| Effective fault prediction model developed using least square support vector machine (LSSVM) L Kumar, SK Sripada, A Sureka, SK Rath Journal of Systems and Software 137, 686-712, 2018 | 112 | 2018 |
| An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes L Kumar, S Misra, SK Rath Computer standards & interfaces 53, 1-32, 2017 | 75 | 2017 |
| Hybrid functional link artificial neural network approach for predicting maintainability of object-oriented software L Kumar, SK Rath Journal of Systems and Software 121, 170-190, 2016 | 54 | 2016 |
| Validating the Effectiveness of Object-Oriented Metrics for Predicting Maintainability L Kumar, ND Ku, SK Rath 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015 …, 2015 | 47 | 2015 |
| Software maintainability prediction using hybrid neural network and fuzzy logic approach with parallel computing concept L Kumar, SK Rath International Journal of System Assurance Engineering and Management 8, 1487 …, 2017 | 39 | 2017 |
| Statistical and machine learning methods for software fault prediction using CK metric suite: a comparative analysis Y Suresh, L Kumar, SK Rath International Scholarly Research Notices 2014 (1), 251083, 2014 | 38 | 2014 |
| Method level refactoring prediction on five open source java projects using machine learning techniques L Kumar, SM Satapathy, LB Murthy Proceedings of the 12th Innovations in Software Engineering Conference …, 2019 | 30 | 2019 |
| Empirical analysis on effectiveness of source code metrics for predicting change-proneness L Kumar, SK Rath, A Sureka Proceedings of the 10th Innovations in Software Engineering Conference, 4-14, 2017 | 30 | 2017 |
| Feature selection techniques to counter class imbalance problem for aging related bug prediction: aging related bug prediction L Kumar, A Sureka Proceedings of the 11th innovations in software engineering conference, 1-11, 2018 | 26 | 2018 |
| Application of LSSVM and SMOTE on seven open source projects for predicting refactoring at class level L Kumar, A Sureka 2017 24th Asia-Pacific Software Engineering Conference (APSEC), 90-99, 2017 | 26 | 2017 |
| Using source code metrics to predict change-prone web services: A case-study on ebay services L Kumar, SK Rath, A Sureka 2017 IEEE workshop on machine learning techniques for software quality …, 2017 | 25 | 2017 |
| An empirical analysis on web service anti-pattern detection using a machine learning framework L Kumar, A Sureka 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC …, 2018 | 24 | 2018 |
| An empirical study on application of word embedding techniques for prediction of software defect severity level L Kumar, M Kumar, LB Murthy, S Misra, V Kocher, S Padmanabhuni 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS …, 2021 | 23 | 2021 |
| The impact of feature selection on maintainability prediction of service-oriented applications L Kumar, A Krishna, SK Rath Service Oriented Computing and Applications 11, 137-161, 2017 | 22 | 2017 |
| Source code metrics for programmable logic controller (PLC) ladder diagram (LD) visual programming language L Kumar, R Jetley, A Sureka Proceedings of the 7th International Workshop on Emerging Trends in Software …, 2016 | 22 | 2016 |
| An empirical study on predictability of software code smell using deep learning models H Gupta, TG Kulkarni, L Kumar, LBM Neti, A Krishna International conference on advanced information networking and applications …, 2021 | 18 | 2021 |
| Predicting object-oriented software maintainability using hybrid neural network with parallel computing concept L Kumar, SK Rath Proceedings of the 8th India software engineering conference, 100-109, 2015 | 18 | 2015 |
| An empirical framework for code smell prediction using extreme learning machine H Gupta, L Kumar, LBM Neti 2019 9th Annual Information Technology, Electromechanical Engineering and …, 2019 | 17 | 2019 |
| Maintainability prediction of web service using support vector machine with various kernel methods L Kumar, M Kumar, SK Rath International Journal of System Assurance Engineering and Management 8, 205-222, 2017 | 17 | 2017 |
| An effective fault prediction model developed using an extreme learning machine with various kernel methods L Kumar, A Tirkey, SK Rath Frontiers of Information Technology & Electronic Engineering 19, 864-888, 2018 | 16 | 2018 |
Santanu Kumar RathProfessor of Computer Science and Engg. dept., National Institute of Technology, NIT Rourkela 769008在 nitrkl.ac.in 的电子邮件经过验证
