Publication Type
Edited Conference Proceeding
Version
acceptedVersion
Publication Date
2-2020
Abstract
Code search methods, especially those that allow programmers to raise queries in a natural language, plays an important role in software development. It helps to improve programmers' productivity by returning sample code snippets from the Internet and/or source-code repositories for their natural-language queries. Meanwhile, there are many code search methods in the literature that support natural-language queries. Difficulties exist in recognizing the strengths and weaknesses of each method and choosing the right one for different usage scenarios, because (1) the implementations of those methods and the datasets for evaluating them are usually not publicly available, and (2) some methods leverage different training datasets or auxiliary data sources and thus their effectiveness cannot be fairly measured and may be negatively affected in practical uses. To build a common ground for measuring code search methods, this paper builds CosBench, a dataset that consists of 1000 projects, 52 code-independent natural-language queries with ground truths, and a set of scripts for calculating four metrics on code research results. We have evaluated four IR (Information Retrieval)-based and two DL (Deep Learning)-based code search methods on CosBench. The empirical evaluation results clearly show the usefulness of the CosBench dataset and various strengths of each code search method. We found that DL-based methods are more suitable for queries on reusing code, and IR-based ones for queries on resolving bugs and learning API uses.
Keywords
natural-language code search, benchmarking, empirical study, information retrieval, machine learning, deep learning, word embedding
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER): Ontario, Canada, February 18-21: Proceedings
First Page
344
Last Page
354
ISBN
9781728151434
Identifier
10.1109/SANER48275.2020.9054840
Publisher
IEEE
City or Country
Piscataway, NJ
Embargo Period
5-31-2021
Citation
YAN, Shuhan; YU, Hang; CHEN, Yuting; and SHEN, Beijun.
Are the code snippets what we are searching for? A benchmark and an empirical study on code search with natural-language queries. (2020). 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER): Ontario, Canada, February 18-21: Proceedings. 344-354.
Available at: https://ink.library.smu.edu.sg/sis_research/5975
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/SANER48275.2020.9054840