Change-Proneness of Object-Oriented Software Using Combination of Feature Selection Techniques and Ensemble Learning Techniques

Kumar, Lov; Lal, Sangeeta; Goyal, Anjali; Neti, Lalita Bhanu Murthy

doi:10.1145/3299771.3299778

Cited by 7 publications

(16 citation statements)

References 8 publications

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“…However, only Zhu et al [5] assessed the performance of HEL for SCP, by changing the underlying base learners. Kumar et al [12] evaluated the SCP models using heterogeneous ensemble learners. The study by Aljamaan and Alazba [7] validated tree-based HEL for SFP, advocating the use of these techniques in the domain.…”

Section: Related Workmentioning

confidence: 99%

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

Khanna

Shanmugapriya

Mehra

2021

IFIP Advances in Information and Communication Technology

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Customizability, extensive community support and ease of availability have led to the popularity of Open-Source Software (OSS) systems. However, maintenance of these systems is a challenge especially as they become considerably large and complex with time. One possible method of ensuring effective quality in large scale OSS is the adoption of software change prediction models. These models aid in identifying change-prone parts in the early stages of software development, which can then be effectively managed by software practitioners. This study extensively evaluates eight Homogeneous Ensemble Learners (HEL) for developing software change prediction models on five large scale OSS datasets. HEL, which integrate the outputs of several learners of the same type are known to generate improved results than other non-ensemble classifiers. The study also statistically compares the results of the models developed by HEL with ten nonensemble classifiers. We further assess the change in performance of HEL for developing software change prediction models by substituting their default base learners with other classifiers. The results of the study support the use of HEL for developing software change prediction models and indicate Random Forest as the best HEL for the purpose.

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Section: Related Workmentioning

confidence: 99%

“…The greater the value of these measures, the better is the performance of developed models. We selected these performance measures as they are robust, stable and give effective results even with imbalanced data [5,8,12].…”

Section: Performance Measuresmentioning

confidence: 99%

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

Khanna

Shanmugapriya

Mehra

2021

IFIP Advances in Information and Communication Technology

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“…The authors highlight that machine learning is a promising approach and is a tendency, adopted in more recent works. Godara et al (2018) and Kumar et al (2019) investigate the use of metrics to predict change-prone classes. While the work of Godara et al (2018) relied on the bee colony algorithm (Karaboga, 2005) to perform its predictions, Kumar et al (2019) addresses the use of machine learning.…”

Section: Introductionmentioning

confidence: 99%

“…Godara et al (2018) and Kumar et al (2019) investigate the use of metrics to predict change-prone classes. While the work of Godara et al (2018) relied on the bee colony algorithm (Karaboga, 2005) to perform its predictions, Kumar et al (2019) addresses the use of machine learning. The work of Kumar et al (2019) used 18 machine learning algorithms, including Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), among others.…”

Section: Introductionmentioning

confidence: 99%

“…While the work of Godara et al (2018) relied on the bee colony algorithm (Karaboga, 2005) to perform its predictions, Kumar et al (2019) addresses the use of machine learning. The work of Kumar et al (2019) used 18 machine learning algorithms, including Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), among others. The results show that code metrics are useful in predicting changes in software modules and that the results may vary according to the choice of metrics.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Change-Prone Class Prediction: A History-Based Approach

Silva

Farah

Vergílio

2022

Proceedings of the XXXVI Brazilian Symposium on Software Engineering

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Federal do Paraná foram convocados para realizar a arguição da Dissertação de Mestrado de ROGÉRIO DE CARVALHO SILVA intitulada: Machine Learning for Change-Prone Class Prediciton: A History-Based Approach, sob orientação da Profa. Dra. SILVIA REGINA VERGILIO, que após terem inquirido o aluno e realizada a avaliação do trabalho, são de parecer pela sua APROVAÇÃO no rito de defesa.A outorga do título de mestre está sujeita à homologação pelo colegiado, ao atendimento de todas as indicações e correções solicitadas pela banca e ao pleno atendimento das demandas regimentais do Programa de Pós-Graduação.

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

Khanna,

Bansal

2024

J Software Evolu Process

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Software managers constantly look out for methods that ensure cost effective development of good quality software products. An important means of accomplishing this is by allocating more resources to weak classes of a software product, which are prone to changes. Therefore, correct prediction of these change‐prone classes is critical. Though various researchers have investigated the performance of several algorithms for identifying them, the search for an optimum classifier still persists. To this end, this study critically investigates the use of six Heterogenous Ensemble Classifiers (HEC) for Software Change Prediction (SCP) by empirically validating datasets obtained from 12 open‐source software systems. The results of the study are statistically assessed using three robust performance indicators (AUC, F‐measure and Mathew Correlation Coefficient) in two different validation scenarios (within project and cross‐project). They indicate the superiority of Average Probability Voting Ensemble, a heterogenous classifier for determining change‐proneness in the investigated systems. The average AUC values of software change prediction models developed using this ensemble classifier exhibited an improvement of 3%‐9% and 3%‐11% respectively when compared with its base learners and homogeneous counter parts. Similar observations were inferred using other investigated performance measures. Furthermore, the evidence obtained from the results suggests that the change in number of base learners or type of meta‐learner does not exhibit significant change in the performance of corresponding heterogenous ensemble classifiers.

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Change-Proneness of Object-Oriented Software Using Combination of Feature Selection Techniques and Ensemble Learning Techniques

Cited by 7 publications

References 8 publications

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

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

Machine Learning for Change-Prone Class Prediction: A History-Based Approach

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

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