My research has been driven by the mission of building robust, reliable, secure, and intelligent software systems. As a senior researcher in Software Reliability Engineering and Machine Learning for Artificial Intelligence, I have explored innovative approaches to address inconsistencies and uncertainties in real-world software behavior through advanced modeling techniques.
Over the past two decades, my work has focused extensively on Software Reliability Growth Models (SRGMs), particularly in designing adaptive and non-parametric models that improve predictive validity and practical applicability when compared with traditional approaches.
Inspired by advances in Machine Learning and Artificial Intelligence, I developed Artificial Neural Network (ANN)-based Software Reliability Growth Models that avoid rigid statistical assumptions and support improved decision-making in software release management. These models address several limitations associated with classical NHPP-based models, including failure clustering, learning effects, nonlinear fault behavior, and incomplete or noisy datasets.
My research interests include:
- Software Reliability Engineering
- Machine Learning for Artificial Intelligence
- Artificial Neural Networks for Reliability Prediction
- Secure Coding and Software Assurance
- Software Testing and Quality Engineering
- Reliability Modeling and Prediction
- Software Process Improvement
- AI Applications in Software Systems
Contributions and Impact
- Published 45+ international journal papers in reputed publishers including Elsevier, Springer, Hindawi, and InderScience
- Authored 10 technical books in programming, quality engineering, and software reliability
- Successfully guided 4 Ph.D. scholars in areas such as Artificial Intelligence, recommendation systems, and reliability modeling
- Introduced flexible ANN-based SRGMs with applications in both academia and industry
- Led funded national initiatives including MEITY’s “Soft QC/QM” project for Software Quality Management in India
- Developed static analysis models and methodologies for secure coding aligned with national cybersecurity objectives
- Delivered invited lectures, FDPs, and technical sessions in national and international forums
Vision
My continuing research vision is to integrate Artificial Intelligence, Software Reliability Engineering, and Secure Development Practices to build dependable software systems for critical and emerging applications.
Research Areas
Software Reliability Engineering
– Machine Learning for Artificial Intelligence
– Artificial Neural Networks
– Secure Coding
– Recommendation Systems
– Software Testing
– Static Analysis Tools
– Data Analytics
– Cyber Security
– Ontology-Based Opinion Mining
Current Research Focus
Current research focuses on Machine Learning inspired Software Reliability Growth Models (SRGMs), Recommendation Systems, Artificial Neural Network based modeling, Secure Coding methodologies, data preprocessing techniques, and Software Quality and Reliability Engineering.
Selected Journal Publications
- Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function – HINDAVI Mathematical Problems in Engineering, 2017.
- Improving Software Reliability Estimation using Multi-layer Neural-Network Combination Model – International Journal of Innovative Computing and Applications, 2017.
- A Software Reliability Growth Model Addressing Learning – Journal of Applied Statistics, 2008.
- Ontology and Context Based Recommendation System using Neuro-Fuzzy Classification – ELSEVIER Computers & Electrical Engineering, 2018.
- Enhancing the Security of C/C++ Programs using Static Analysis – Indian Journal of Science and Technology, 2016.
- Application of Artificial Neural Network for Software Reliability Growth Modeling – Indian Journal of Science and Technology, 2016.
Research Supervision
Guided PhD scholars and postgraduate researchers in Machine Learning, Recommendation Systems, Software Reliability Engineering, Cyber Security, and Big Data Analytics.
Academic Output
- 38 Journal Articles
- 27 Conference Papers
- 2 Book Chapters
- 7 Technical Books