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Showing 1 to 12 of 12 for “"defect prediction"”.
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Wafer defect prediction with statistical machine learning
… important to drive cost savings by screening out defective die upstream. The primary goal of the project is to build a statistical prediction model to facilitate operational improvements across two global manufacturing locations. The scope of the project includes one high-volume product line, an …
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Empirical Evaluation of Graph-Anonymized Metrics for JIT Defect Prediction
… like MORPH, LACE, and LACE2 provide privacy to defect prediction data. While effective, they often sacrifice metric performance due to a disregard for data relationships during anonymization. To preserve these relationships, we propose graph anonymization techniques. These methods maintain data …
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k-Label Dependent Evolutionary Distance Weighting for Software Defect Prediction
Software Defect Prediction is a field of study that uses machine learning algorithms to identify software that is susceptible to defects. Software defects and bugs can potentially cause errors that have significant consequences ranging from minor inconveniences to system failures. Identification of …
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A Wavelet-Based Rail Surface Defect Prediction and Detection Algorithm
Early detection of rail defects is necessary for preventing derailments and costly damage to the train and railway infrastructure. A rail surface flaw can quickly propagate from a small fracture to a broken rail after only a few train cars have passed over it. Rail defect detection is typically …
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Using Time Series Models for Defect Prediction in Software Release Planning
… time should be allowed for testing and fixing defects. Otherwise, there is a risk of slip in the development schedule and/or software quality. A time series model is used to predict the number of bugs created during development. The model depends on the previous numbers of bugs created. The …
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Enhancing Software Defect Prediction: Investigating Diverse Representations of Source Code as Feature Values in Classical and Quantum Machine Learning Approaches
Software defect prediction is one of the prominent topics within the software engineering research domain. Studies have been delved into predicting and addressing software bugs (also known as defects/ issues). Yet, the landscape remains marked by a steady influx of new reports filling bug …
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Change Request Prediction and Effort Estimation in an Evolving Software System
<p>Prediction of software defects has been the focus of many researchers in empirical software engineering and software maintenance because of its significance in providing quality estimates from the project management perspective for an evolving legacy system. Software Reliability Growth Models …
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Real-Time Thermography for In Operando Additive Manufacturing Defect Mitigation using a Machine Learning Approach
… continues to be confronted by process-induced defects, such as surface irregularities, geometric distortions, and porosity caused by multilayer effects and unoptimized parameters. The structural integrity and reliability of the printed components are compromised by these imperfections. Defect …
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Automated Bug Severity Prediction using Source Code Metrics, Static Analysis, and Code Representation
… significant research efforts are devoted to the prediction of software bugs. However, most existing work in this domain treats all bugs the same, which is not the case in practice. It is important for a defect prediction method to estimate the severity of the identified bugs so that the higher …
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Predicting solder defects in printed circuit board assembly (PCBA) process
… One such promising opportunity is using defect prediction to improve downstream yields. For instance, x-ray inspection, which mostly detects solder defects, has a yield of about 97% for one of Flex's automated PCBA lines, and an improvement even to just 98% would create significant cost …
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Leveraging Defects Life-Cycle for Labeling Defective Classes
… Specically,</p> <p>the identication of likely defect-prone les (i.e., classes in Object-Oriented systems)</p> <p>helps in prioritizing, testing, and analysis activities. This work focuses on automated</p> <p>methods for labeling a class in a version as defective or not. The most used …