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An empirical evaluation of defect prediction approaches in within-project and cross-project context
The software defect prediction approaches are evaluated, in within-project context only, with only a few other approaches, according to distinct...
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Cross-project defect prediction via semantic and syntactic encoding
Cross-Project Defect Prediction (CPDP) is a promising research field that focuses on detecting defects in projects with limited labeled data by...
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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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Improving transfer learning for software cross-project defect prediction
Software cross-project defect prediction (CPDP) makes use of cross-project (CP) data to overcome the lack of data necessary to train well-performing...
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Exploring the impact of data preprocessing techniques on composite classifier algorithms in cross-project defect prediction
Success in software projects is now an important challenge. The main focus of the engineering community is to predict software defects based on the...
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SMAN2: Soft-Max Multilayer Adversarial Neural Network-Based Cross-Project Software Defect Prediction
Cross-project software defect prediction (CPSDP) is an excessive way to enhance test performance and ensure software reliability. The CPSDP allows...
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FENSE: A feature-based ensemble modeling approach to cross-project just-in-time defect prediction
Context:Just-in-time defect prediction (JITDP) leverages modern machine learning models to predict the defect-proneness of commits. Such models...
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An Evaluation of Cross-Project Defect Prediction Approaches on Cross-Personalized Defect Prediction
Context: Just-in-time software defect prediction (JIT SDP) helps to prioritize fault-prone commits for efficient software quality assurance. As each...
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An effective approach to improve the performance of eCPDP (early cross-project defect prediction) via data-transformation and parameter optimization
Cross-project defect prediction (CPDP) utilizes other finished projects (i.e., source project) data to predict defects of the current working...
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Effort-aware cross-project just-in-time defect prediction framework for mobile apps
As the boom of mobile devices, Android mobile apps play an irreplaceable roles in people’s daily life, which have the characteristics of frequent...
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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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Exploring better alternatives to size metrics for explainable software defect prediction
Delivering reliable software under the constraint of limited time and budget is a significant challenge. Recent progress in software defect...
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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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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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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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Hybrid Defect Prediction Model Based on Counterfactual Feature Optimization
Software defect prediction is critical to ensuring software quality. Researchers have worked on building various defect prediction models to improve...
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Lifecycle-Based Software Defect Prediction Technology
In order to improve the efficiency and quality of software testing, aiming at various factors affecting software reliability, how to find defective...
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An investigation of online and offline learning models for online Just-in-Time Software Defect Prediction
Just-in-Time Software Defect Prediction (JIT-SDP) operates in an online scenario where additional training data is received over time. Existing...
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LineFlowDP: A Deep Learning-Based Two-Phase Approach for Line-Level Defect Prediction
Software defect prediction plays a key role in guiding resource allocation for software testing. However, previous defect prediction studies still...
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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...
