1. S. Shukla and S. Kumar, “Self-adaptive ensemble-based approach for software effort estimation,” in Proceedings of the 30th International Conference on Software Analysis, Evolution and Reengineering (SANER), Macao SAR, China, 2023, pp. 581–592.
2. S. Shukla and S. Kumar, “Towards automated prediction of software bugs from textual description,” in Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE), Prague, Czech Republic, 2023, pp. 193–201.
3. S. Shukla and S. Kumar, “A stacking ensemble-based approach for software effort estimation,” in Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE), Virtual Event, 2021, pp. 205–212.
4. S. Shukla and S. Kumar, “An extreme learning machine-based approach for software effort estimation,” in Proceedings of the 16th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE), Virtual Event, 2021, pp. 47–57.
5. S. Shukla and S. Kumar, “Applicability of neural network-based models for software effort estimation,” in Proceedings of the IEEE World Congress on Services (SERVICES), Milan, Italy, 2019, pp. 339–342.
6. S. Shukla, S. Kumar, and P. R. Bal, “Analyzing effect of ensemble models on multi-layer perceptron network for software effort estimation,” in Proceedings of the IEEE World Congress on Services (SERVICES), Milan, Italy, 2019, pp. 386–387.
7. R. K. Behera, S. Shukla, S. K. Rath, and S. Misra, “Software reliability assessment using machine learning technique,” in Computational Science and Its Applications–ICCSA 2018: 18th International Conference, Melbourne, VIC, Australia, July 2-5, 2018, Proceedings, Part V 18, Melbourne, VIC, Australia, 2018, pp. 403–411.
Book Chapters
1. S. Shukla, R. K. Behera, S. Misra, and S. K. Rath, “Software reliability assessment using deep learning technique,” in Towards Extensible and Adaptable Methods in Computing, Springer, 2018, pp. 57–68.
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