Mutation Testing is a white-box, unit testing technique widely used for the software quality assurance. This technique although powerful, but is computationally expensive and this expense has barred mutation testing from becoming a popular software testing technique. However the recent engineering advancements have provided us with a number of ways for reducing the cost of mutation testing. There are a number of factors that are making mutation testing an expensive technique, one is high computational cost involved due to execution of large number of generated mutants, second being the huge amount of human effort involved for checking the output of mutant program with original one and for manually detecting the equivalent mutants. In this paper we have tried to closely analyze the available techniques for reducing the number of mutants so that we can come up with an efficient mutant reduction technique to reduce the high computational cost involved in mutation testing.


This paper deals with application of Total Quality Management (TQM) in respect to library infrastructure, library services, library collection, library personnel, library users and awareness of TQM in a University Library. TQM is a management approach focused on user with quality based on the library functions and services aiming at long term success. The main aim is achieved through user satisfaction and benefits of all members of the institution and society. It is an approach adopted by the libraries to reduce the operating expenses by application of the principles of TQM.

An Efficient Artificial intelligence for knowledge management using Artificial Bee Colony (ABC) algorithm and Genetic Algorithm

In Artificial bee colony, there are 3 types of bees: Employed, Onlooker and Scout. Employed foragers, they are associated with a particular food source which they are currently exploiting or are “employed” at. They carry with them information about this particular source, its distance and direction from the nest, the profitability of the source and share this information with a certain probability. Unemployed foragers, they are continually at look out for a food source to exploit. GA is AI methodology that is inspired by the evolution theory of Darwin. Initially, Holland developed a methodology for GA that consists of a sequence of steps which are followed to move from one generation to another. In this research paper we solve problem for travelling sales man and we have extend the travelling salesman problem through genetic algorithm and ABC. Artificial bee colony algorithm gives the solution of cheap cost and best quality and Genetic algorithm gives fittest value for problem through crossover and mutation.