| Title | Effective API Recommendation Without Historical Software Repositories |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Liu, Xiaoyu, Huang, LiGuo, Ng, Vincent |
| Conference Name | Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5937-5 |
| Keywords | API Recommendation, APIs, compositionality, machine learning, pubcrawl, resilience, Resiliency |
| Abstract | It 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. |
| DOI | 10.1145/3238147.3238216 |
| Citation Key | liu_effective_2018 |
Effective API Recommendation Without Historical Software Repositories