A Systematic Literature Review On Fault Prediction Performance In Software Engineering

A Systematic Literature Review On Fault Prediction Performance In Software Engineering-67
Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.Important User Information: Remote access to EBSCO's databases is permitted to patrons of subscribing institutions accessing from remote locations for personal, non-commercial use.

Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.Important User Information: Remote access to EBSCO's databases is permitted to patrons of subscribing institutions accessing from remote locations for personal, non-commercial use.

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Systematic literature review is defined as a process of identifying, assessing, and interpreting all available research evidence with the purpose to provide answers for specific research questions. A systematic and comprehensive investigation of methods to build and evaluate fault prediction models. Expert Systems with Applications, 34(3), 2091–2101.

Analysis of the selected primary studies revealed that current software defect prediction research focuses on five topics and trends: estimation, association, classification, clustering and dataset analysis.

Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm. 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’06), 39–46.

A Unified Framework for Defect Data Analysis Using the MBR Technique.

Eng degrees in Computer Science respectively from Saitama University, Japan, and Ph. Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. A Novel PCA-BP Fuzzy Neural Network Model for Software Defect Prediction.

He is a lecturer at the Faculty of Computer Science, Dian Nuswantoro University, Indonesia. In addition, 64.79% of the research studies used public datasets and 35.21% of the research studies used private datasets. Nineteen different methods have been applied to predict software defects. From the nineteen methods, seven most applied methods in software defect prediction are identified. Researchers proposed some techniques for improving the accuracy of machine learning classifier for software defect prediction by ensembling some machine learning methods, by using boosting algorithm, by adding feature selection and by using parameter optimization for some classifiers. Based on the defined inclusion and exclusion criteria, 71 software defect prediction studies published between January 2000 and December 2013 were remained and selected to be investigated further. An ant colony optimization algorithm to improve software quality prediction models: Case of class stability. International Journal of Software Engineering and Its Applications, 6(4). IEEE Transactions on Knowledge and Data Engineering, 24(6), 1146–1150. This literature review has been undertaken as a systematic literature review. Information and Software Technology, 53(4), 388–393. Bibi, S., Tsoumakas, G., Stamelos, I., & Vlahavas, I. Regression via Classification applied on software defect estimation. Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software.Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models.Professional member of the ACM, PMI and IEEE Computer Society. Recent studies of software defect prediction typically produce datasets, methods and frameworks which allow software engineers to focus on development activities in terms of defect-prone code, thereby improving software quality and making better use of resources.

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