Effective API Recommendation without Historical Software Repositories

Xiaoyu Liu, LiGuo Huang, and Vincent Ng.
Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering, pp. 282-292, 2018.

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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.

BibTeX entry

@InProceedings{Liu+etal:18b,
  author = {Xiaoyu Liu and LiGuo Huang and Vincent Ng},
  title = {Effective API Recommendation without Historical Software Repositories},
  booktitle = {Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering},
  pages = {282--292},
  year = 2018}