Ontology-Assisted and Autonomic Testing Verified Model for Automated and Reliable Web Development

Abstract

This paper proposed a web engineering architecture based on three main phenomena called Structured Development, high reliability and multi-level flexibility. The ontology is formed and mapped to generate a structured and user-assisted design. The user requirements are captured in this model based on an ontology map. Web engineering is applied to capture the various elements of web development including the functional features. In the development phase, the captured inputs and features are processed for designing the static and active web pages. The functional interconnectivity, validations, and security are integrated for all pages. The second important aspect of high reliability is achieved by managing the control level validations, BlackBox testing, cookies control, and session tracking. Multi-level flexibility is achieved at each level of this model including requirement gathering, ontology formation, webpage development, control validations, and testing. The XML files are generated at different levels to manage and control user responses. The proposed model is verified on seven case studies and the analysis is done in terms of design time and the number of cycles required for finalizing the website. The qualitative evaluation of the proposed model is done against 11 existing web designing tools. The project development is accomplished from 15 to 70 s based on the project size. The number of change cycles of different projects lies between 4 and 12. The analytical study identified that the proposed model is effective and reliable enough against template support, requirement gathering, customization, control validation, testing, and security.

Keywords:

• Automated system
• Requirement gathering
• Testing
• Web autonomic system
• Web development

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

DATA AVAILABILITY STATEMENT

The open source projects that are used in this research are cited properly. Some results are derived from earlier research and these are also cited properly.

Additional information

Notes on contributors

Kapil Juneja

Kapil Juneja is a post-doctoral fellow in the Department of Computer Science & Engineering at the Bennett University, Greater Noida, India. He completed PhD in computer science and engineering from the MD University, Rohtak, Haryana, India. His research interests include image processing, biometrics, pattern recognition, computer networks, software engineering, and machine learning. He is currently doing research on image processing, network security, and data classification applications. He has published over 40 research papers. He has also been a reviewer for IEEE, Springer, and Elsevier Publishers.

Vipul Kumar Mishra

Vipul Kumar Mishra is an associate professor in the Department of Computer Science & Engineering at Bennett University, Greater Noida, India. He has done PhD from Indian Institute of Technology, Indore, MP, India in 2015. He was a post-doctoral scholar at University of Kentucky, USA in 2015–2016. His research interests include machine learning, deep learning, neural network optimization and design space exploration. He is currently working to make deep learning model faster, smaller, and power-efficient. He is also working in applied deep learning in computer vision. E-mail: [email protected].

Dhiraj Khurana

Dhiraj Khurana completed his PhD in computer science and engineering from Maharshi Dayanand University, Rohtak, Haryana, India, MTech in computer science and engineering from Guru Jambheshwar University of science and Technology, Hisar, Haryana, India and BTech in computer science and engineering from Maharshi Dayanand University, Rohtak, Haryana, India in the year 2021, 2007 and 2004 respectively, where he is working as an assistant professor in Department of Computer Science and Engineering. His areas of interest include recommender system and machine learning. E-mail: [email protected].