Software Requirement Acquision Method based on Probabilistic Action-Model Algorithm

Jie Gao, Han-kui Zhuo, and Lei Li


Automated planning, machine learning, software requirement, knowledge acquisition


Software systems are becoming an integral part of all walks of life. This aggravates the need for an artificial intelligent perspective for requirements engineering, which allows for modeling and analyzing requirements formally, rapidly and automatically, avoiding mistakes made by misunderstanding between engineers and users, and saving lots of time and manpower. To address this problem, we propose a process of acquiring requirements automatically, which adopts automated planning techniques and machine learning methods to convert software requirement into an incomplete planning domain, and propose an algorithm AMLCP 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.

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