Rakesh Garg

Selection of Optimal Software Reliability Growth Models Using a Distance Based Approach

A large number of software reliability growth models (SRGMs) have been proposed during the past 30 years to estimate software reliability measures such as the number of remaining faults, software failure rate, and software reliability. Selection of an optimal SRGM for use in a particular case has been an area of interest for researchers in the field of software reliability. Tools and techniques for software reliability model selection found in the literature cannot be used with high confidence as they use a limited number of model selection criteria. For the first time, we developed a deterministic quantitative model based on a distance based approach (DBA) method, then applied it for evaluation, optimal selection, and ranking of SRGMs. DBA recognizes the need for relative importance of criteria for a given application, without which inter-criterion comparison could not be accomplished. It requires a set of model selection criteria, along with a set of SRGMs, and their level of criteria for optimal selection; and it successfully presents the results in terms of a merit value which is used to rank the SRGMs. We use two distinct, real data sets for demonstration of the DBA method. The result of this study will be a ranking of SRGMs based on the Euclidean composite distance of each alternative to the designated optimal SRGM.

E-learning website evaluation and selection using multi-attribute decision making matrix methodology

E‐learning website selection is a deliberate decision with a significant influence on the educational sector. E‐learning website selection decision is based on multiple attributes/criteria. In the present research, the various attributes having a huge influence in the evaluation process of E‐learning websites that are termed collectively as ranking criteria were identified and extracted from the available literature at the first step. Now, the relative importance or the significance of each ranking criteria was determined by collecting data through the interviews with experts and applying some aggregate operations on that data. Simply, the priority weight of each ranking criteria was determined at the second step. The third step is concerned with the evaluation work of the alternatives against the identified ranking criteria by considering the priority weights determined at the second step. In the present study, E‐learning websites selection problem is resolved using matrix method which is capable to solve such types of multiple‐attribute decision making (MADM) problems. Further, the results obtained from the proposed methodology are compared with the existing approaches namely Weighted Distance Based Approximation and Technique for Order Preference by Similarity to Ideal Solution to validate the applicability and utility of the proposed method.