Sushant Kumar Pandey

1. EDUCATION 

   • Ph.D, 2017 to 2021, from Indian Institute of Technology (Banaras Hindu University), Varanasi, India, with 8.4 coursework grade. Thesis title "Observations on Software Defect Prediction."

   • Mater of Technology (M.Tech), 2014 to 2014, from National Institute of Technology, Patna, Bihar, India, in Information Technology with 8.21 CGPA, Thesis title "Intrusion Detection system using Anomaly detection technique in Wireless Sensor Network."

   • Bachelor of Technology (B. Tech) in Information Technology (IT), 2008 to 2012 from United college of Engineering and research, Greater Noida, Uttar Pradesh Technical University (UPTU), India.

   • Intermediate and High school from CBSE, New Delhi, India. 

2. Experience

   • Teaching assistant at Dept. of CSE, NIT Patna, India, 2014 to 2016.

   • Research scholar and teaching assistant at IIT(BHU), Varanasi, India, 2017 to 2021. 

   • Post PhD researcher at  IIT(BHU), Varanasi, India, June 2021 to Feb 2022. 

   • Postdoctoral researcher at Dept. of CSE Chalmers | University of Gothenburg, Sweden, March 2022 to currently. 

3. Publications

JOURNALS 

3. • • “DNNAttention: A deep neural network and attention based architecture for cross project defect  number prediction”, Sushant Kumar Pandey et al. Knowledge Based Systems, Elsevier, and impact factor 8.8, Science citation  indexed (SCI), 2021.

     •  “An empirical study toward dealing with noise and class imbalance issues in software defect  prediction”,  Sushant Kumar Pandey et al. Soft Computing, Springer, and impact factor 4.1 Science Citation Index Expanded  (SCIE), 2021. 

     •  “BCV-Predictor: A bug count vector predictor of a successive version of the software system”, Sushant Kumar Pandey et al.  Knowledge Based Systems, Elsevier, and impact factor 8.8, Science citation indexed (SCI), 2020.

     •  “Software Defect Prediction using K-PCA and various Kernel based Extreme Learning Machine: An  Empirical Study”, Sushant Kumar Pandey et al. IET Software, impact factor: 1.6, Science citation indexed expanded (SCIE), 2020.

     • “BPDET: An effective software bug prediction model using deep representation and ensemble learning  techniques”, Sushant Kumar Pandey et al. Expert system with Applications, Elsevier, impact factor: 8.5, Science citation indexed  expanded (SCIE), 2019.

     •  “Machine Learning Based Methods for Software Fault Prediction: A Survey”, Sushant Kumar Pandey et al. Expert system with  Applications, Elsevier, impact factor: 8.5, Science citation indexed expanded, 2020.

     • “Design and performance analysis of various feature selection methods for anomaly-based techniques  in intrusion detection system”, Sushant Kumar Pandey.  Security and Privacy, Wiley, and impact factor: 1.9, 2019. 

     •  “Data Handling for Assuring Production Quality of Image Intensive Autonomous Drive Systems: An Industrial Case Study”, Journal of Software Engineering for Autonomous Systems, Sushant Kumar Pandey et al. Athena 2023. (Accepted)

     • “Is Deep learning good enough for software defect prediction?,” Innovations in Systems and Software Engineering (ISSE), Sushant Kumar Pandey et al.  Springer, 2023. (Accepted).

     •  “An anomaly detection technique-based intrusion detection system for wireless sensor network”,  Sushant Kumar Pandey. Inderscience, Scopus, 2019.

     •  “DNNT-CBVP: Deep Neural Network and Transfer learning based Cross-project Bug count Vector  Prediction,” Neurocomputing, Elsevier, impact factor 5.77, 2023 (Second revision submitted).

     •  “DBDNN-Estimator: A Cross-Project Number of Fault Estimation Technique”, in SN Computer Science, Springer, 2023. (Revision submitted)

CONFERENCES 

3. • • • “Design Patterns Understanding and Use in the Automotive Industry: An Interview Study”, Sushant Kumar Pandey et al. International Conference on Product-Focused Software Process Improvement conference 2023, (PROFES’23) Springer, Dornbirn, Austria. (Core rank B). (Accepted)

       • “TransDPR: Design Pattern Recognition Using Programming Language Models”, Sushant Kumar Pandey et al. (IEEE, ESEM-ESEIW – 2023, Orleans, Louisiana, United States), (Core rank A). (Accepted)

       • “Cross-Project setting using Deep learning Architectures in Just-In-Time Software Fault Prediction: An Investigation”, Sushant Kumar Pandey et al. (IEEE, AST [co located-ICSE (Core rank A*)] – 2023, Melbourne, Australia), (In press).

       • “Comparing Word-based and AST-based Models for Design Pattern Recognition”, Sivajeet Chand, Sushant Kumar Pandey et al. ” Proceedings of the 19th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE). 2023., co-located with ESEC/FSE 2023, San Francisco, California, United States (core rank A*)), (Accepted).

       • “Defect Backlog Size Prediction for Open-Source Projects with the Autoregressive Moving Average and Exponential Smoothing Models”, Paulina Aniola, Sushant Kumar Pandey et al. IEEE 18th Conference on Computer Science and Intelligence Systems FedCSIS 2023, Warsaw, Poland.  (core rank B), (Accepted).

       • Mosin, Vasilii, et al. "Comparing Input Prioritization Techniques for Testing Deep Learning Algorithms."  2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2022. (Core rank B)

       • “Class Imbalance Issue in Software Defect Prediction Models by various Machine Learning  Techniques: An Empirical Study”, 2021, IEEE, 8th International Conference on Smart  Computing and Communications (ICSCC), Kerala, India

       • “Software bug prediction prototype using Bayesian network classifier: A comprehensive  model”. International Conference on Computational Intelligence and Data Science (ICCIDS  2018), Elsevier, Scopus indexed. Gurugram India.

       • “Intrusion detection system using anomaly technique in wireless sensor network”,  International Conference on Computing, Communication and Automation (ICCCA 2016),  Scopus indexed. Greater Noida India.

4. Language and tools 

   • Programming language: C, Python, Java, and Matlab. 

   •  Machine learning and deep learning libraries in Python. 

   •  WEKA, and Stastica machine learning tools. 

5. ACHIEVEMENTS 

   •  Course certificate of Structuring Machine Learning Projects from Coursera (2018).

   • Course certificate of Neural Networks and Deep Learning from Coursera (2018). 

   • Course certificate of Neural Convolutional Neural Networks from Coursera (2019). 

   • Course certificate Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization from Coursera (2019). 

   • Course certificate Sequence Models from Coursera (2019). 

   •  Course certificate Improving Machine learning from Coursera (2020). 

   • ETHICAL HACKING & SECURITY certification  from APPINE TECHNOLOGY LAB Noida, India