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A Clustering Approach Towards Cross-Project Technical Debt Forecasting
Technical debt (TD) describes quality compromises that can yield short-term benefits but may negatively affect the quality of software products in...
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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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Cross project defect prediction for open source software
Software defect prediction is the process of identification of defects early in the life cycle so as to optimize the testing resources and reduce...
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Yet Another Model! A Study on Model’s Similarities for Defect and Code Smells
Software defect and code smell prediction help developers identify problems in the code and fix them before they degrade the quality or the user...
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Autoclassify Software Defects Using Orthogonal Defect Classification
Software systems have become an integral part of all the organizations. These systems are performing many critical operations. A defect in these...
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Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics
Community smells are sub-optimal developer community structures that hinder productivity. Prior studies performed smell prediction and provided...
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Cost-sensitive Dictionary Learning for Software Defect Prediction
In recent years, software defect prediction has been recognized as a cost-sensitive learning problem. To deal with the unequal misclassification...
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Self-Transfer Learning Network for Multicolor Fabric Defect Detection
This paper presented a self-transfer learning network (STLN) for multicolor fabric defect detection. Deep neural networks were adopted to detect...
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On the assessment of software defect prediction models via ROC curves
Software defect prediction models are classifiers often built by setting a threshold t on a defect proneness model, i.e., a scoring function. For...
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A graph-based two-stage classification network for mobile screen defect inspection
Defect inspection, also known as defect detection, is significant in mobile screen quality control. There are some challenging issues brought by the...
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Joint attention mechanism with dynamic kernel for yolov5 mobile wireless charging coil surface defect identification
The yolov5-CTD (you only look once version five-carafe triplet double attention vision transformer) coil defect detection algorithm is proposed to...
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Software Fault Prediction Using Deep Neural Networks
Software failure prediction is the process of building models that software interpreters can use to detect faulty constructs early in the software...
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Cross-project bug type prediction based on transfer learning
The prediction of bug types provides useful insights into the software maintenance process. It can improve the efficiency of software testing and...
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When less is more: on the value of “co-training” for semi-supervised software defect predictors
Labeling a module defective or non-defective is an expensive task. Hence, there are often limits on how much-labeled data is available for training....
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Software fault prediction with imbalanced datasets using SMOTE-Tomek sampling technique and Genetic Algorithm models
Over the years, there has been a considerable discussion regarding machine learning (ML) techniques to forecast software faults. It can be...
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SS-WDRN: sparrow search optimization-based weighted dual recurrent network for software fault prediction
Predicting software faults at the primary stage is a challenging role for software engineers and tech industries. During the development of software...
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Software fault prediction using deep learning techniques
Software fault prediction (SFP) techniques identify faults at the early stages of the software development life cycle (SDLC). We find machine...
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Software defect prediction model based on LASSO–SVM
A software defect report is a bug in the software system that developers and users submit to the software defect library during software development...
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A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill
Width-related defects are a common occurrence in the Hot Strip Mill process which can lead to extra processing, concessions, or scrap**. The...
