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Integration of artificial intelligence activities in software development processes and measuring effectiveness of integration

Recently, the modelling of whole process of software (SW) development is performed using extended waterfall and agile models. The further advancement of extended waterfall and agile models in the main phases like communication, planning, modelling, construction and deployment can improve the overall quality of the product. Accordingly, in this study, artificial intelligence (AI) activities are integrated into SW development processes. The important AI activities like intelligent agents, machine learning (ML), knowledge representation, statistical model, probabilistic methods, and fuzzy are integrated into the extended waterfall model. Again, AI activities like intelligent decision making, ML, Turing test, search and optimisation are integrated into the agile model. Two metrics such as, Usability Goals Achievement Metric and Index of Integration are evaluated in five independent SW projects. Once SW projects are developed using these models, feedback queries have been collected formally and the collected data are extensively analysed to identify the individual characteristics of products, identifying correlation behaviour of products with respect to model and metrics.