TY - GEN
T1 - PROPER-SDP
T2 - 5th International Workshop on Big Data Driven Edge Cloud Services, BECS 2025 and 3rd International Workshop on the Semantic WEb of EveryThing, SWEET 2025, co-located with 25th International Conference on Web Engineering, ICWE 2025
AU - Yeo, Inseok
AU - Lee, Sungu
AU - Ryu, Duksan
AU - Baik, Jongmoon
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Edge-cloud systems, which bring computing, storage, and networking resources closer to end-users, offer significant advantages in reducing latency and enabling real-time data processing. Ensuring software reliability in these environments is critical, which has led to growing attention on Just-in-Time (JIT) defect prediction as an effective technique for prioritizing testing efforts by identifying code changes likely to introduce defects. However, edge-cloud systems often face challenges such as data scarcity, rapid project evolution, and limited historical defect information. These characteristics lead to the cold-start problem, where prediction models struggle to perform accurately on new or low-data projects due to the lack of training data. In this paper, we propose a novel prompt-based approach that uses Large Language Models (LLMs) in a prompt-based framework. By incorporating project evolution data directly into prompts, our approach enables LLMs to effectively capture the contextual information essential for accurate JIT defect prediction. Evaluation results demonstrate that our method significantly improves prediction performance, surpassing baseline method by an average of 13% in F1 score. This approach offers a practical solution for achieving high-accuracy JIT defect prediction in resource-constrained, rapidly evolving edge-cloud environments.
AB - Edge-cloud systems, which bring computing, storage, and networking resources closer to end-users, offer significant advantages in reducing latency and enabling real-time data processing. Ensuring software reliability in these environments is critical, which has led to growing attention on Just-in-Time (JIT) defect prediction as an effective technique for prioritizing testing efforts by identifying code changes likely to introduce defects. However, edge-cloud systems often face challenges such as data scarcity, rapid project evolution, and limited historical defect information. These characteristics lead to the cold-start problem, where prediction models struggle to perform accurately on new or low-data projects due to the lack of training data. In this paper, we propose a novel prompt-based approach that uses Large Language Models (LLMs) in a prompt-based framework. By incorporating project evolution data directly into prompts, our approach enables LLMs to effectively capture the contextual information essential for accurate JIT defect prediction. Evaluation results demonstrate that our method significantly improves prediction performance, surpassing baseline method by an average of 13% in F1 score. This approach offers a practical solution for achieving high-accuracy JIT defect prediction in resource-constrained, rapidly evolving edge-cloud environments.
KW - Edge-cloud system
KW - Just-in-time defect prediction
KW - Large Language Model
UR - https://www.scopus.com/pages/publications/105027182199
U2 - 10.1007/978-3-032-11233-0_6
DO - 10.1007/978-3-032-11233-0_6
M3 - Conference paper
AN - SCOPUS:105027182199
SN - 9783032112323
T3 - Communications in Computer and Information Science
SP - 68
EP - 81
BT - The Inclusive Web
A2 - Hsu, Yen-Chia
A2 - Systä, Kari
A2 - Ko, In-Young
A2 - Gramegna, Filippo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 30 June 2025 through 3 July 2025
ER -