JIIT

Academic Experts

Prof. Sandeep Kumar Singh

DEAN ACADEMICS

sandeepk.singh@jiit.ac.in

Biography

Dr. Sandeep Kumar Singh is a dynamic academic leader with over 22 years of progressive experience in higher education, research, academic governance, and institutional strategy. He currently serves as Professor and Dean (Academics – UG) at JIIT (Main Campus), where he has spearheaded several transformative initiatives that have strengthened academic quality and institutional excellence.

He has played a pivotal role in driving NAAC and NBA accreditations, NEP 2020–aligned curriculum reforms, Outcome-Based Education (OBE) implementation, and digital transformation projects. He led the launch of the BCA 2+2 Dual Degree Program under the CSU–JIIT collaboration. His leadership in SEO optimization, website modernization, and ICICI payment gateway integration has significantly enhanced the university’s digital ecosystem and operational efficiency.

A prolific researcher, Dr. Singh has authored 79 research publications (including 12 SCIE and 9 ESCI indexed papers), 7 international book chapters, and filed a patent on developer load-sensitive bug triaging. His research has received 539 citations (Google Scholar) and 648 citations (Scopus). He has supervised 8 Ph.D. theses and 22 M.Tech dissertations, and serves as a reviewer for Pearson Education and McGraw Hill.

With a leadership philosophy grounded in faculty empowerment, quality assurance, research excellence, and global benchmarking, Dr. Sandeep Kumar Singh brings visionary thinking and operational rigor to advance the mission of higher education institutions.

Research Highlights

Dr. Sandeep Kumar Singh has made significant contributions to the fields of Software Engineering, Mining Software Repositories, Search-Based Software Engineering, Recommender Systems, and Healthcare Data Analytics. His primary research focuses on Requirements Engineering, Software Fault Prediction, Bug Triaging, and Code Quality Assessment, with a strong orientation toward applying AI/ML techniques to solve complex software engineering challenges.

He has authored 79 research publications, including 12 SCIE and 9 ESCI indexed papers, along with 7 international book chapters, and filed a patent on developer load-sensitive and expertise-based software bug triaging. His publications are featured in reputed journals such as Automated Software Engineering, Information and Software Technology, Expert Systems, and Journal of King Saud University – Computer and Information Sciences. His work has received 539 citations (Google Scholar) and 648 citations (Scopus), reflecting its scholarly impact.

Dr. Singh’s research has advanced cross-project defect prediction, feature selection for software quality improvement, reinforcement learning for cold-start problems in bug triaging, and hybrid approaches for code smell detection. He has supervised 8 Ph.D. theses and 22 M.Tech dissertations, building a strong research pipeline in emerging areas.

He has actively disseminated his work through numerous international conferences including IC3, Confluence, and IEEE forums, and serves as a reviewer for Pearson Education and McGraw Hill. His research blends methodological rigor with real-world applicability, contributing to the evolution of intelligent, data-driven software engineering systems.

Areas Of Interest

Mining Software Repositories
Data Mining and Healthcare
Software Code Quality
Recommender Systems in Software Engineering
Software Fault Prediction
Search based Software Engineering
Social Media Analytics
Sustainable Green Computing and Green Software Engineering.

Publications

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 & Scopus Indexed, Impact Factor: 2.3, H-index: 51)
👉 Pioneers the use of contextual multi-armed bandits to solve the cold-start problem in bug triaging.
Khatri, Y., & Singh, S. K. Towards Building a Pragmatic Cross-Project Defect Prediction Model combining Non-effort based and Effort based Performance Measures for a Balanced Evaluation. Information and Software Technology.
(SCIE & Scopus Indexed, Impact Factor: 2.73, H-index: 103)
👉 Proposes a comprehensive defect prediction model enhancing software quality evaluation.
Gupta, R., & Singh, S. K. (2021). A Novel Metric based Detection of Temporary Field Code Smell and its Empirical Analysis. Journal of King Saud University – Computer and Information Sciences.
(SCIE & Scopus Indexed, Impact Factor: 13.47)
👉 Introduces a novel metric for detecting code smells, contributing to code quality assurance.
Yadav, A., & Singh, S. K. (2019). Ranking of software developers based on expertise score for bug triaging. Information and Software Technology, 112, 1–17.
(SCIE & Scopus Indexed, Impact Factor: 2.9, H5-index: 81)
👉 Develops an expertise-based ranking model improving efficiency in large-scale bug repositories.
Srivastava, S. K., Singh, S. K., & Suri, J. S. (2019). Effect of Incremental Feature Enrichment on Healthcare Text Classification System: A Machine Learning Paradigm. Computer Methods and Programs in Biomedicine, 172, 35–51.
(SCIE & Scopus Indexed, Impact Factor: 3.42, H-index: 45)
👉 Advances text classification in healthcare using incremental feature enrichment strategies.