research-article

Active code search: incorporating user feedback to improve code search relevance

Authors:

Shaowei Wang,

David Lo,

Lingxiao JiangAuthors Info & Claims

ASE '14: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

Pages 677 - 682

https://doi.org/10.1145/2642937.2642947

Published: 15 September 2014 Publication History

Get Access

Abstract

Code search techniques return relevant code fragments given a user query. They typically work in a passive mode: given a user query, a static list of code fragments sorted by the relevance scores decided by a code search technique is returned to the user. A user will go through the sorted list of returned code fragments from top to bottom. As the user checks each code fragment one by one, he or she will naturally form an opinion about the true relevance of the code fragment. In an active model, those opinions will be taken as feedbacks to the search engine for refining result lists.

In this work, we incorporate users' opinion on the results from a code search engine to refine result lists: as a user forms an opinion about one result, our technique takes this opinion as feedback and leverages it to re-order the results to make truly relevant results appear earlier in the list. The refinement results can also be cached to potentially improve future code search tasks. We have built our active refinement technique on top of a state-of-the-art code search engine---Portfolio. Our technique improves Portfolio in terms of Normalized Discounted Cumulative Gain (NDCG) by more than 11.3%, from 0.738 to 0.821.

References

[1]

W.-K. Chan, H. Cheng, and D. Lo. Searching connected api subgraph via text phrases. In SIGSOFT FSE, 2012.

Digital Library

Google Scholar

[2]

G. Gay, S. Haiduc, A. Marcus, and T. Menzies. On the use of relevance feedback in IR-based concept location. In ICSM, 2009.

Crossref

Google Scholar

[3]

S. Haiduc, G. Bavota, A. Marcus, R. Oliveto, A. D. Lucia, and T. Menzies. Automatic query reformulations for text retrieval in software engineering. In ICSE, 2013.

Digital Library

Google Scholar

[4]

J. Hayes, A. Dekhtyar, and S. Sundaram. Advanced candidate link generation for requirements tracing: The study of methods. In TSE, 2006.

Digital Library

Google Scholar

[5]

Lucia, D. Lo, L. Jiang, and A. Budi. Active refinement of clone anomaly reports. In ICSE, 2012.

Digital Library

Google Scholar

[6]

C. D. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge, 2008.

Crossref

Google Scholar

[7]

C. McMillan, M. Grechanik, D. Poshyvanyk, Q. Xie, and C. Fu. Portfolio: finding relevant functions and their usage. In ICSE, 2011.

Digital Library

Google Scholar

[8]

C. McMillan, D. Poshyvanyk, M. Grechanik, Q. Xie, and C. Fu. Portfolio: Searching for relevant functions and their usages in millions of lines of code. TOSEM, 22(4), 2013.

Digital Library

Google Scholar

[9]

S. Wang, D. Lo, and L. Jiang. Code search via topic-enriched dependence graph matching. In WCRE, 2011.

Digital Library

Google Scholar

[10]

X. Wang, D. Lo, J. Cheng, L. Zhang, H. Mei, and J. X. Yu. Matching dependence-related queries in the system dependence graph. In ASE, 2010.

Digital Library

Google Scholar

Cited By

View all

Khan MYu Z(2024)Approaching code search for python as a translation retrieval problem with dual encodersEmpirical Software Engineering10.1007/s10664-024-10580-330:1Online publication date: 30-Oct-2024
https://doi.org/10.1007/s10664-024-10580-3
Rahman MRoy C(2023)A Systematic Review of Automated Query Reformulations in Source Code SearchACM Transactions on Software Engineering and Methodology10.1145/360717932:6(1-79)Online publication date: 4-Jul-2023
https://dl.acm.org/doi/10.1145/3607179
Kim KGhatpande SKim DZhou XLiu KBissyandé TKlein JLe Traon Y(2023)Big Code Search: A BibliographyACM Computing Surveys10.1145/360490556:1(1-49)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3604905
Show More Cited By

Index Terms

Active code search: incorporating user feedback to improve code search relevance
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
        Software maintenance
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues

