Visible to the public Effective API Recommendation Without Historical Software Repositories

TitleEffective API Recommendation Without Historical Software Repositories
Publication TypeConference Paper
Year of Publication2018
AuthorsLiu, Xiaoyu, Huang, LiGuo, Ng, Vincent
Conference NameProceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5937-5
KeywordsAPI Recommendation, APIs, compositionality, machine learning, pubcrawl, resilience, Resiliency
AbstractIt is time-consuming and labor-intensive to learn and locate the correct API for programming tasks. Thus, it is beneficial to perform API recommendation automatically. The graph-based statistical model has been shown to recommend top-10 API candidates effectively. It falls short, however, in accurately recommending an actual top-1 API. To address this weakness, we propose RecRank, an approach and tool that applies a novel ranking-based discriminative approach leveraging API usage path features to improve top-1 API recommendation. Empirical evaluation on a large corpus of (1385+8) open source projects shows that RecRank significantly improves top-1 API recommendation accuracy and mean reciprocal rank when compared to state-of-the-art API recommendation approaches.
Citation Keyliu_effective_2018