An intelligent planning technique-based software requirement analysis

Abstract

With the rapid growth of internet applications, many institutes and organisations are planning to update their software by re-analysing requirements. However, many conventional techniques often fail to efficiently achieve this goal because of the complexity and gigantic scale of current software. Therefore, the need for intelligent software requirements engineering becomes significant, which allows for modelling and analysing requirements formally, rapidly and automatically, avoiding mistakes made by misunderstanding between engineers and users, and saving lots of time and manpower. In this paper, we propose an approach to acquiring requirements automatically, which adopts automated planning techniques and machine learning methods to convert software requirement into an incomplete planning domain. By this approach, we design an algorithm called intelligent planning-based requirement analysis IPRA, to learn action models with uncertain effects. Furthermore, we obtain a complete planning domain by applying this algorithm and convert it into software requirement specification.