Automated Regression Test Case Generation And Prioritization For Web Applications To Reduce The Testing Cost

Abstract

Testing is one of the most important processes of any product or software development. Continuous changes in the software due to modification in customer s specifications, correction in defects, or impact of environment changes forces the testing team to focus more on regression testing. There is a dire need for complete solution and automation of generating test cases directly based on user requirements, and subsequently prioritizing them. These test cases have to be generated and implemented by the tool so that the reduction in the overall effort and cost can be achieved. In the present times, test case prioritization technique has gained a lot of recognition in regression testing because of its ability to focus on only those test cases which are imperative as per the user specification. In spite of various techniques evolving every day, the task to identify the exact prioritization technique suitable for a particular application still remains a challenge. Our study and research is based on exploring various methods and methodologies for regression test case generation and prioritization. The effective requirements of the customer play an important role in an efficient generation of regression test cases. With the advancement of agile technology, the industry demands for effective and useful specifications from the customer so as to decrease the efforts, time and cost of software development. The storyboard is an effective tool to cater customer requirements in an efficient manner. Our proposed framework and tool provides a platform where customer and business analyst may sit together and use the tool to provide required functionalities and their parameter. Generation of test cases requires more efforts when performed manually. Increase in efforts and lack of coverage may be one of the causes of generating test cases manually. The work is carried out to generate test cases using storyboard generated for the user. The generated storyboard is processed using Natural Language Processing and Decision tr