TY - GEN
T1 - CodeBERT Based Software Defect Prediction for Edge-Cloud Systems
AU - Kwon, Sunjae
AU - Jang, Jong In
AU - Lee, Sungu
AU - Ryu, Duksan
AU - Baik, Jongmoon
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Edge-cloud system is a crucial computing infrastructure for the innovations of modern society. In addition, the high interest in the edge-cloud system leads to various studies for testing to ensure the reliability of the system. However, like traditional software systems, the amount of resources for testing is always limited. Thus, we suggest CodeBERT Based Just-In-Time (JIT) Software Defect Prediction (SDP) model to address the limitation. This method helps practitioners prioritize the limited testing resources for the defect-prone functions in commits and improves the system’s reliability. We generate GitHub Pull-Request (GHPR) datasets on two open-source framework projects for edge-cloud system in GitHub. After that, we evaluate the performance of the proposed model on the GHPR datasets in within-project environment and cross-project environment. To the best of our knowledge, it is the first attempt to apply SDP to edge-cloud systems, and as a result of the evaluation, we can confirm the applicability of JIT SDP in edge-cloud project. In addition, we expect the proposed method would be helpful for the effective allocation of limited resources when developing edge-cloud systems.
AB - Edge-cloud system is a crucial computing infrastructure for the innovations of modern society. In addition, the high interest in the edge-cloud system leads to various studies for testing to ensure the reliability of the system. However, like traditional software systems, the amount of resources for testing is always limited. Thus, we suggest CodeBERT Based Just-In-Time (JIT) Software Defect Prediction (SDP) model to address the limitation. This method helps practitioners prioritize the limited testing resources for the defect-prone functions in commits and improves the system’s reliability. We generate GitHub Pull-Request (GHPR) datasets on two open-source framework projects for edge-cloud system in GitHub. After that, we evaluate the performance of the proposed model on the GHPR datasets in within-project environment and cross-project environment. To the best of our knowledge, it is the first attempt to apply SDP to edge-cloud systems, and as a result of the evaluation, we can confirm the applicability of JIT SDP in edge-cloud project. In addition, we expect the proposed method would be helpful for the effective allocation of limited resources when developing edge-cloud systems.
KW - CodeBERT
KW - Edge-cloud system
KW - Just-in-time software defect prediction
UR - https://www.scopus.com/pages/publications/85149690376
U2 - 10.1007/978-3-031-25380-5_1
DO - 10.1007/978-3-031-25380-5_1
M3 - Conference paper
AN - SCOPUS:85149690376
SN - 9783031253799
T3 - Communications in Computer and Information Science
SP - 11
EP - 21
BT - Current Trends in Web Engineering - ICWE 2022 International Workshops, 2022, Revised Selected Papers
A2 - Agapito, Giuseppe
A2 - Bernasconi, Anna
A2 - Cappiello, Cinzia
A2 - Pinoli, Pietro
A2 - Khattak, Hasan Ali
A2 - Ko, InYoung
A2 - Loseto, Giuseppe
A2 - Mrissa, Michael
A2 - Nanni, Luca
A2 - Ragone, Azzurra
A2 - Ruta, Michele
A2 - Scioscia, Floriano
A2 - Srivastava, Abhishek
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Workshop on Big Data driven Edge Cloud Services, BECS 2022, 1st International Workshop on the Semantic WEb of Everything, SWEET 2022 and 1st International Workshop on Web Applications for Life Sciences, WALS 2022 held in conjunction with 22nd International Conference on Web Engineering, ICWE 2022
Y2 - 5 July 2022 through 8 July 2022
ER -