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Santosh Rathore
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Year
A study on software fault prediction techniques
SS Rathore, S Kumar
Artificial Intelligence Review 51, 255-327, 2019
1992019
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
1352017
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
1352016
Towards an ensemble based system for predicting the number of software faults
SS Rathore, S Kumar
Expert Systems with Applications 82, 357-382, 2017
1142017
Open-world machine learning: applications, challenges, and opportunities
J Parmar, S Chouhan, V Raychoudhury, S Rathore
ACM Computing Surveys 55 (10), 1-37, 2023
1112023
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
1112012
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
1042017
TextConvoNet: a convolutional neural network based architecture for text classification
S Soni, SS Chouhan, SS Rathore
Applied Intelligence 53 (11), 14249-14268, 2023
782023
A decision tree logic based recommendation system to select software fault prediction techniques
SS Rathore, S Kumar
Computing 99, 255-285, 2017
732017
Predicting number of faults in software system using genetic programming
SS Rathore, S Kumar
Procedia Computer Science 62, 303-311, 2015
722015
An empirical study of ensemble techniques for software fault prediction
SS Rathore, S Kumar
Applied Intelligence 51, 3615-3644, 2021
592021
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
582014
A methodology for the selection of requirement elicitation techniques
S Tiwari, SS Rathore
arXiv preprint arXiv:1709.08481, 2017
502017
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
442012
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
392018
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
322018
Generative oversampling methods for handling imbalanced data in software fault prediction
SS Rathore, SS Chouhan, DK Jain, AG Vachhani
IEEE Transactions on Reliability 71 (2), 747-762, 2022
262022
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
252021
Software fault prediction: a road map
S Kumar, SS Rathore
Springer Singapore, 2018
232018
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
232012
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