基于多粒度语义匹配的编程任务解决方案推荐OA

Existing programming solution recommendation methods either directly ignore the full text of the question or fail to find a suitable way to exert its value,resulting in inaccurate recommendation results.In this paper,we propose MGSMR,a programming task-oriented solution recommendation method based on multi-granularity semantic matching.For a given programming task,MGSMR used the semantic matching model to find relevant question-and-answer discussions as candidate solutions based on two different granularities of the question title and the full text of the question.It further re-ranked the candidate solutions based on the multi-granularity matching method.It combined candidate solutions with external knowledge such as API documentation and third-party libraries to generate recommendation solutions.The evaluation shows that this method outperforms baselines in terms of multiple IR indicators of two datasets by 18%～26%and 2%～4%,andsolution generation can help participants solve programming tasks 23%faster and 22%more accurately.