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A Taxonomy of Metrics for Software Fault Prediction
2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2020Researchers in the field of Software Fault Prediction (SFP) make use of software metrics to build predictive models, for example, by means of machine learning and statistical techniques. The number of metrics used for SFP has increased dramatically in the last few decades.
Maria Caulo, Giuseppe Scanniello
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Software fault prediction tool
Proceedings of the 19th international symposium on Software testing and analysis, 2010We have developed an interactive tool that predicts fault likelihood for the individual files of successive releases of large, long-lived, multi-developer software systems. Predictions are the result of a two-stage process: first, the extraction of current and historical properties of the system, and second, application of a negative binomial ...
Thomas J. Ostrand, Elaine J. Weyuker
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On the Automation of Software Fault Prediction
Testing: Academic & Industrial Conference - Practice And Research Techniques (TAIC PART'06), 2006This paper discusses the issues involved in building a practical automated tool to predict the incidence of software faults in future releases of a large software system. The possibility of creating such a tool is based on the authorsÂ’ experience in analyzing the fault history of several large industrial software projects, and constructing statistical ...
Thomas J. Ostrand, Elaine J. Weyuker
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A sequential ensemble model for software fault prediction
Innovations in Systems and Software Engineering, 2021Unlike several other engineering disciplines, software engineering lacks well-defined research strategies. However, with the exponential rise in automation, the demand for software has observed an enormous elevation. Simultaneously, it necessitates having zero failures in the software modules to maximize the availability and optimize the maintenance ...
Monika Mangla +2 more
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A practical method for the software fault-prediction
2007 IEEE International Conference on Information Reuse and Integration, 2007In the paper, a novel machine learning method, SimBoost, is proposed to handle the software fault-prediction problem when highly skewed datasets are used. Although the method, proved by empirical results, can make the datasets much more balanced, the accuracy of the prediction is still not satisfactory.
Zhan Li, Marek Z. Reformat
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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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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 ...
Viet-Anh Phan +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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A Study on Software Metric Selection for Software Fault Prediction
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019For most software systems, superfluous software metrics are often collected. Sometimes, metrics that are collected may be redundant or irrelevant to fault prediction results. Feature (software metric) selection helps separating relevant software metrics from irrelevant or redundant ones, thereby identifying the small set of software metrics that are ...
Huanjing Wang, Taghi M. Khoshgoftaar
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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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