Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph

• Published in: 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) pp. 451 - 461
• Main Authors: Cheng, Wei, Zhu, Xiangrong, Hu, Wei
• Format: Conference Proceeding
• Language: English
• Published: ACM 01.05.2022
• Subjects: Codes; Configuration management; Conflict resolution; Dependency solving; Knowledge engineering; Knowledge graph; Libraries; NP-complete problem; Ontologies; Python; Runtime environment; Runtime environment inference; Software algorithms
• ISSN: 1558-1225
• DOI: 10.1145/3510003.3510078

Code sharing and reuse is a widespread use practice in software engineering. Although a vast amount of open-source Python code is accessible on many online platforms, programmers often find it difficult to restore a successful runtime environment. Previous studies validated automatic inference of Python dependencies using pre-built knowledge bases. However, these studies do not cover sufficient knowledge to accurately match the Python code and also ignore the potential conflicts between their inferred dependencies, thus resulting in a low success rate of inference. In this paper, we propose PyCRE, a new approach to automatically inferring Python compatible runtime environments with domain knowledge graph (KG). Specifically, we design a domain-specific ontology for Python third-party packages and construct KGs for over 10,000 popular packages in Python 2 and Python 3. PyCRE discovers candidate libraries by measuring the matching degree between the known libraries and the third-party resources used in target code. For the NP-complete problem of dependency solving, we propose a heuristic graph traversal algorithm to efficiently guarantee the compatibility between packages. PyCRE achieves superior performance on a real-world dataset and efficiently resolves nearly half more import errors than previous methods.