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Dr. Lov Kumar
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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
3862019
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
1042018
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
702017
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
522016
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
482015
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, 2014
392014
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
372017
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
272017
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
272017
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
252018
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
242017
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
232018
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
212016
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
202021
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
192017
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
192015
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
162017
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
152019
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
142021
Detection of web service anti-patterns using weighted extreme learning machine
S Tummalapalli, L Kumar, LBM Neti, A Krishna
Computer Standards & Interfaces 82, 103621, 2022
132022
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