Pre-trained Model-based Software Defect Prediction for Edge-cloud Systems

Edge-cloud computing is a distributed computing infrastructure that brings computation and data storage with low latency closer to clients. As interest in edge-cloud systems grows, research on testing the systems has also been actively studied. However, as with traditional systems, the amount of res...

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Published in:Journal of web engineering Vol. 22; no. 2; p. 255
Main Authors: Kwon, Sunjae, Lee, Sungu, Ryu, Duksan, Baik, Jongmoon
Format: Journal Article
Language:English
Published: Milan River Publishers 01.01.2023
Subjects:
Automation
Cloning
Cloud computing
Data storage
Deep learning
Defects
Distributed processing
Natural language
Neural networks
Performance evaluation
Programming languages
Quality control
Semantics
Software
Software engineering
Software quality
Software reliability
Syntax
ISSN:1540-9589, 1544-5976
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Abstract Edge-cloud computing is a distributed computing infrastructure that brings computation and data storage with low latency closer to clients. As interest in edge-cloud systems grows, research on testing the systems has also been actively studied. However, as with traditional systems, the amount of resources for testing is always limited. Thus, we suggest a function-level just-in-time (JIT) software defect prediction (SDP) model based on a pre-trained model to address the limitation by prioritizing the limited testing resources for the defect-prone functions. The pre-trained model is a transformer-based deep learning model trained on a large corpus of code snippets, and the fine-tuned pre-trained model can provide the defect proneness for the changed functions at a commit level. We evaluate the performance of the three popular pre-trained models (i.e., CodeBERT, GraphCodeBERT, UniXCoder) on edge-cloud systems in within-project and cross-project environments. To the best of our knowledge, it is the first attempt to analyse the performance of the three pre-trained model-based SDP models for edge-cloud systems. As a result, we can confirm that UniXCoder showed the best performance among the three in the WPDP environment. However, we also confirm that additional research is necessary to apply the SDP models to the CPDP environment.
AbstractList Edge-cloud computing is a distributed computing infrastructure that brings computation and data storage with low latency closer to clients. As interest in edge-cloud systems grows, research on testing the systems has also been actively studied. However, as with traditional systems, the amount of resources for testing is always limited. Thus, we suggest a function-level just-in-time (JIT) software defect prediction (SDP) model based on a pre-trained model to address the limitation by prioritizing the limited testing resources for the defect-prone functions. The pre-trained model is a transformer-based deep learning model trained on a large corpus of code snippets, and the fine-tuned pre-trained model can provide the defect proneness for the changed functions at a commit level. We evaluate the performance of the three popular pre-trained models (i.e., CodeBERT, GraphCodeBERT, UniXCoder) on edge-cloud systems in within-project and cross-project environments. To the best of our knowledge, it is the first attempt to analyse the performance of the three pre-trained model-based SDP models for edge-cloud systems. As a result, we can confirm that UniXCoder showed the best performance among the three in the WPDP environment. However, we also confirm that additional research is necessary to apply the SDP models to the CPDP environment.
Author Kwon, Sunjae
Lee, Sungu
Baik, Jongmoon
Ryu, Duksan
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StartPage 255
SubjectTerms Automation
Cloning
Cloud computing
Data storage
Deep learning
Defects
Distributed processing
Natural language
Neural networks
Performance evaluation
Programming languages
Quality control
Semantics
Software
Software engineering
Software quality
Software reliability
Syntax
Title Pre-trained Model-based Software Defect Prediction for Edge-cloud Systems
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