Modelling Fault Detection Using SRGM in Agile Environment and Ranking of Models

Abhishek Srivastava (Amity School of Engineering and Technology, Amity University, New Delhi, India), P. K. Kapur (Amity Centre for Interdisciplinary Research, Amity University, Noida, India), Deepti Mehrotra (Amity School of Engineering and Technology, New Delhi, India) and Rana Majumdar (Amity School of Engineering and Technology, New Delhi, India)
Copyright: © 2019 |Pages: 20
EISBN13: 9781522597018|DOI: 10.4018/JCIT.2019040101
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Today's software applications deployed in an enterprise to cater to the complex business processes, integrate various business units and address requirements of a global customer base. The traditional methodology of software engineering succumbs to the changing need of customer and technology advancement. On the behest of the customer, a software system should be designed in a way that it goes in concert with the present user needs. Agile methodology targets complex systems with its iterative, incremental, and evolutionary approach. There are numerous factors attributing towards the successful implementation of agile methodology. This led to adopting an approach of agile based on ‘lean' principles over the traditional software development life cycle (SDLC) approach. Collaborative work is done with the project team on a priority list. The implementation is done through “SCRUM” an empirical framework for learning. It has multiple sprints which are deliverable products. This idea has substantially reduced the ‘time to market' as the customer can decide which features of the software they would like to be delivered on a priority basis. To model trends of fault detection in each sprint, a growth model of software reliability is used. This research article presents a framework to analyze and measure the cumulative errors in an Agile Testing Process, the authors have applied modeling on various SRGMs to prove acceptability in an agile development process and finally compares these models using the Mahalanobis Distance Formula for Model ranking. The Mahalanobis distance criteria is easy to compute and that can be utilized to get the ranks and select the best model in view of an arrangement of contributing criteria.
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