Performance Analysis of Proprietary and Non-Proprietary Tools for Regression Testing Using Genetic Algorithm

The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Quantitative Evaluation of Frameworks for Web Applications

An empirical study of web applications that use software frameworks is presented here. The analysis is based on two approaches. In the first, developers using such frameworks are required, based on their experience, to assign weights to parameters such as database connection. In the second approach, a performance testing tool, OpenSTA, is used to compute start time and other such measures. From such an analysis, it is concluded that open source software is superior to proprietary software. The motivation behind this research is to examine ways in which a quantitative assessment can be made of software in general and frameworks in particular. Concepts such as metrics and architectural styles are discussed along with previously published research.