| A study on software fault prediction techniques SS Rathore, S Kumar Artificial Intelligence Review 51, 255-327, 2019 | 180 | 2019 |
| An empirical study of some software fault prediction techniques for the number of faults prediction SS Rathore, S Kumar Soft Computing 21, 7417-7434, 2017 | 126 | 2017 |
| A decision tree regression based approach for the number of software faults prediction SS Rathore, S Kumar ACM SIGSOFT Software Engineering Notes 41 (1), 1-6, 2016 | 123 | 2016 |
| Towards an ensemble based system for predicting the number of software faults SS Rathore, S Kumar Expert Systems with Applications 82, 357-382, 2017 | 112 | 2017 |
| Selecting requirement elicitation techniques for software projects S Tiwari, SS Rathore, A Gupta 2012 CSI Sixth International Conference on Software Engineering (CONSEG), 1-10, 2012 | 103 | 2012 |
| Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems SS Rathore, S Kumar Knowledge-Based Systems 119, 232-256, 2017 | 101 | 2017 |
| Open-world machine learning: applications, challenges, and opportunities J Parmar, S Chouhan, V Raychoudhury, S Rathore ACM Computing Surveys 55 (10), 1-37, 2023 | 73 | 2023 |
| A decision tree logic based recommendation system to select software fault prediction techniques SS Rathore, S Kumar Computing 99, 255-285, 2017 | 70 | 2017 |
| Predicting number of faults in software system using genetic programming SS Rathore, S Kumar Procedia Computer Science 62, 303-311, 2015 | 68 | 2015 |
| A comparative study of feature-ranking and feature-subset selection techniques for improved fault prediction SS Rathore, A Gupta Proceedings of the 7th India Software Engineering Conference, 1-10, 2014 | 59 | 2014 |
| An empirical study of ensemble techniques for software fault prediction SS Rathore, S Kumar Applied Intelligence 51, 3615-3644, 2021 | 45 | 2021 |
| A methodology for the selection of requirement elicitation techniques S Tiwari, SS Rathore arXiv preprint arXiv:1709.08481, 2017 | 44 | 2017 |
| Investigating object-oriented design metrics to predict fault-proneness of software modules SS Rathore, A Gupta 2012 CSI Sixth International Conference on Software Engineering (CONSEG), 1-10, 2012 | 42 | 2012 |
| An approach for the prediction of number of software faults based on the dynamic selection of learning techniques SS Rathore, S Kumar IEEE Transactions on Reliability 68 (1), 216-236, 2018 | 35 | 2018 |
| Coupling and cohesion metrics for object-oriented software: A systematic mapping study S Tiwari, SS Rathore Proceedings of the 11th Innovations in Software Engineering Conference, 1-11, 2018 | 27 | 2018 |
| TextConvoNet: a convolutional neural network based architecture for text classification S Soni, SS Chouhan, SS Rathore Applied Intelligence 53 (11), 14249-14268, 2023 | 26 | 2023 |
| Analysis of use case requirements using sfta and sfmea techniques S Tiwari, SS Rathore, S Gupta, V Gogate, A Gupta 2012 IEEE 17th International Conference on Engineering of Complex Computer …, 2012 | 23 | 2012 |
| Software fault prediction: a road map S Kumar, SS Rathore Springer Singapore, 2018 | 22 | 2018 |
| Validating the effectiveness of object-oriented metrics over multiple releases for predicting fault proneness SS Rathore, A Gupta 2012 19th Asia-Pacific Software Engineering Conference 1, 350-355, 2012 | 21 | 2012 |
| Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study SS Rathore, S Kumar Applied Intelligence 51 (12), 8945-8960, 2021 | 20 | 2021 |
Saurabh TiwariDA-IICT Gandhinagar, IndiaGeverifieerd e-mailadres voor daiict.ac.in
