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Academic Experts
Dr. Asmita Yadav

Biography

Dr. Asmita Yadav is an Assistant Professor (Senior Grade) in the Department of Computer Science & Engineering and Information Technology, with over 16 years of teaching experience. She earned her Ph.D. in Computer Science and Engineering from Jaypee Institute of Information Technology, Noida, in June 2020. Her doctoral research, titled "Metadata-Based Efficient Approaches for Triaging Software Bugs", focused on advancing techniques in Software Repository Mining.

She holds an M.Tech in Computer Science and Engineering from BIT, Mesra (2014) and an MCA from MIET, Meerut (2008). Her research interests include Software Repository Mining, Mining Data Streams, and Machine Learning.

She has academic expertise in Software Repository Mining, Mining Data Streams, and Machine Learning. She has taught core subjects such as Design and Analysis of Algorithms, Automata Theory, Data Structures, Big Data, and Data Science. Backed by a strong research foundation, her work involves active participation in academic conferences, collaborative research, curriculum development, and student mentoring—fostering innovation and practical learning.

Educational Qualifications

Educational Qualifications: Ph.D. in CSE & IT (Jaypee Institute of Information Technology, Noida, 2020) – Domain: “Metadata Based Efficient Approaches for Triaging Software Bugs”, M.Tech in Computer Science & Engineering (BIT, Mesra, 2014), MCA (MIET, Meerut, 2008)

Research Highlights

Dr. Asmita Yadav’s research explores the intersection of software repository mining, machine learning, and data analytics to address critical challenges in software engineering, particularly in the areas of bug triaging and resolution. Her work involves mining software repositories—such as GitHub, Bugzilla, and JIRA—to extract rich metadata and historical information about past bug reports, developer contributions, resolution times, and module dependencies.

Additionally, her research emphasizes data-driven decision-making in software maintenance and highlights the potential of predictive analytics in reducing technical debt. The broader impact of her work lies in its applicability to both open and closed source projects, making it a valuable contribution to sustainable and intelligent software development.

Areas of Interest
  • Open Source Software Bug Repository
  • Machine Learning and Soft Computing Models
  • Feature Engineering and Clustering in Complex Datasets
  • Neural Networks and NLP for Software Artifacts
  • Integrated AI Techniques for Data-Driven Insights
Publications
  • Yadav, M. Baljon, S. Mishra, S. K. Singh, S. Saxena, and S. K. Sharma, “Developer Load Balancing Bug Triager: Developed Load Balance,” Expert Systems, May 2022.
  • A. Yadav and S. K. Singh, “An Automated Time-Based Multi-Criteria Bug Triage Approach: A Developer Working Efficiency and Social Network Based Developer Recommendation,” Journal of Shanghai Jiao Tong University (Science), May 2022.
  • A. Yadav, S. K. Singh, and J. S. Suri, “Ranking of Software Developers based on Expertise Score for Bug Triaging,” Information and Software Technology, vol. 112, pp. 1–17, 2019.
  • A. Yadav and S. K. Singh, “A novel and improved developer rank algorithm for bug assignment,” International Journal of Intelligent Systems Technologies and Applications, vol. 19, no. 1, pp. 78–101, 2020.
  • A. Yadav and S. K. Singh, “An Information-Theoretic Approach for Bug Triaging,” in Proc. 8th Int. Conf. on Cloud Computing, Data Science & Engineering (Confluence), 2018.