Article (Périodiques scientifiques)
Fusing Code Searchers
Wang, Shangwen; Geng, Mingyang; Lin, Bo et al.
2024In IEEE Transactions on Software Engineering, 50 (7), p. 1852 - 1866
Peer reviewed vérifié par ORBi
 

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Détails



Mots-clés :
Code search; data fusion; information retrieval; Basic hypothesis; Code; Different mechanisms; Intrinsic differences; Pre-training; Query provides; Relevance score; Search technique; State of the art; Software; Codes
Résumé :
[en] Code search, which consists in retrieving relevant code snippets from a codebase based on a given query, provides developers with useful references during software development. Over the years, techniques alternatively adopting different mechanisms to compute the relevance score between a query and a code snippet have been proposed to advance the state of the art in this domain, including those relying on information retrieval, supervised learning, and pre-training. Despite that, the usefulness of existing techniques is still compromised since they cannot effectively handle all the diversified queries and code in practice. To tackle this challenge, we present Dancer, a data fusion based code searcher. Our intuition (also the basic hypothesis of this study) is that existing techniques may complement each other because of the intrinsic differences in their working mechanisms. We have validated this hypothesis via an exploratory study. Based on that, we propose to fuse the results generated by different code search techniques so that the advantage of each standalone technique can be fully leveraged. Specifically, we treat each technique as a retrieval system and leverage well-known data fusion approaches to aggregate the results from different systems. We evaluate six existing code search techniques on two large-scale datasets, and exploit eight classic data fusion approaches to incorporate their results. Our experiments show that the best fusion approach is able to outperform the standalone techniques by 35%-550% and 65%-825% in terms of MRR (mean reciprocal rank) on the two datasets, respectively.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > TruX - Trustworthy Software Engineering
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Wang, Shangwen ;  National University of Defense Technology, Changsha, China
Geng, Mingyang ;  National University of Defense Technology, Changsha, China
Lin, Bo ;  National University of Defense Technology, Changsha, China
Sun, Zhensu ;  ShanghaiTech University, Shanghai, China
Wen, Ming ;  Huazhong University of Science and Technology, Wuhan, China
Liu, Yepang ;  Southern University of Science and Technology, Shenzhen, China
Li, Li ;  Monash University, Melbourne, Australia
BISSYANDE, Tegawendé François d Assise  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Mao, Xiaoguang ;  National University of Defense Technology, Changsha, China
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Fusing Code Searchers
Date de publication/diffusion :
2024
Titre du périodique :
IEEE Transactions on Software Engineering
ISSN :
0098-5589
eISSN :
1939-3520
Maison d'édition :
Institute of Electrical and Electronics Engineers Inc.
Volume/Tome :
50
Fascicule/Saison :
7
Pagination :
1852 - 1866
Peer reviewed :
Peer reviewed vérifié par ORBi
Projet européen :
H2020 - 949014 - NATURAL - Natural Program Repair
Intitulé du projet de recherche :
R-AGR-3885 - H2020-ERC StG - NATURAL - BISSYANDE Tegawendé
Organisme subsidiant :
ERC - European Research Council
European Union
N° du Fonds :
949014
Disponible sur ORBilu :
depuis le 10 décembre 2024

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