An Effective Early Software Reliability Prediction Procedure for Process Oriented Development at Prototype Level Employing Artificial Neural Networks

Abstract

Reliability of a software product should be tracked during the software lifecycle right from the architectural phase to its operational phase. Heterogeneous systems consist of several globally distributed components, thus rendering their reliability evaluation more complex with respect to the conventional methods. In this context, reliability prediction of software process oriented systems assumes prime importance. It is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate risks and increase the reliability of systems while building distributed based applications. This paper presents an algorithm using feed-forward neural network for early qualitative software reliability prediction. The inputs for neural networks consist of technocomplexity, practitioner's level, creation effort, size and leakage defects. The number of defects detected in each cycle can be predicted by using Artificial Neural Networks (ANN). Illustrative examples prove the effectiveness of the methodology employed.

Financial Risk Assessment using Machine Learning Engineering (FRAME): Scenario based Quantitative Analysis under Uncertainty

“…Quantitative Risk Assessment (QRA) [23][24][25][26][27][28][29][30][31][32][33][34][35] is a methodology for evaluating the risks associated with complex real-time business processes and applications through a systematic Int J Auto AI Mach Learn, Vol 3, Issue 1, December 2023 and comprehensive approach. The purpose of this section is to provide a detailed analysis of the modeling requirements for QRA, with a focus on critical factors that must be taken into account in order to ensure accurate and reliable results.…”

A new fuzzy rule based algorithm for estimating software faults in early phase of development

“…Incidentally very few works are available in this area (Jiang et al (2007); Kim et al (2013); Mohan et al (2011);Yadav et al 2012Yadav et al , 2014Mohanta et al 2010;Cheung et al 2008;Pandey andGoyal 2009, 2010;Kumar and Misra 2008;Smidts et al 1998;Tripathi and Mall 2005;Fenton et al 2008;Xie et al 1999) compared to the SRGMs based on failure data gathered during testing phase. Among these models some are based on fuzzy logic (Yadav et al 2012(Yadav et al , 2014Pandey andGoyal 2009, 2010;Kumar and Misra 2008).…”