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Mining software repositories for comprehensible software fault prediction models
Journal of Systems and Software, 2008Software managers are routinely confronted with software projects that contain errors or inconsistencies and exceed budget and time limits. By mining software repositories with comprehensible data mining techniques, predictive models can be induced that offer software managers the insights they need to tackle these quality and budgeting problems in an ...
Vandecruys, Olivier +5 more
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Software faults prediction using multiple classifiers
2011 3rd International Conference on Computer Research and Development, 2011In recent years, the use of machine learning algorithms (classifiers) has proven to be of great value in solving a variety of problems in software engineering including software faults prediction. This paper extends the idea of predicting software faults by using an ensemble of classifiers which has been shown to improve classification performance in ...
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Important Issues in Software Fault Prediction
2014Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it.
Golnoush Abaei, Ali Selamat
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Variance Analysis in Software Fault Prediction Models
2009 20th International Symposium on Software Reliability Engineering, 2009Software fault prediction models play an important role in softwarequality assurance. They identify software subsystems (modules,components, classes, or files) which are likely to contain faults.These subsystems, in turn, receive additional resources forverification and validation activities.
Yue Jiang +3 more
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Software Fault Prediction Using Deep Learning Algorithms
International Journal of Open Source Software and Processes, 2019Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area.
Osama Al Qasem, Mohammed Akour
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A study on software fault prediction techniques
Artificial Intelligence Review, 2017Software fault prediction aims to identify fault-prone software modules by using some underlying properties of the software project before the actual testing process begins.
Santosh S. Rathore, Sandeep Kumar
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A clustering algorithm for software fault prediction
2010 International Conference on Computer and Communication Technology (ICCCT), 2010Software metrics are used for predicting whether modules of software project are faulty or fault free. Timely prediction of faults especially accuracy or computation faults improve software quality and hence its reliability. As we can apply various distance measures on traditional K-means clustering algorithm to predict faulty or fault free modules ...
Deepinder Kaur +3 more
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An empirical approach for software fault prediction
2010 5th International Conference on Industrial and Information Systems, 2010Measuring software quality in terms of fault proneness of data can help the tomorrow's programmers to predict the fault prone areas in the projects before development. Knowing the faulty areas early from previous developed projects can be used to allocate experienced professionals for development of fault prone modules.
Arashdeep Kaur +2 more
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Software Fault Prediction Using Cross-Validation
2020Software faults are dangerous. Software systems are often essential to a business operation or organization, and failures in such systems cause disruption of some goal-directed activity (mission critical). Faults in safety-critical systems may result in death, loss of property, or environmental harm.
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Comprehensive model for software fault prediction
2017 International Conference on Inventive Computing and Informatics (ICICI), 2017Software Fault prediction (SFP) is an important task in the fields of software engineering to develop a cost effective software. Most of the software fault prediction is performed on same project date i.e., training and testing with same projects fault data.
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