RECOMMENDING JAVA API METHODS BASED ON PROGRAMMING TASK DESCRIPTIONS BY NOVICE PROGRAMMERS - Lim, Chun Jiann; Su, Moon Ting (Malaysian Journal of Computer Science, 2023)
The overwhelming number of Application Programming Interfaces (APIs) and the lexical gap between novices’ programming task descriptions in their search queries and API documentations deter novice programmers from finding suitable API methods to be used in their code. To address the lexical gap, this study investigated novice programmers’ descriptions of their programming tasks and used the found insights in a novel approach (APIFind) for recommending relevant API methods for the programming tasks. Queries written by novice programmers were collected and analysed using term frequency and constituency parsing. Four common patterns related to the return type of an API method and/or API class that provides an implementation for the API method were found and captured in the Novice Programming Task Description Model (NPTDM). APIFind uses NPTDM that was operationalised in a rule-based module, a WordNet map of API word-synonyms, a programming task dataset comprising the collected queries, a Java API class and method repository, a Stack Overflow Q&A thread repository, and the BM25 model in Apache Lucene, to produce the top-5 API methods relevant to a search query. Benchmarking results using mean average precision @ 5 and mean reciprocal rank @ 5 as the evaluation metrics show that APIFind outperformed BIKER and CROKAGE when the novice queries test dataset was used. It performed slightly better than BIKER but slightly worse than CROKAGE when the reduced BIKER test dataset was used. In conclusion, common patterns exist in novice programmers’ search queries and can be used in API recommendations for novice programmers.