基于不确定性动作模型学习理论的软件需求获取方法

目前,软件系统已逐渐成为日常生活中不可缺少的组成部分。利用人工智能的方法进行软件需求获取,可以在短时间内自动获取软件需求,有利于避免人为的理解偏差以及节省人力时间成本。为了解决软件需求的自动获取问题,利用智能规划与机器学习的方法,将需求领域转化为部分规划域,并建立了具有不确定性效果的动作模型学习算法AMLCP。应用该算法,可以获得完整规划域以及需求规格说明。

Action Models Learning Algorithm with Indeterminate Effects for Software Requirement Specification

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 analysing requirements formally, rapidly and automatically, avoiding mistakes made by misunderstanding between engineers and users, and saving lots of time and manpower. For exacting software requirement specification automatically, we applied intelligent planning and machine learning methods to convert software reduirement into an incomplete planning domain, and proposed an algo- rithm AMI_CP to learn action models with indeterminate effects. Furthermore, we obtained a complete planning domain by applying this algorithm and converted it into software requirement specification.