دانلود کتاب راهنمای پردازش زبان طبیعی برای مهندسی نیازمندی ها

Requirements are both the cornerstone and the pain point of software engineer- ing. They define the functionalities and qualities that the software must exhibit to satisfy stakeholder needs, yet their incorrect analysis and management can lead to costly errors and delays in the development lifecycle. Moreover, require- ments can become challenging to handle in large projects due to their tendency to change, proliferation in number, diversification in terms of abstraction levels and stakeholders’ perspectives considered. Last but not least, requirements are notoriously expressed in natural language, a medium subject to multiple inter- pretations. This inherent ambiguity does not align well with the precision and rigour required in software development. Requirements Engineering (RE) aims to tackle these issues systematically, in- corporating tasks such as defect detection, traceability, classification and more. However, performing these tasks manually can be error-prone, as it demands meticulous attention to detail and is sometimes tedious since RE activities can often become repetitive. For these reasons, requirements engineers have long hoped for automated partners to assist them in addressing the challenges asso- ciated with natural language requirements, and relieve their daily frustrations. Natural Language Processing (NLP) is a field of artificial intelligence focus- ing on developing algorithms and models to process, interpret and understand natural language data. In order to support RE tasks with automated tools and facilitate the work of requirements engineers, researchers have experimented with NLP techniques for over 20 years, tailoring existing approaches to the specific needs of the RE domain. The interest in NLP techniques for RE has recently witnessed significant growth, also driven by the advancements in NLP technolo- gies, which have become increasingly accessible to researchers and even to the general public, with tools that perform complex tasks such as machine transla- tion and question answering with near-human levels of performance. We do not mention ChatGPT because this name may soon become obsolete given the pace of evolution of the NLP field. This Handbook on Natural Language Processing for Requirements Engineer- ing aims to provide a comprehensive guide on how NLP can be leveraged to enhance various aspects of RE, leading the reader from the exploration of fun- damental concepts and techniques to the practical implementation of NLP for RE solutions in real-world scenarios. The motivation for this book is threefold. Firstly, the field of NLP for RE has accumulated a vast body of knowledge over the years, yet this knowledge is dispersed across various publications and venues, making it challenging for inter- ested individuals to efficiently obtain a comprehensive overview. This book aims to consolidate this knowledge into a single, accessible source, providing readers with a clear roadmap through the complexities of integrating NLP with RE. Secondly, NLP is currently experiencing a transformative period, mainly driven by advancements in Large Language Models (LLMs). These models present un- precedented opportunities to tackle RE challenges with enhanced effectiveness. By clarifying how NLP can support RE and showcasing state-of-the-art tech- niques and methodologies, this book serves as a gateway for researchers and practitioners to leverage these advancements in practical applications. Lastly, throughout our career, we have witnessed firsthand the struggles practitioners face in handling natural language requirements and the potential of NLP to alle- viate these challenges. We thus aim to help them understand how they can apply NLP to enhance their RE process and, ultimately, the quality of their systems.