Current Position: Assistant Professor
Qualification: MCA
Teaching Experience: 4 Years
Courses offered: Computer Networks, Data Communication, Machine Learning, and Artificial Intelligence.
Current research interests:
Automated Software Engineering (ASE) represents a burgeoning field of research at the intersection of computer science and software engineering, offering profound implications for the efficiency, reliability, and scalability of modern software development. As the complexity of software systems continues to escalate, researchers are increasingly exploring innovative approaches to automate various facets of the software engineering lifecycle. This PhD research delves into the theoretical underpinnings and practical applications of ASE, aiming to advance the state-of-the-art in automated code generation, model-driven development, testing, debugging, and other critical aspects of software engineering. The study seeks to unravel the complexities associated with incorporating artificial intelligence, machine learning, and natural language processing into ASE tools, fostering a deeper understanding of how these technologies can synergistically enhance the software development process. By addressing challenges related to scalability, adaptability, and the integration of human-centric elements, this research endeavours to provide novel insights that not only contribute to the academic discourse but also yield tangible advancements in industry practices. Ultimately, the envisaged outcome of this research is to empower software engineers with cutting-edge automated tools and methodologies, fostering a paradigm shift towards more efficient, reliable, and agile software development practices.