Artificial Intelligence and Recommender Systems in E-Commerce: Trends and Research Agenda

With advanced NLP, AI has transformed how software specifications and validation are made. Ambiguity, inconsistency, and inefficiency are the enemies of most traditional methods, causing delays and unneeded project expenses. These issues are resolved in AI by automatically extracting, validating, prioritizing, and classifying requirements from documents. NLP helps AI understand unstructured texts to simplify communication, avoid errors, and match requirements with business goals and regulatory standards. It further allows organizations to predict changes and optimize their resources. Integrating AI in DevOps and agile production environments reduces the software development lifecycle by providing real-time documentation updates, offering true automation of risk assessments, and even facilitating the process of negotiation of requirements through intelligent methods. Of course, AI chatbots and virtual assistants also help developers, project managers, and clients interact without any complications. Although requirements engineering with an AI can be challenging regarding data quality, interpretability, and ethics, and there are no good quality AI tools, AI continues to evolve, as with deep learning and contextual understanding, which will result in better requirements engineering. As AI becomes more integrated, businesses can develop high quality software faster and more accurately, increasing project success.