ROSF: Leveraging Information Retrieval and Supervised Learning for Recommending Code Snippets

He Jiang, Liming Nie, Zeyi Sun, Zhilei Ren, Weiqiang Kong, Tao Zhang, Xiapu Luo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)

Abstract

When implementing unfamiliar programming tasks, developers commonly search code examples and learn usage patterns of APIs from the code examples or reuse them by copy-pasting and modifying. For providing high-quality code examples, previous studies present several methods to recommend code snippets mainly based on information retrieval. In this paper, to provide better recommendation results, we propose ROSF, Recommending code Snippets with multi-aspect Features, a novel method combining both information retrieval and supervised learning. In our method, we recommend Top-K code snippets for a given free-form query based on two stages, i.e., coarse-grained searching and fine-grained re-ranking. First, we generate a code snippet candidate set by searching a code snippet corpus using an information retrieval method. Second, we predict probability values of the code snippets for different relevance scores in the candidate set by the learned prediction model from a training set, re-rank these candidate code snippets according to the probability values, and recommend the final results to developers. We conduct several experiments to evaluate our method in a large-scale corpus containing 921,713 real-world code snippets. The results show that ROSF is an effective method for code snippets recommendation and outperforms the-state-of-the-art methods by 20-41percent in Precision and 13-33 percent in NDCG.

Original languageEnglish
Article number7516727
Pages (from-to)34-46
Number of pages13
JournalIEEE Transactions on Services Computing
Volume12
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Code snippets recommendation
  • feature
  • information retrieval
  • supervised learning
  • topic model

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management

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