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Searching crowd knowledge to recommend solutions for API usage tasks
Author(s) -
Campos Eduardo C.,
Souza Lucas B. L.,
Maia Marcelo de A.
Publication year - 2016
Publication title -
journal of software: evolution and process
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 29
eISSN - 2047-7481
pISSN - 2047-7473
DOI - 10.1002/smr.1800
Subject(s) - computer science , ranking (information retrieval) , software , service (business) , similarity (geometry) , information retrieval , quality (philosophy) , world wide web , artificial intelligence , philosophy , economy , epistemology , economics , image (mathematics) , programming language
Stack Overflow (SO) is a question and answer service directed to issues related to software development. In SO, developers post questions related to a programming topic and other members of the site can provide answers to help them. The information available on this type of service is also known as ‘crowd knowledge’ and currently is one important trend in supporting activities related to software development. We present an approach that makes use of ‘crowd knowledge’ in SO to recommend information that can assist developer activities. This strategy recommends a ranked list of question‐answer pairs from SO based on a query. The criteria for ranking are based on three main aspects: the textual similarity of the pairs with respect to the query related to the developer's problem, the quality of the pairs, and a filtering mechanism that considers only ‘how‐to’ posts. We conducted an experiment considering programming problems on three different topics (Swing, Boost and LINQ) widely used by the software development community to evaluate the proposed recommendation strategy. The results have shown that for Lucene + Score + How-to approach, 77.14% of the assessed activities have at least one recommended pair proved to be useful concerning the target programming problem. Copyright © 2016 John Wiley & Sons, Ltd.