Large-scale configurable static analysis

Abstract

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. This talk presents a general computer-assisted approach to effectively adapt program analyses to diverse clients. The approach has three key ingredients. First, it poses optimization problems that expose a large set of choices to adapt various aspects of an 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 comprises 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.