Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

Mutation testing has been used to evaluate the quality of the test set and provides the confidence in testing activity. Generation as well as running of mutants needs practice. A huge amount of mutants are generated and therefore, it is a very costly activity in terms of time and effort. Automatic mutant generation and execution is essential to support testing. Automation achieves more attention, saves time and effort as developers and testers use automated tools. In the recent years, researchers have found that practitioners have diminutive knowledge about tools and their effectiveness. This paper compares five well-known publicly accessible mutation testing tools: `MuClipse', `Judy', `Jumble', `Jester' and PIT. This comparison uses a collection of Java classes taken from various easily accessible sources. Tests were designed and generated with the help of test generation techniques. The performance of each mutation tool was analyzed and was compared based on their mutation operators, mutation score and execution time.

In the past few years, the need of prediction has been increased rapidly. Predicting the purchase, reading of news, buying food items are few popular interests of researchers and marketing people as well. Newspapers are quite essential to urge information concerning recent activity and general awareness for all age group. Varied solutions are being developed to convert paper News system to digital news and it has become a standard for all news company. Recommendation system has been proposed and developed for suggestion to the user. Researchers have developed many algorithms for developing the recommendation system. Each approach has its pros and cons. In this paper, we have studied different approaches for news recommendation systems and developed a content-based filtering model for recommending the news articles to the user. The articles with more euclidean similarity have higher chances of suggesting to the user.