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Academic Experts
Academic Experts
Neetu Singh
ASSISTANT PROFESSOR(GRADE I)
neetu.singh@mail.jiit.ac.in
Biography

Neetu Singh is a dedicated educator and researcher with over 17 years of academic and corporate training experience, with extensive research in areas like Mining Software Repositories, Recommender Systems, and Social Media Analytics. She shares a strong passion for cutting-edge research, having published work in reputed international Journals, and conferences and contributed to book chapters. She has actively organized and attended numerous National and International conferences and guided approximately 60+ postgraduate and graduate dissertation projects. In addition, she has collaborated with various universities across India, serving as an expert in the CS/IT domain.

Currently pursuing her Ph.D. at JIIT, Noida, her research interests span Mining Software Repositories, Artificial Intelligence, Machine Learning, Data Mining, Power BI, and Web Development.

As an educator, she is committed to inspire students to cultivate critical thinking and a passion for learning, while actively leading research and development efforts in advanced technologies.

Research Highlights

My research work centers on the Bug Cold Start Problem (BCSP) with a focus on reinforcement learning-based solutions. Over the course of my Ph.D., I have explored and developed several approaches including MABTriage, CMABTriage, Enhanced CMAB_Triage, and Optimized CMAB_Triage. These approaches have been designed and evaluated to address challenges in BCSP and contribute toward more effective bug triaging frameworks. The work has resulted in multiple publications in reputed journals and international conferences, along with a book chapter contribution. In addition, I have carried out a systematic literature review to establish a comprehensive understanding of existing solutions in the area. My research poster on smart triaging using reinforcement learning for BCSP was awarded first prize at Research and Innovation Day (2025). Overall, my research highlights reflect a consistent focus on advancing reinforcement learning applications in software engineering with specific emphasis on BCSP.

Areas Of Interest
  • Mining Software Repositories
  • Software Engineering
  • Machine Learning
  • Recommender systems
  • Reinforcement Learning
  • SLR based analysis
Publications
  1. Singh, N., & Singh, S. K. (2024). A systematic literature review of solutions for cold start problem. International Journal of System Assurance Engineering and Management, 15(7), 2818-2852. ESCI and Scopus Indexed journal “International Journal of System Assurance Engineering and Management,” Impact Factor:1.6 H-index: 45. https://doi.org/10.1007/s13198-024-02359-y
  2. Singh, N., & Singh, S. K. (2025). Navigating bug cold start with contextual multi-armed bandits: an enhanced approach to developer assignment in software bug repositories. Automated Software Engineering, 32(2), 39. SCIE and Scopus Indexed journal named “Automated Software Engineering” Impact Factor:2.3, H-index: 51. https://doi.org/10.1007/s10515-025-00508-6
  3. Singh, N., & Singh, S. K. (2025). Optimizing developer assignments in software bug repositories: An empirical study. Innovations in Systems and Software Engineering, [Under review]. ESCI and Scopus Indexed journal named Innovations in Systems and Software Engineering, Impact Factor: 1.1, H-index: 32. https://doi.org/10.1007/s11334-025-00612-6
  4. Singh, N., & Kumar Singh, S. (2021, August). MABTriage: multi armed bandit triaging model approach. In Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing (pp. 457-460). https://doi.org/10.1145/3474124.3474194, H-index:21
  5. Singh, N., & Singh, S. K. (2023, August). An Empirical Assessment of the Performance of Multi-Armed Bandits and Contextual Multi-Armed Bandits in Handling Cold-Start Bugs. In Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing (pp. 750-758). https://doi.org/10.1145/3607947.3608094, H-index:21