We present Trimmer, a state-of-the-art tool for reducing code size. Trimmer reduces code sizes by specializing programs with respect to constant inputs provided by developers. The static data can be provided as command-line options or through configuration files. The constants define the features that must be retained, which in turn determine the features that are unused in a specific deployment (and can therefore be removed). Trimmer includes sophisticated compiler transformations for input specialization, supports precise yet efficient context-sensitive inter-procedural constant propagation, and introduces a custom loop unroller. Trimmer is easy-to-use and extensively parameterized. We discuss how Trimmer can be configured by developers to explicitly trade analysis precision and specialization time. We also provide a high-level description of Trimmer’s static analysis passes. The source code is publicly available at: https://github.com/ashish-gehani/Trimmer. A video demonstration can be found here: https://youtu.be/6pAuJ68INnI.

Software bloat has negative implications for security, reliability, and performance. To counter bloat, we proposeTrimmer, a static analysis-based system for pruning unused functionality.Trimmerremoves code that is unused with respect to user-provided command-line arguments and application-specific configuration files.Trimmeruses concrete memory tracking and a custom inter-procedural constant propagation analysis that facilitates dead code elimination. Our system supports both context-sensitive and context-insensitive constant propagation. We show that context-sensitive constant propagation is important for effective software pruning in most applications. We introducesparse constant propagationthat performs constant propagation only for configuration-hosting variables and show that it performs better (higher code size reductions) compared to constant propagation for all program variables. Overall, our results show thatTrimmerreduces binary sizes for real-world programs with reasonable analysis times. Across 20 evaluated programs, we observe a mean binary size reduction of 22.7 percent and a maximum reduction of 62.7 percent. For 5 programs, we observe performance speedups ranging from 5 to 53 percent. Moreover, we show that winnowing software applications can reduce the program attack surface by removing code that contains exploitable vulnerabilities. We find that debloating usingTrimmerremoves CVEs in 4 applications.