Revisiting Knowledge-Based Inference of Python Runtime Environments: A Realistic and Adaptive Approach

The reuse and integration of existing code is a common practice for efficient software development. Constantly updated Python interpreters and third-party packages introduce many challenges to Python runtime environment inference. Existing knowledge-based approaches have achieved good performance but still suffer from several limitations in the real world, especially from incomplete domain knowledge. In this paper, we propose ReadPyE, a realistic and adaptive approach to Python runtime environment inference. To leverage the rich code information, we present an automated approach to the construction and maintenance of our designed Python ecosystem knowledge graph (KG). Moreover, we are the first to handle real-world challenges such as complex dependency specifications and incomplete domain knowledge. Specifically, we define a naming similarity measure to match candidate packages for unknown modules and set priorities for multiple candidate packages. ReadPyE solves the optimization problems of candidate package selection and generates compatible runtime environments step by step based on the current Python environment. The inferred environments are iteratively validated and adjusted by matched exception templates in the validation logs. The evaluation results on three real-world datasets show the superior effectiveness and good efficiency of our ReadPyE compared to the existing knowledge-based approaches. ReadPyE solves the environment-related exceptions for 79.75% single-file code snippets, 93% Python projects, and 63.34% program pairs for code integration. We believe ReadPyE can help programmers reduce the time spent on inferring Python runtime environments and facilitate automated software configuration management.