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

机译：人工智能活动在软件开发流程中的集成以及衡量集成的有效性

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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.
机译：最近，使用扩展的瀑布和敏捷模型对软件（SW）开发的整个过程进行了建模。在通信，计划，建模，构造和部署等主要阶段，扩展瀑布和敏捷模型的进一步发展可以提高产品的整体质量。因此，在这项研究中，人工智能（AI）活动被集成到软件开发过程中。重要的AI活动（如智能代理，机器学习（ML），知识表示，统计模型，概率方法和模糊）已集成到扩展瀑布模型中。再次，将诸如智能决策，机器学习，图灵测试，搜索和优化之类的AI活动集成到敏捷模型中。在五个独立的软件项目中评估了两个指标，例如可用性目标成就指标和集成指数。一旦使用这些模型开发了SW项目，便会正式收集反馈查询，并对收集到的数据进行广泛的分析，以识别产品的各个特征，从而确定产品在模型和指标方面的相关行为。

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