Early Quantitative Software Reliability Prediction Using Petri-nets

Abstract

Abstract-In a competitive business landscape, large organizations such as insurance companies and banks are under high pressure to innovate, improvise and distinguish their products and services while continuing to reduce the time-to market for new product introductions. Generating a single view of the customer is vital from different perspectives of the systems developer over a period of time because of the existence of disconnected systems within an enterprise. Therefore, to increase revenues and cost optimization, it is important to build enterprise systems more closely with the business requirements by reusing the existing systems. While building distributed based applications, it is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate risks and increase the reliability of systems. Experiences in developing applications in Java Enterprise Edition (JEE) with customized RUP have been presented in this paper. RUP is adopted into an onsite-offshore development model along with ISO 9001and SEI CMM Level 5 standards. This paper provides an RUP approach to achieve increased reliability with higher productivity and lower defect density along with competitiveness through cost effective custom software solutions.Quantitative software reliability prediction is done using Generalized Stochastic Petri Nets, based on the RUP implemented prototype obtained from the PoC of a financial application prior to the actual implementation of the application development.

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“…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.…”

Reliability analysis of safety‐critical and control systems: a state‐of‐the‐art review

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