首页|Predictive framework of software reliability analysis under multiple change points and imperfect debugging
Predictive framework of software reliability analysis under multiple change points and imperfect debugging
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NETL
NSTL
Springer Nature
Software often goes through multiple testing phases, which can lead to discovering hidden faults. We propose a software hazard rate model with an imperfect debugging framework incorporating Multiple Change Points (MCP). This approach aims to improve Software Reliability Growth Models, providing a more accurate representation of the real-world testing environment during the software development process. The proposed model has fewer parameters, which offers better fitting across various datasets while reducing complexity compared to existing models. Four distinct real-world datasets are used to assess goodness of fit, demonstrating its efficacy and producing a more efficient and general model. This study integrated an MCP into the Jelinski-Moranda model to develop a hazard rate approach. Our model predicts software reliability with greater precision compared to existing models. Akaike's Information Criterion, Root Mean Squared Error, Hazard Rate Approach, Absolute Error, Average Error, Multiple Determination Coefficient, and Relative Predictive Error depict favourable outcomes for the MCP model.
ReliabilityHazard functionImperfect debuggingFault generationError predictionMultiple change point
Nageswari N、Ansuman Mahapatra、G. S. Mahapatra
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Department of Computer Science and Engineering. National Institute of Technology, Puducherry, Puducherry, India
Department of Mathematics, National Institute of Technology, Puducherry, Puducherry, India
NETL
NSTL
Springer Nature
