Framework for Determining Maturity Level of Test Organization Using RCNN

摘要

Testing is an important phase in software development. Many organizations have not fully realized and recognized their testing process that can effectively influence the improvement of their development process and doesn't have sufficient insight into the effectiveness of these testing process. If we don't consider these, it will lead to lack of maturity in a test organization and often leads to frequent conflicts between the business and IT. Improving the test process is essential for ensuring the quality of the system being tested. Many Maturity Models are available to find the maturity level of the test organization and suggest the improvements to their process areas, but they are focusing a limited set of testing process areas for determining that. The proposed system provides an effective approach to determine the maturity level of Test Organization by considering twenty four key process areas and seven vectors and provide test process improvement for achieving better maturity level. The framework has a set of questions, which will be answered by the testing staff working in the test organization. These answers will give the information about the current situations of the organization. Information collected from the testing staff will be evaluated by the NLP engine, which consists of Recurrent Convolutional Neural Network. RCNN is a deep neural network used to categorize the information.