Mining Stack Overflow for API class recommendation using DOC2VEC and LDA
Abstract To address the lexical gaps between natural language (NL) queries and Application Programming Interface (API) documentations, and between NL queries and programme code, this study developed a novel approach for recommending Java API classes that are relevant to the programming tasks descri...
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| Format: | Article |
| Language: | English |
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Wiley
2021-10-01
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| Series: | IET Software |
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| Online Access: | https://doi.org/10.1049/sfw2.12023 |
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| _version_ | 1832559568235790336 |
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| author | Wai Keat Lee Moon Ting Su |
| author_facet | Wai Keat Lee Moon Ting Su |
| author_sort | Wai Keat Lee |
| collection | DOAJ |
| description | Abstract To address the lexical gaps between natural language (NL) queries and Application Programming Interface (API) documentations, and between NL queries and programme code, this study developed a novel approach for recommending Java API classes that are relevant to the programming tasks described in NL queries. A Doc2Vec model was trained using question titles mined from Stack Overflow. The model was used to find question titles that are semantically similar to a query. Latent Dirichlet Allocation (LDA) topic modelling was applied on the Java API classes (extracted from code snippets found in the accepted answers of these similar questions) to extract a single topic comprising of the Top‐10 Java API classes that are relevant to the query. The benchmarking of the proposed approach against state‐of‐the‐art approaches, RACK and NLP2API, by using four performance metrics show that it is possible to produce comparable API recommendation results using a less complex approach that makes use of some basic machine learning models, in particular, Doc2Vec and LDA. The approach was implemented in a Java API class recommender with an Eclipse IDE's plug‐in serving as the front‐end. |
| format | Article |
| id | doaj-art-911df404890240d3bc6325e72919fa50 |
| institution | Kabale University |
| issn | 1751-8806 1751-8814 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-911df404890240d3bc6325e72919fa502025-02-03T01:29:44ZengWileyIET Software1751-88061751-88142021-10-0115530832210.1049/sfw2.12023Mining Stack Overflow for API class recommendation using DOC2VEC and LDAWai Keat Lee0Moon Ting Su1Department of Software Engineering Faculty of Computer Science and Information Technology, University of Malaya Kuala Lumpur MalaysiaDepartment of Software Engineering Faculty of Computer Science and Information Technology, University of Malaya Kuala Lumpur MalaysiaAbstract To address the lexical gaps between natural language (NL) queries and Application Programming Interface (API) documentations, and between NL queries and programme code, this study developed a novel approach for recommending Java API classes that are relevant to the programming tasks described in NL queries. A Doc2Vec model was trained using question titles mined from Stack Overflow. The model was used to find question titles that are semantically similar to a query. Latent Dirichlet Allocation (LDA) topic modelling was applied on the Java API classes (extracted from code snippets found in the accepted answers of these similar questions) to extract a single topic comprising of the Top‐10 Java API classes that are relevant to the query. The benchmarking of the proposed approach against state‐of‐the‐art approaches, RACK and NLP2API, by using four performance metrics show that it is possible to produce comparable API recommendation results using a less complex approach that makes use of some basic machine learning models, in particular, Doc2Vec and LDA. The approach was implemented in a Java API class recommender with an Eclipse IDE's plug‐in serving as the front‐end.https://doi.org/10.1049/sfw2.12023application program interfacesdata miningdocument handlingJavalearning (artificial intelligence)natural language processing |
| spellingShingle | Wai Keat Lee Moon Ting Su Mining Stack Overflow for API class recommendation using DOC2VEC and LDA IET Software application program interfaces data mining document handling Java learning (artificial intelligence) natural language processing |
| title | Mining Stack Overflow for API class recommendation using DOC2VEC and LDA |
| title_full | Mining Stack Overflow for API class recommendation using DOC2VEC and LDA |
| title_fullStr | Mining Stack Overflow for API class recommendation using DOC2VEC and LDA |
| title_full_unstemmed | Mining Stack Overflow for API class recommendation using DOC2VEC and LDA |
| title_short | Mining Stack Overflow for API class recommendation using DOC2VEC and LDA |
| title_sort | mining stack overflow for api class recommendation using doc2vec and lda |
| topic | application program interfaces data mining document handling Java learning (artificial intelligence) natural language processing |
| url | https://doi.org/10.1049/sfw2.12023 |
| work_keys_str_mv | AT waikeatlee miningstackoverflowforapiclassrecommendationusingdoc2vecandlda AT moontingsu miningstackoverflowforapiclassrecommendationusingdoc2vecandlda |