Javaid Iqbal

Work place: Department of Computer Sciences, University of Kashmir, Srinagar, 190006, India



Research Interests: Mathematical Analysis, Software Engineering, Computational Science and Engineering


Javaid Iqbal received his B.Sc in Mathematics from the University of Kashmir, and Masters in Computer Applications from the same university in 2004. He completed his Ph.D from the University of Kashmir in 2014. His research interests are Software engineering, Software reliability engineering and reliability modeling, and mathematical modeling of dynamic systems. He is a member of ACM, CSI and IETE.

Author Articles
Analysis of Some Software Reliability Growth Models with Learning Effects

By Javaid Iqbal

DOI:, Pub. Date: 8 Jul. 2016

A newly developed software system before its deployment is subjected to vigorous testing so as to minimize the probability of occurrence of failure very soon. Software solutions for safety critical and mission-critical application areas need a much focused level of testing. The testing process is basically carried out to build confidence in the software for its use in real world applications. Thus, reliability of systems is always a matter of concern for us. As we keep on performing the error detection and correction process on our software, the reliability of the system grows. In order to model this growth in the system reliability, many formulations in Software Reliability Growth Models (SRGMs) have been proposed including some based on Non-Homogeneous Poisson Process (NHPP). The role of human learning and experiential pattern gains are being studied and incorporated in such models. The realistic assumptions about human learning behavior and experiential gains of new skill-sets for better detection and correction of faults on software are being incorporated and studied in such models. In this paper, a detailed analysis of some select SRGMs with learning effects is presented based on use of seven data sets. The estimation of parameters and comparative analysis based on goodness of fit using seven data sets are presented. Moreover, model comparisons on the basis of total defects predicted by the select models are also tabulated.

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