![]() ![]() This paper demonstrates in-depth, comprehensive research into the current state, advantages, limitations and future scope of artificial intelligence in the domain of software engineering. This work elucidates the various techniques of intelligent computing that have been applied to these stages of software engineering, as well as the scope for some of these techniques to solve existing challenges and optimize SDLC processes. Software Development Lifecycle is the foundation of this paper, and each phase of it – Requirements Engineering, Design and Architecture, Development and Implementation, and Testing – serves as a building block. This paper is a study on the amalgamation of artificial intelligence with software engineering. ONLINE USE CASE DIAGRAM GENERATOR MANUALIn every sphere of technology nowadays, the world has been moving away from manual procedures towards more intelligent systems that minimize human error and intervention, and software engineering is no exception. We validate our findings using this extended dataset. In order to enable this step of our pipeline, we address the lack of available annotated data by labelling the widely used PURE requirements dataset on a word level by tagging classes and attributes within the texts. Finally, to prove the effectiveness of supervised machine learning models on requirements texts, a sequence labelling model is applied to the text specific for class modelling to distinguish classes and attributes in the running text. ![]() Secondly, we develop a rule-based bucketing method for selecting sentences into a range of ‘buckets’ for transformation into their corresponding UML models. Firstly, we implement entity-based extractive summarization of the raw text to enable highlighting certain parts of the requirements that are of interest to the modelling goal. To address this issue, we propose a pipeline for preprocessing such requirements documents at the conceptual level, for subsequent automatic generation of class, activity, and use case models in the Unified Modelling Language (UML) downstream. Such input texts do not reflect real-world requirements documents. Current approaches to natural language processing of requirements documents restrict their input to documents that are relevant to specific types of models only, such as domain- or process-focused models. ![]()
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