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Requirements Engineering: Bridging Theory, Research and Practice

Submission Deadline: 31 May 2024 Submit to Special Issue

Guest Editors

Dr. Affan Yasin, Northwestern Polytechnical University, China
Dr. Javed Ali Khan, University of Hertfordshire, UK
Dr. Ziqi Wei, Chinese Academy of Sciences, China
Prof. Shijun Liu, Shandong University, China

Summary

Software engineering involves creating digital systems, and a crucial part of this process is requirements engineering. Requirements engineering focuses on understanding user needs and defining how a system should function. This ensures that the software we develop is not only valuable and functional but also user-friendly and enjoyable.

 

In today's rapidly evolving software landscape, we're constantly seeking fresh ideas. Agile and DevOps teams are introducing innovative strategies into requirements engineering to adapt swiftly and bring products to market faster. The emergence of artificial intelligence, particularly machine learning, presents both opportunities and challenges. Future systems must also address ethical and societal concerns like sustainability, human values, and gender-related issues, as they influence how these systems impact society and the environment.

 

Requirements engineering (RE) is a human-centered process seamlessly integrated into systems and software engineering. It aids our understanding of complex systems throughout their lifecycle through tasks such as gathering, analyzing, defining, documenting, validating, and managing requirements. Neglecting these initial RE tasks can lead to problems, as extensively discussed in academic literature.

 

In the realm of requirements engineering, we must consider how people perceive their environment, interact with systems, and are influenced by societal dynamics. To achieve this, insights from cognitive and social sciences are drawn upon to establish both theoretical foundations and practical methods for defining requirements. These insights come from diverse fields, including computer science, software engineering, psychology, anthropology, sociology, and linguistics.

 

Given these ongoing developments, it is imperative for the requirements engineering community to adopt a proactive approach. We must adapt current practices and rigorously assess the foundations and effectiveness of these novel approaches in RE. This proactive adaptation is essential to remain at the forefront of our ever-evolving field.


Keywords

• Software requirements methods and tools
• Software requirements application in industry
• Software requirements education
• Software requirements and Artificial Intelligence (AI)
• Functional and non-functional requirements
• Model driven requirements engineering
• Formal methods for the early software development stages
• User-centered software development
• Requirements engineering for sustainability
• Ethical and societal concerns in software requirements
• Requirements in Agile development
• Requirements in DevOps (processes)
• Security and Privacy challenges in context of requirements engineering
• Interdisciplinary studies
• New and Emerging Ideas

Published Papers


  • Open Access

    ARTICLE

    Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

    Qazi Mazhar ul Haq, Fahim Arif, Khursheed Aurangzeb, Noor ul Ain, Javed Ali Khan, Saddaf Rubab, Muhammad Shahid Anwar
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172
    (This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)
    Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature selection, which is used to… More >

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