Automatic static analysis of software performance
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Performance is a critical component of software quality. Software performance can have drastic repercussions on an application, frustrating its users, breaking the functionality of its components, or even rendering it defenseless against hackers. Unfortunately, unlike in the program verification domain, robust analysis techniques for software performance are almost non-existent. In this thesis we formalize important classes of performance-related bugs and security vulnerabilities, and implement novel static analysis techniques for automatically detecting them in widely used open-source applications. Our tools are able to uncover 92 performance bugs and 47 security vulnerabilities, while analyzing hundreds of thousands of lines of code and reporting a modest amount of false positives. Our work opens a new avenue for research: the development of rigorous automatic analyses for effective software performance understanding, inspired by traditional research in functional verification.