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Adversarial domain adaptation for cross-project defect prediction
Cross-Project Defect Prediction (CPDP) is an attractive topic for locating defects in projects with little labeled data (target projects) by using...
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MHCPDP: multi-source heterogeneous cross-project defect prediction via multi-source transfer learning and autoencoder
Heterogeneous cross-project defect prediction (HCPDP) is aimed at building a defect prediction model for the target project by reusing datasets from...
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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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An extended study on applicability and performance of homogeneous cross-project defect prediction approaches under homogeneous cross-company effort estimation situation
Software effort estimation (SEE) models have been studied for decades. One of serious but typical situations for data-oriented models is the...
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Software defect prediction: future directions and challenges
Software defect prediction is one of the most popular research topics in software engineering. The objective of defect prediction is to identify...
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Development of Homogenous Cross-Project Defect Prediction Model Using Artificial Neural Network
Defect prediction is an extremely new software quality assurance study field. A project team’s goal is to provide a high-quality product with no or...
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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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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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Is deep learning good enough for software defect prediction?
Due to high impact of internet technology and rapid change in software systems, it has been a tough challenge for us to detect software defects with...
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Deep Adversarial Learning Based Heterogeneous Defect Prediction
Cross-project defect prediction (CPDP) is a hot study that predicts defects in the new project by utilizing the model trained on the data from other...
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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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Heterogeneous Cross Project Defect Prediction – A Survey
In the testing phase of Software Development Life Cycle (SDLC), Software Defect Prediction (SDP) is one of the pivotal task which finds the modules...
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Performance evaluation of software defect prediction with NASA dataset using machine learning techniques
The software industry’s growth and increasing complexity have made software maintenance more challenging, with Software Defects (SD) being a...
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Heterogeneous transfer learning: recent developments, applications, and challenges
Transfer learning (TL) has emerged as a promising area of research in machine learning (ML) due to its ability to enhance learning efficiency and...
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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...
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A Defect Heterogeneous Risk Assessment Method with Misclassification Cost
Existing software defect prediction techniques do not pay enough attention to the different cost impacts caused by misclassification and cannot...
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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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A novel approach for software defect prediction using CNN and GRU based on SMOTE Tomek method
Software defect prediction (SDP) plays a vital role in enhancing the quality of software projects and reducing maintenance-based risks through the...
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Handling class overlap and imbalance using overlap driven under-sampling with balanced random forest in software defect prediction
Various techniques in machine learning have been used for building software defect prediction (SDP) models to identify the defective software...
