VarSem: declarative expression and automated inference of variable usage semantics

Abstract

Programmers declare variables to serve specific implementation purposes that we refer to as variable usage semantics (VUS). Understanding VUS is required for various software engineering tasks, including program comprehension, code audits, and vulnerability detection. To help programmers understand VUS, we present a new program analysis that infers a variable's usage semantics from its textual and context information (e.g., symbolic name, type, scope, information flow). To support this analysis, we introduce VarSem, a domain-specific language, in which a variable's semantic category is expressed as a set of declarative rules. VarSem's execution determines which program variables belong to a given semantic category. VarSem translates high-level declarative rules into low-level program analysis techniques, including natural language processing and data flow, and provides a highly extensible architecture for specifying new rules and analysis techniques. We evaluate VarSem with eight real-world systems to identify their personally identifiable information variables. The evaluation results show that VarSem infers variable semantics with satisfying accuracy/precision and passable recall, thus potentially benefiting both software and security engineers.