Open AccessAPI Recommendation System for Software - Game Category
Author(s) -
Luisa Hernández,
Paulo de Marco Júnior,
Heitor Costa
Publication year - 2016
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbqs.2016.15126
Subject(s) - computer science , application programming interface , software development , software , software engineering , ranking (information retrieval) , software metric , software bug , metric (unit) , recommender system , precision and recall , software construction , world wide web , information retrieval , operating system , engineering , operations management
Software development depends on Application Programming Interfaces (APIs) to achieve their goals. However, choosing the right APIs remains as a difficult ask for software engineers. Considering that recommendation systems are emerging to support software engineers in their decision-making task and Games industry has a huge economic and cultural success, we proposed a technique that considers Game category from SourceForge and recommends PIs to software engineers with software in initial (not using APIs) or advanced (using some APIs) stage of software development. We used collaborative filtering technique along with frequent Itemset mining technique for generating the corresponding large and top-N lists of APIs recommended. We evaluated lists performance based on two classification accuracy metrics (precision and recall) and one efficacy metric (recall rate), obtaining promising outcomes. Thus, the results of evaluation metrics showed that our technique could make useful API recommendations for software engineers with Game software that used a small number of APIs or did not use any API. Besides, our technique was able to put relevant APIs even in high-ranking positions, even in small top-N lists, of APIs recommended.