| A systematic review of machine learning techniques for software fault prediction R Malhotra Applied Soft Computing 27, 504-518, 2015 | 723 | 2015 |
| Empirical Study of Object-Oriented Metrics. KK Aggarwal, Y Singh, A Kaur, R Malhotra J. Object Technol. 5 (8), 149-173, 2006 | 291* | 2006 |
| Empirical validation of object-oriented metrics for predicting fault proneness models Y Singh, A Kaur, R Malhotra Software quality journal 18, 3-35, 2010 | 259 | 2010 |
| Empirical analysis for investigating the effect of object‐oriented metrics on fault proneness: a replicated case study KK Aggarwal, Y Singh, A Kaur, R Malhotra Software process: Improvement and practice 14 (1), 39-62, 2009 | 183 | 2009 |
| Fault prediction using statistical and machine learning methods for improving software quality R Malhotra, A Jain Journal of Information Processing Systems 8 (2), 241-262, 2012 | 175 | 2012 |
| An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data R Malhotra, S Kamal Neurocomputing 343, 120-140, 2019 | 160 | 2019 |
| Empirical research in software engineering: concepts, analysis, and applications R Malhotra CRC press, 2016 | 154 | 2016 |
| Comparative analysis of statistical and machine learning methods for predicting faulty modules R Malhotra Applied Soft Computing 21, 286-297, 2014 | 125 | 2014 |
| Techniques for text classification: Literature review and current trends. R Jindal, R Malhotra, A Jain webology 12 (2), 2015 | 114 | 2015 |
| Application of random forest in predicting fault-prone classes A Kaur, R Malhotra 2008 international conference on advanced computer theory and engineering, 37-43, 2008 | 106 | 2008 |
| Investigation of relationship between object-oriented metrics and change proneness R Malhotra, M Khanna International Journal of Machine Learning and Cybernetics 4, 273-286, 2013 | 84 | 2013 |
| Software reuse metrics for object-oriented systems KK Aggarwal, Y Singh, A Kaur, R Malhotra Third ACIS Int'l Conference on Software Engineering Research, Management and …, 2005 | 83 | 2005 |
| Software maintainability: Systematic literature review and current trends R Malhotra, A Chug International Journal of Software Engineering and Knowledge Engineering 26 …, 2016 | 82 | 2016 |
| Soft computing approaches for prediction of software maintenance effort A Kaur, K Kaur, R Malhotra International Journal of Computer Applications 1 (16), 69-75, 2010 | 82 | 2010 |
| Software maintainability prediction using machine learning algorithms R Malhotraš, A Chug Software engineering: an international Journal (SeiJ) 2 (2), 2012 | 79 | 2012 |
| Comparative analysis of regression and machine learning methods for predicting fault proneness models Y Singh, A Kaur, R Malhotra International journal of computer applications in technology 35 (2-4), 183-193, 2009 | 78 | 2009 |
| An empirical study for software change prediction using imbalanced data R Malhotra, M Khanna Empirical Software Engineering 22, 2806-2851, 2017 | 76 | 2017 |
| An empirical framework for defect prediction using machine learning techniques with Android software R Malhotra Applied Soft Computing 49, 1034-1050, 2016 | 76 | 2016 |
| Software effort prediction using statistical and machine learning methods R Malhotra, A Jain International Journal of Advanced Computer Science and Applications 2 (1), 2011 | 69 | 2011 |
| Software fault proneness prediction using support vector machines Y Singh, A Kaur, R Malhotra Proceedings of the world congress on engineering 1, 1-3, 2009 | 69 | 2009 |
Megha KhannaDepartment of Computer Science, Sri Guru Gobind Singh College of Commerce, University of DelhiVerified email at sggscc.ac.in
