: Adaptive Large-Scale Program Analysis

在过去的三十年中开发的自动程序分析已经证明了证明真实世界程序的非平凡属性的能力。 反过来，这种能力可以应用于安全、软件定义网络、网络物理系统等领域的新兴软件挑战。 这种应用程序的多样性需要适应客户需求的底层程序分析，在可扩展性，适用性和准确性方面。 然而，今天的程序分析并没有提供有用的调整旋钮。 本研究的目标是一个通用的计算机辅助方法，以有效地适应程序分析，以不同的客户。 它一方面弥合了几十年的程序分析研究与另一方面建立在它们之上的各种工件之间的差距，以解决新兴的软件挑战。 在这样做的过程中，它扩大和增强了程序分析的好处，它的用户，以及用户的软件质量受到程序分析的影响。 首先，它提出了优化问题，暴露了大量的选择，以适应程序分析的各个方面，如其成本，其结果的准确性，以及它对缺失信息的假设。 其次，它通过新的搜索算法解决了这些优化问题，这些算法可以有效地导航大搜索空间，在存在噪声的情况下进行推理，与用户交互，并跨程序学习。 第三，它建立了一个程序分析平台，方便用户指定和组成的分析，使搜索算法的原因分析，并允许使用大规模的计算资源并行分析。 该方法在分析移动的应用程序的背景下得到了演示-这些应用程序在智能手机和平板电脑等高级移动的设备上运行。 移动的应用程序代表了越来越多的非专业程序员的使用，它们可能会在异构和苛刻的条件下被广泛的用户使用，这些用户可以从程序分析提供的假设分析中受益。

Automated program analyses developed over the last three decades have demonstrated the ability to prove non-trivial properties of real-world programs. This ability, in turn, has applications to emerging software challenges in security, software-defined networking, cyber-physical systems, and beyond. The diversity of such applications necessitates adapting the underlying program analyses to client needs, in aspects of scalability, applicability, and accuracy. Today's program analyses, however, do not provide useful tuning knobs. The goal of this research is a general computer-assisted approach to effectively adapt program analyses to diverse clients. It bridges the gap between decades of program analysis research on one hand and diverse artifacts built atop them to address emerging software challenges on the other. In doing so, it broadens and enhances the benefits of program analysis to its users, as well as users of software whose quality is impacted by program analysis.The research has three key ingredients. First, it poses optimization problems that expose a large set of choices to adapt various aspects of a program analysis, such as its cost, the accuracy of its result, and the assumptions it makes about missing information. Second, it solves those optimization problems by new search algorithms that efficiently navigate large search spaces, reason in the presence of noise, interact with users, and learn across programs. Third, it builds a program analysis platform that facilitates users to specify and compose analyses, enables search algorithms to reason about analyses, and allows using large-scale computing resources to parallelize analyses. The approach is demonstrated in the context of analyzing mobile apps -- programs that run on advanced mobile devices such as smartphones and tablets. Mobile apps represent an increasing use of non-expert programmers and they are likely to be used across a wide range of users in heterogeneous and demanding conditions that can benefit from what-if analyses that program analysis can offer.