Validating functional correctness of mobile apps is challenging for manual testing and automated UI testing (limited to simple crashing bugs). Property-based testing is promising to tackle this challenge given functional properties. To this end, we introduce Kea2, a practical property-based testing tool for apps: (1) specifying properties in Python with enough flexibility and expressiveness; and (2) reusing existing GUI fuzzing techniques to support effective property checking. Indeed, Kea2 can find functional (logic) bugs in real-world apps. Kea2 has been open-sourced at https://github.com/ecnusse/Kea2 (a demo video: https://youtu.be/HS4rTCcaSPE), and received positive feedback for its usability and features.
Xixian Liang, Cheng Peng, Bo Ma et al.· SIGSOFT FSE Companion· 0 citations
Deep learning (DL) compilers such as Apache TVM translate high-level models into optimized low-level code through multi-stage compilation pipelines. While recent testing efforts have improved fuzzing of optimization stages, they still face two key challenges: (i) the lack of semantics-preserving test models, leading to low validity, and (ii) coarse-grained input generation that fails to trigger hard-to-reach compiler components. To address these limitations, we propose CovCraft, a unified testing framework that integrates constraint-driven model generation with large language model (LLM)-guided input adaptation. CovCraft constructs diverse and valid ONNX models via symbolic constraint encoding and SMT solving, and then iteratively refines inputs using LLM-guided prompts to target uncovered functions, enabling the activation of rarely executed code paths. We evaluate CovCraft on TVM and observe consistent improvements over state-of-the-art techniques: it increases branch and function coverage by 8.9% and 7.0%, respectively, and detects 8 bugs. Moreover, the LLM-guided component achieves an 83.75% success rate in covering designated target functions, demonstrating the effectiveness of combining constraint-based generation with adaptive LLM reasoning for DL compiler testing. The prototype implementation of CovCraft is publicly available at: https://github.com/duduhedangdang/CovCraft.
Yifei He, Fangyu Yang, Ting Su et al.· Annual International Compute...· 0 citations