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Cross project defect prediction: a comprehensive survey with its SWOT analysis
Software fault prediction (SFP) refers to the process of identifying (or predicting) faulty modules based on its characteristics/software metrics....
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CodeBERT Based Software Defect Prediction for Edge-Cloud Systems
Edge-cloud system is a crucial computing infrastructure for the innovations of modern society. In addition, the high interest in the edge-cloud...
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Research on Cross-Project Software Defect Prediction Based on Machine Learning
In recent years, machine learning technology has developed vigorously. The research on software defect prediction in the field of software...
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A software defect prediction method with metric compensation based on feature selection and transfer learning
Cross-project software defect prediction solves the problem of insufficient training data for traditional defect prediction, and overcomes the...
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Data sampling and kernel manifold discriminant alignment for mixed-project heterogeneous defect prediction
Heterogeneous defect prediction (HDP) refers to identifying more likely defect-proneness of software modules in a target project using heterogeneous...
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A Three-Level Training Data Filter for Cross-project Defect Prediction
The purpose of cross-project defect prediction is to predict whether there are defects in this project module by using a prediction model trained by...
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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction
Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced...
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An empirical study of data sampling techniques for just-in-time software defect prediction
Just-in-time software defect prediction (JIT-SDP) is a fine-grained, easy-to-trace, and practical method. Unfortunately, JIT-SDP usually suffers from...
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Implicit and explicit mixture of experts models for software defect prediction
Accurately predicting defects in software modules helps the developers and testers to find the defective modules quickly and save their efforts in...
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Just-in-time defect prediction for mobile applications: using shallow or deep learning?
Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of...
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Hybrid deep architecture for software defect prediction with improved feature set
The software Defect Prediction (SDP) model uses previously learned data to predict whether a future example (such as a file, class, or module) will...
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DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique
Cross-project fault prediction (CPFP) uses data sets from projects to predict faulty/non-faulty modules. Cross-project fault number estimation...
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Enhancing Security and Performance of Software Defect Prediction Models: A Literature Review
There have recently been many advances in software defect prediction (SDP). Just-in-time defect prediction (JIT), heterogeneous defect prediction...
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Outlier Mining Techniques for Software Defect Prediction
Using software metrics as a method of quantification of software, various approaches were proposed for locating defect-prone source code units within...
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A comparative study of software defect binomial classification prediction models based on machine learning
As information technology continues to advance, software applications are becoming increasingly critical. However, the growing size and complexity of...
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On the time-based conclusion stability of cross-project defect prediction models
Researchers in empirical software engineering often make claims based on observable data such as defect reports. Unfortunately, in many cases, these...
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Software Defect Prediction Survey Introducing Innovations with Multiple Techniques
The software is applied in various areas, so that the quality of the software is very important. The software defect prediction (SDP) is used to...
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Software defect prediction using a bidirectional LSTM network combined with oversampling techniques
Software defects are a critical issue in software development that can lead to system failures and cause significant financial losses. Predicting...
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Exploring the relationship between performance metrics and cost saving potential of defect prediction models
Context:Performance metrics are a core component of the evaluation of any machine learning model and used to compare models and estimate their...
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Software-defect prediction within and across projects based on improved self-organizing data mining
This paper proposes a new method for software-defect prediction based on self-organizing data mining; this method can establish a causal relationship...
