Liveness-Driven Random Program Generation
Not on the map. Why?
Abstract (arXiv)
Randomly generated programs are popular for testing compilers and program analysis tools, with hundreds of bugs in real-world C compilers found by random testing. However, existing random program generators may generate large amounts of dead code (computations whose result is never used). This leaves relatively little code to exercise a target compiler's more complex optimizations. To address this shortcoming, we introduce liveness-driven random program generation. In this approach the random program is constructed bottom-up, guided by a simultaneous structural data-flow analysis to ensure that the generator never generates dead code. The algorithm is implemented as a plugin for the Frama-C framework. We evaluate it in comparison to Csmith, the standard random C program generator. Our tool generates programs that compile to more machine code with a more complex instruction mix.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2022High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancingcited by 25
- 2021Neurogenetic Programming Framework for Explainable Reinforcement Learningcited by 2
- 2025wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimationcited by 2
- 2021Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translationcited by 1
- 2025wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation6 Nov 2025
- 2022High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing18 Jan 2022
- 2021Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation13 Sep 2021
- 2021Neurogenetic Programming Framework for Explainable Reinforcement Learning8 Feb 2021