Recommendations

Deep code search
ICSE '18: Proceedings of the 40th International Conference on Software Engineering

To implement a program functionality, developers can reuse previously written code snippets by searching through a large-scale codebase. Over the years, many code search tools have been proposed to help developers. The existing approaches often treat ...
Learning to rank code examples for code search engines

Source code examples are used by developers to implement unfamiliar tasks by learning from existing solutions. To better support developers in finding existing solutions, code search engines are designed to locate and rank code examples relevant to user'...
Code Search is All You Need? Improving Code Suggestions with Code Search
ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ASE '14: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

September 2014

934 pages

ISBN:9781450330138

DOI:10.1145/2642937

General Chair:
Ivica Crnkovic
Mälardalen University, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Paul Grünbacher
Johannes Kepler Universität Linz, Austria

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 September 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ASE '14

Sponsor:

SIGAI
SIGSOFT
Mälardalen University

ASE '14: ACM/IEEE International Conference on Automated Software Engineering

September 15 - 19, 2014

Vasteras, Sweden

Acceptance Rates

ASE '14 Paper Acceptance Rate 82 of 337 submissions, 24%;

Overall Acceptance Rate 82 of 337 submissions, 24%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

49
Total Citations
View Citations
407
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)2

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Khan MYu Z(2024)Approaching code search for python as a translation retrieval problem with dual encodersEmpirical Software Engineering10.1007/s10664-024-10580-330:1Online publication date: 30-Oct-2024
https://doi.org/10.1007/s10664-024-10580-3
Rahman MRoy C(2023)A Systematic Review of Automated Query Reformulations in Source Code SearchACM Transactions on Software Engineering and Methodology10.1145/360717932:6(1-79)Online publication date: 4-Jul-2023
https://dl.acm.org/doi/10.1145/3607179
Kim KGhatpande SKim DZhou XLiu KBissyandé TKlein JLe Traon Y(2023)Big Code Search: A BibliographyACM Computing Surveys10.1145/360490556:1(1-49)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3604905
Di Grazia LPradel M(2023)Code Search: A Survey of Techniques for Finding CodeACM Computing Surveys10.1145/356597155:11(1-31)Online publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1145/3565971
Pérez FLapeña RMarcén ACetina C(2023)How the Quality of Maintenance Tasks is Affected by Criteria for Selecting Engineers for CollaborationACM Transactions on Software Engineering and Methodology10.1145/356138432:3(1-22)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1145/3561384
Zeng CYu YLi SXia XWang ZGeng MBai LDong WLiao X(2023)deGraphCS: Embedding Variable-based Flow Graph for Neural Code SearchACM Transactions on Software Engineering and Methodology10.1145/354606632:2(1-27)Online publication date: 30-Mar-2023
https://dl.acm.org/doi/10.1145/3546066
Liu KChen XChen CXie XCui Z(2023)Automated Question Title Reformulation by Mining Modification Logs From Stack OverflowIEEE Transactions on Software Engineering10.1109/TSE.2023.329239949:9(4390-4410)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TSE.2023.3292399
Jin HZhou YHussain Y(2023)Enhancing Code Completion with Implicit Feedback2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS)10.1109/QRS60937.2023.00030(218-227)Online publication date: 22-Oct-2023
https://doi.org/10.1109/QRS60937.2023.00030
Zhou YYang XChen THuang ZMa XGall H(2022)Boosting API Recommendation With Implicit FeedbackIEEE Transactions on Software Engineering10.1109/TSE.2021.305311148:6(2157-2172)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TSE.2021.3053111
Rubei RDi Sipio CDi Rocco JDi Ruscio DNguyen P(2022)Endowing third-party libraries recommender systems with explicit user feedback mechanisms2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER53432.2022.00099(817-821)Online publication date: Mar-2022
https://doi.org/10.1109/SANER53432.2022.00099
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Deep code search

Learning to rank code examples for code search engines

Code Search is All You Need? Improving Code Suggestions with Code Search