Dr. Shweta Rani - ASSISTANT PROFESSOR (SENIOR GRADE) | JIIT Noida

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Dr. Shweta Rani

Biography

Dr. Shweta Rani is currently serving as an Assistant Professor (Senior Grade) in the Department of Computer Science & Engineering and Information Technology at Jaypee Institute of Information Technology (JIIT), Noida (Sector 62), Uttar Pradesh. She has over 4.5 years of academic experience and 3 years of industrial exposure, having worked as a Programmer on government projects for the National Informatics Centre (NIC) between 2010 and 2013.

She earned her Ph.D. in Information Technology from Guru Gobind Singh Indraprastha University (GGSIPU), Delhi, in January 2022. During her doctoral studies, she was awarded the prestigious Visvesvaraya Ph.D. Fellowship, funded by the Ministry of Electronics and Information Technology (MeitY), Government of India, for a period of five years.

Prior to joining JIIT, Dr. Shweta worked as an Assistant Professor at the KIET Group of Institutions, Ghaziabad. Throughout her academic career, she has taught various undergraduate and postgraduate courses, including Software Testing, Software Project Management, Programming in C and Python, Data Structures, Web Technologies, and Database Management Systems.

Her research interests span across Software Testing, Software Engineering, Search-Based Software Testing (SBST), Machine Learning, and Deep Learning. She has published multiple research papers in reputed international journals and conferences, contributing significantly to her areas of expertise. In addition to her academic publications, she holds two Indian patents, one of which has been granted.

Research Highlights

Dr. Shweta Rani’s research primarily focuses on Software Engineering, with a special emphasis on Search-Based Software Testing (SBST) and Mutation Testing. Her work in SBST explores the application of optimization techniques to automate and enhance software testing processes, aiming to improve test case generation and fault detection efficiency. She has extensively explored mutation analysis, a fault-based testing technique, to assess and improve the quality of test suites.

In addition to her contributions in software testing, Dr. Rani is actively involved in Natural Language Processing (NLP), specifically working on punctuation prediction using modern deep learning and transformer-based architectures. Her recent research investigates the application of BERT, LSTM, and Transfer Learning approaches to restore punctuation in transcribed or unstructured text. She is also exploring the potential of Large Language Models (LLMs) to further enhance prediction accuracy in low-resource and domain-specific scenarios.

Areas of Interest

Software Testing
Software Engineering
Search Based Software Testing
Machine Learning
Deep Learning

Publications

Shweta Rani, Bharti Suri, “Searching and Evolving Test Cases using Moth Flame Optimization for Mutation Testing”, International Journal of Intelligent Engineering Informatics, Inderscience, vol. 9(3), pp.276-293, 2021. (ESCI indexed)
Shweta Rani, Bharti Suri, “Mutation Based Test Generation using Search Based Social Group Optimization Approach," Evolutionary Intelligence, Springer, 2021. (ESCI indexed)
Shweta Rani, Bharti Suri, “Investigating Different Metrics for Evaluation and Selection of Mutation Operators for Java”, International Journal of Software Engineering and Knowledge Engineering, world scientific, vol. 31(3), pp. 311-336, 2021. (SCI-E indexed)
Shweta Rani, Bharti Suri and Rinkaj Goyal, “On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing”, Symmetry, vol. 11(9), 1145, September, 2019. (SCI-E indexed)
Nishtha Jatana, Bharti Suri and Shweta Rani, "Systematic Literature Review on Search Based Mutation Testing", e-Informatica Software Engineering Journal, vol. 11(1), pp. 59–76, 2017. (web of science ESCI)