Manju Bala

Sangeeta, Kapil Sharma, Manju Bala

Software reliability models are one of the most generally used mathematical tool for estimation of reliability, failure rate and number of remaining faults in the software. Existing software reliability models are designed to follow waterfall software development life cycle process. These existing models do not take advantage of iterative software development process. In this paper, a new failure rate model centered on iterative software development life cycle process has been developed. It aims to integrate a new modulation factor for incorporating varying needs in each phase of iterative software development process. It comprises imperfect debugging with the possibility of fault introduction and removal of multiple faults in an interval as iterative development of the software proceeds. The proposed model has been validated on twelve iterations of Eclipse software failure dataset and nine iterations of Java Development toolkit (JDT) software failure dataset. Parameter estimation for the proposed model has been done by hybrid Particle Swarm Optimization and Gravitational Search Algorithm. Experimental results in-terms of goodness-of-fit shows that proposed model has outperformed Jelinski Moranda, Shick Wolverton, Goel Okummotto Imperfect debugging, GS Mahapatra, Modified Shick Wolverton in 83.33 % of iterations for eclipse dataset and 77.77% of iterations for JDT dataset.

Ashish Kumar Tripathi, Kapil Sharma, Manju Bala

Over the last three decades more then sixty meta-heuristic algorithms have been proposed by the various authors. Such algorithms are inspired from physical phenomena, animal behavior or evolutionary concepts. These algorithms have been widely used for solving the various real world optimization problems. Researchers are continuously working to improve the existing algorithms and also proposing new algorithms that are giving competitive results as compared to the existing algorithms present in the literature. In this paper a novel meta heuristic algorithm based on military dogs squad is introduced. The proposed algorithm mimics the searching capability of the trained military dogs. Military dogs have strong smell senses by which they are able to search the suspicious objects like bombs, wildlife scats, currency, or blood as well as they can communicate with each other by their barking. The performance of the proposed algorithm is tested on 17 benchmark functions and compared with five other meta-heuristics namely particle swarm optimization (PSO), multiverse optimizer (MVO), genetic algorithm (GA), probability based learning (PBIL) and evolutionary strategy (ES). The results are validated in terms of mean and standard deviation of the fitness value. The convergence behavior and consistency of the results have been also validated by plotting convergence graphs and BoxPlots. Further the, proposed algorithm is successfully utilized to solve the real world fake review detection problem. The experimental results demonstrate that the proposed algorithm outperforms the other considered algorithms on the majority of performance parameters.