NLP2API: Query Reformulation for Code Search Using Crowdsourced Knowledge and Extra-Large Data Analytics

التفاصيل البيبلوغرافية
العنوان: NLP2API: Query Reformulation for Code Search Using Crowdsourced Knowledge and Extra-Large Data Analytics
المؤلفون: Chanchal K. Roy, Mohammad Masudur Rahman
المصدر: ICSME
بيانات النشر: IEEE, 2018.
سنة النشر: 2018
مصطلحات موضوعية: Information retrieval, Java, business.industry, Computer science, 020207 software engineering, 02 engineering and technology, Upload, Search engine, Software, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), Data analysis, Vocabulary mismatch, 020201 artificial intelligence & image processing, business, computer, Natural language, computer.programming_language
الوصف: Software developers frequently issue generic natural language (NL) queries for code search. Unfortunately, such queries often do not lead to any relevant results with contemporary code (or web) search engines due to vocabulary mismatch problems. In our technical research paper (accepted at ICSME 2018), we propose a technique–NLP2API–that reformulates such NL queries using crowdsourced knowledge and extra-large data analytics derived from Stack Overflow Q & A site. In this paper, we discuss all the artifacts produced by our work, and provide necessary details for downloading and verifying them.
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::89b5d1fbb44fde752729e69a6afddf14Test
https://doi.org/10.1109/icsme.2018.00086Test
رقم الانضمام: edsair.doi...........89b5d1fbb44fde752729e69a6afddf14
قاعدة البيانات: OpenAIRE