Megha Khanna

According to our database, Megha Khanna authored at least 22 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

An assessment of heterogenous ensemble classifiers for analyzing change-proneness in open-source software systems.

Megha Khanna, Ankita Bansal

J. Softw. Evol. Process., August, 2024

2023

On the applicability of search-based algorithms for software change prediction.

Ruchika Malhotra, Megha Khanna

Int. J. Syst. Assur. Eng. Manag., February, 2023

2022

A Text Mining Framework for Analyzing Change Impact and Maintenance Effort of Software Bug Reports.

Ruchika Malhotra, Megha Khanna

Int. J. Inf. Retr. Res., 2022

A Systematic Review of Ensemble Techniques for Software Defect and Change Prediction.

Megha Khanna

e Informatica Softw. Eng. J., 2022

2021

Software Change Prediction with Homogeneous Ensemble Learners on Large Scale Open-Source Systems.

Megha Khanna, Srishti Priya, Diksha Mehra

Proceedings of the Open Source Systems - 17th IFIP WG 2.13 International Conference, 2021

2019

Dynamic selection of fitness function for software change prediction using Particle Swarm Optimization.

Ruchika Malhotra, Megha Khanna

Inf. Softw. Technol., 2019

Analyzing Evolution Patterns of Object-Oriented Metrics: A Case Study on Android Software.

Ruchika Malhotra, Megha Khanna

Int. J. Rough Sets Data Anal., 2019

Software Change Prediction: A Systematic Review and Future Guidelines.

Ruchika Malhotra, Megha Khanna

e Informatica Softw. Eng. J., 2019

2018

Prediction of change prone classes using evolution-based and object-oriented metrics.

Ruchika Malhotra, Megha Khanna

J. Intell. Fuzzy Syst., 2018

Particle swarm optimization-based ensemble learning for software change prediction.

Ruchika Malhotra, Megha Khanna

Inf. Softw. Technol., 2018

Threats to validity in search-based predictive modelling for software engineering.

Ruchika Malhotra, Megha Khanna

IET Softw., 2018

An empirical study to evaluate the relationship of object-oriented metrics and change proneness.

Ruchika Malhotra, Megha Khanna

Int. Arab J. Inf. Technol., 2018

2017

On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions.

Ruchika Malhotra, Megha Khanna, Rajeev R. Raje

Swarm Evol. Comput., 2017

An empirical study for software change prediction using imbalanced data.

Ruchika Malhotra, Megha Khanna

Empir. Softw. Eng., 2017

An exploratory study for software change prediction in object-oriented systems using hybridized techniques.

Ruchika Malhotra, Megha Khanna

Autom. Softw. Eng., 2017

Software change prediction using voting particle swarm optimization based ensemble classifier.

Ruchika Malhotra, Megha Khanna

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2016

Common threats to software quality predictive modeling studies using search-based techniques.

Ruchika Malhotra, Megha Khanna

Proceedings of the 2016 International Conference on Advances in Computing, 2016

2015

Mining the impact of object oriented metrics for change prediction using Machine Learning and Search-based techniques.

Ruchika Malhotra, Megha Khanna

Proceedings of the 2015 International Conference on Advances in Computing, 2015

2014

A new metric for predicting software change using gene expression programming.

Ruchika Malhotra, Megha Khanna

Proceedings of the 5th International Workshop on Emerging Trends in Software Metrics, 2014

Analyzing software change in open source projects using Artificial Immune System algorithms.

Ruchika Malhotra, Megha Khanna

Proceedings of the 2014 International Conference on Advances in Computing, 2014

Examining the effectiveness of machine learning algorithms for prediction of change prone classes.

Ruchika Malhotra, Megha Khanna

Proceedings of the International Conference on High Performance Computing & Simulation, 2014

2013

Investigation of relationship between object-oriented metrics and change proneness.

Ruchika Malhotra, Megha Khanna

Int. J. Mach. Learn. Cybern., 2013