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Class level fault prediction using software clustering
2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2013Defect prediction approaches use software metrics and fault data to learn which software properties associate with faults in classes. Existing techniques predict fault-prone classes in the same release (intra) or in a subsequent releases (inter) of a subject software system.
Giuseppe Scanniello +4 more
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Software fault prediction using firefly algorithm
International Journal of Intelligent Engineering Informatics, 2018The software fault prediction (SFP) literature has shown an immense growth of the research studies involving the artificial neural network (ANN) based fault prediction models. However, the default gradient descent back propagation neural networks (BPNNs) have a high risk of getting stuck in the local minima of the search space.
Ishani Arora, Anju Saha
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Journal of Systems and Software, 1995
Abstract Cost-effective and timely software development methods are essential today as software costs and backlogs escalate while applications are developed in rapidly changing environments. Focusing testing efforts on those portions of the code with the largest number of faults can reduce development costs and time, but requires prediction of the ...
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Abstract Cost-effective and timely software development methods are essential today as software costs and backlogs escalate while applications are developed in rapidly changing environments. Focusing testing efforts on those portions of the code with the largest number of faults can reduce development costs and time, but requires prediction of the ...
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Software Fault Prediction Process
2018Accurate detection and early removal of software faults during the software development can reduce the overall cost of software development and can result in the improved software quality product. These inherent advantages of software fault prediction have attracted many researchers to focus on the software fault prediction.
Sandeep Kumar, Santosh Singh Rathore
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Software Fault Prediction Using Random Forests
2020In this paper, we present a software fault prediction model using random forests. Software fault prediction identifies the faulty regions in a software product early in its lifecycle and hence improves the quality attributes such as reliability of the software. Random forest is an ensemble learning method for classification. Random forests contain many
Kulamala Vinod Kumar +3 more
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Types of Software Fault Prediction
2018A large number of researchers have presented various fault prediction studies to predict the fault-proneness of the given software system. These fault prediction studies reported the results in term of different–different contexts. Depending upon the context of the results, a fault prediction model can classify a software module into faulty or non ...
Sandeep Kumar, Santosh Singh Rathore
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K-means Clusteringfor Software faults prediction
Computing Trendz - The Journal of Emerging Trends in Information Technology, 2016The occurrence of defects in the software system has become prominent problem in development process of software. A software fault refers to wrong transition within software that causes the product to act in an unintended manner. Faults are the root causes of software failures.
Meenu Singla, Bhavtosh Mishra
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Transfer Learning for Predicting Software Faults
2019 11th International Conference on Knowledge and Systems Engineering (KSE), 2019This paper investigates a transfer learning application for predicting software faults. Detecting faulty modules in software projects is challenging due to two main issues 1) the low quality of existing handcrafted features leads to the bad performance of traditional learning models and 2) the shortage of annotated data hinders applying deep neural ...
Anh Phan Viet +2 more
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Predicting Faults in High Assurance Software
2010 IEEE 12th International Symposium on High Assurance Systems Engineering, 2010Reducing the number of latent software defects is a development goal that is particularly applicable to high assurance software systems. For such systems, the software measurement and defect data is highly skewed toward the not-fault-prone program modules, i.e., the number of fault-prone modules is relatively very small.
Naeem Seliya +2 more
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Software Fault Prediction Based on Fault Probability and Impact
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019Nowadays, software tests prioritization is a crucial task. Indeed, testing exhaustively the whole software system can be very difficult, heavily time and resources consuming. Using machine learning algorithms to predict which parts of a software system are fault-prone can help testers to focus on high-risk parts of the code and improve resources ...
Salim Moudache, Mourad Badri
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