IT Home January 7 news, according to the Chinese Academy of Sciences website, recently, the Chinese Academy of Sciences Institute of Software Software Research and Development Center in the Python program construction of the software engineering technology research and development center in the Python program construction has made research progress, put forward the knowledge-driven Python program dependency inference methods and tools, to help developers improve code reuse efficiency, reduce the lack of dependency and dependency version errors caused by Python program construction and operation errors, It plays a supporting role in improving the ability to automate application construction in the integration of DevOps and Maintenance.
According to reports, the Python language is widely used in scientific computing, etc., and developers often improve development efficiency through code reuse. However, Python programs run in a complex environment and rely on Python packages, system libraries, and specific versions of the Python interpreter. Missing program dependencies or incompatible dependent versions can cause program builds to fail and run errors.
Aiming at this problem, a knowledge-driven Python program dependency inference method is proposed, including two stages: knowledge graph construction and program dependency inference. In the knowledge graph construction stage, this method collects a large amount of multi-source heterogeneous data, extracts and integrates knowledge, and constructs a python domain knowledge graph. In the program dependency inference stage, this method is based on the domain knowledge graph, and the multi-level dependencies of the target Python program are inferred through the program analysis and constraint solving method.
IT House learned that based on the above approach, the research developed PyEGo: a knowledge-driven Python program dependency inference tool. Experimental results show that the dependency inference success rate of PyEGo tool is 1.5-4.5 times that of the existing methods, which greatly improves the correct rate and execution efficiency of program construction.
▲ Schematic diagram of Python program dependency analysis method