Quality of Service Prediction Using Fuzzy Logic and Rup Implementation for Process Oriented Development

Abstract

In a competitive business landscape, large organizations such as insurance companies and banks are under high pressure to innovate, improvise and differentiate their products and services while continuing to reduce the time-to market for new product introductions. Generating a single view of the customer is critical 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 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 the 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 9001 and 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. Early qualitative software reliability prediction is done using fuzzy expert systems, using which the expected number of defects in the software prior to the experimental testing is obtained. The predicted results are then compared with the practical values obtained during the actual testing procedure.

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.…”

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

“…The data collected from the defect consolidation log for a typical module is shown in Table 1. 8 Out of the fifteen modules used in the study for each phase, 9 modules are used for training, and 6 modules are used for testing. The input features are normalized in the range of 0.0 to 1.0 for all the features.…”

“…In the unit testing and IST testing phases, practitioner's level is has more relevance compared to the other parameters. A model for qualitative prediction of number of defects in software for process oriented development at prototype level using fuzzy logic has been originally proposed by the authors of this paper 8 (Krishna Mohan et al, 2008). A fuzzy logic based maturity rating for software performance prediction has been proposed by Verma et al 11 The MMRE values obtained from the fuzzy logic based method and ANN based method shown in Table 3 and are represented graphically in Fig.…”

Early Quantitative Software Reliability Prediction Using Petri-nets

“…A natural extension of the adoption of RUP would be its inclusion to the offshore development to reduce the total cost of ownership (TCO) with improved reliability that comes with higher productivity. However, with the comprehensive nature of RUP comes the significant complexity regarding the process steps and the types of artefacts produced at each step [2]. Thus, this research paper is intended to cover the elements of a RUP-based development process that are vital to successful development projects.…”