An Empirical Study on Software Defect Prediction Using CodeBERT Model
Applied Sciences, 2021 Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks.
Cong Pan, Minyan Lu, Biao Xu
doaj +3 more sources
A Survey on Transfer Learning for Cross-Project Defect Prediction
IEEE AccessSoftware defect prediction involves predicting which components in a software program, like classes or functions, are likely to have defects, based on metrics that describe those components.
Bruno Sotto-Mayor, Meir Kalech
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Interpretable Software Defect Prediction from Project Effort and Static Code Metrics
ComputersSoftware defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to ...
Susmita Haldar, Luiz Fernando Capretz
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Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
Future Internet, 2021 Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data.
Yu Zhao, Yi Zhu, Qiao Yu, Xiaoying Chen
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Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning. [PDF]
PeerJ Comput SciEffective software defect prediction is a crucial aspect of software quality assurance, enabling the identification of defective modules before the testing phase.
Ali M +5 more
europepmc +2 more sources
Bottlenecks in Software Defect Prediction Implementation in Industrial Projects
Foundations of Computing and Decision Sciences, 2015 Case studies focused on software defect prediction in real, industrial software development projects are extremely rare. We report on dedicated R&D project established in cooperation between Wroclaw University of Technology and one of the leading ...
Hryszko Jarosław, Madeyski Lech
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On the Use of Deep Learning in Software Defect Prediction [PDF]
Journal of Systems and Software, 2022 Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based approaches require manually extracted features, which are cumbersome, time consuming ...
G. Giray +4 more
semanticscholar +1 more source
Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model. [PDF]
Sci RepSoftware defect prediction aims to find a reliable method for predicting defects in a particular software project and assisting software engineers in allocating limited resources to release high-quality software products.
Abdu A +6 more
europepmc +2 more sources
Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review. [PDF]
Sensors (Basel), 2022 Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules ...
Jorayeva M +3 more
europepmc +2 more sources
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction [PDF]
IEEE Working Conference on Mining Software Repositories, 2021 A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-introducing. Recently, CC2Vec—a deep learning approach for Just-In-Time defect prediction—has been proposed.
Chanathip Pornprasit +1 more
semanticscholar +1 more source