Refactoring Cost Estimation (RCE) Model for Object Oriented System

Desai, Ankit; Parmar, Jekishan K.

doi:10.1109/iacc.2016.48

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The authors used expert opinion to assess the impact of refactoring on maintainability. Desai et al [21] focused on using the refactoring cost estimation (RCE) concept by applying different identifies such as misuse of classes, violation of the encapsulation principle, lack of inheritance concept, misuse of inheritance, misplaced polymorphism to assess refactoring proneness of a software. Lacerda et al [22] observed the challenges and effectiveness of code smells and refactoring.…”

Section: Literature Surveymentioning

confidence: 99%

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Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

et al. 2022

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Background: Refactoring is changing a software system without affecting the software functionality. The current researchers aim i to identify the appropriate method(s) or class(s) that needs to be refactored in object-oriented software. Ensemble learning helps to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level. Objective: This work aims to develop an ensemble-based refactoring prediction model with structural identification of source code metrics using different feature selection techniques and data sampling techniques to distribute the data uniformly. Our model finds the best classifier after achieving fewer errors during refactoring prediction at the class level. Methodology: At first, our proposed model extracts a total of 125 software metrics computed from object-oriented software systems processed for a robust multi-phased feature selection method encompassing Wilcoxon significant text, Pearson correlation test, and principal component analysis (PCA). The proposed multi-phased feature selection method retains the optimal features characterizing inheritance, size, coupling, cohesion, and complexity. After obtaining the optimal set of software metrics, a novel heterogeneous ensemble classifier is developed using techniques such as ANN-Gradient Descent, ANN-Levenberg Marquardt, ANN-GDX, ANN-Radial Basis Function; support vector machine with different kernel functions such as LSSVM-Linear, LSSVM-Polynomial, LSSVM-RBF, Decision Tree algorithm, Logistic Regression algorithm and extreme learning machine (ELM) model are used as the base classifier. In our paper, we have calculated four different errors i.e., Mean Absolute Error (MAE), Mean magnitude of Relative Error (MORE), Root Mean Square Error (RMSE), and Standard Error of Mean (SEM). Result: In our proposed model, the maximum voting ensemble (MVE) achieves better accuracy, recall, precision, and F-measure values (99.76, 99.93, 98.96, 98.44) as compared to the base trained ensemble (BTE) and it experiences less errors (MAE = 0.0057, MORE = 0.0701, RMSE = 0.0068, and SEM = 0.0107) during its implementation to develop the refactoring model. Conclusions: Our experimental result recommends that MVE with upsampling can be implemented to improve the performance of the refactoring prediction model at the class level. Furthermore, the performance of our model with different data sampling techniques and feature selection techniques has been shown in the form boxplot diagram of accuracy, F-measure, precision, recall, and area under the curve (AUC) parameters.

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Section: Literature Surveymentioning

confidence: 99%

“…In the proposed ELM model, a minimum norm least-squares solution β * is applied to minimize β * in addition to the weight parameters and bias values. Mathematically, β * is referred to as Equation (21).…”

Section: 𝑦(𝑡mentioning

confidence: 99%

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

et al. 2022

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“…In 2016, Desai and Parmar [20] suggested that the object-oriented software frameworks are to be worked to last finish the time yet they will debase as much as any heritage programming framework. Programming reengineering essentially centers on reexecuting more established frameworks to enhance or make it more viable.…”

Section: Literature Surveymentioning

confidence: 99%

A clustering based on optimization for object oriented quality prediction

Thakur¹,

Patidar²,

Chouhan³

et al. 2018

IJATEE

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Refactoring Cost Estimation for Architectural Technical Debt

Deeb

BenIdris

Ammar

et al. 2021

Int. J. Soft. Eng. Knowl. Eng.

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Paying-off the Architectural Technical Debt by refactoring the flawed code is important to control the debt and to keep it as low as possible. Project Managers tend to delay paying off this debt because they face difficulties in comparing the cost of the refactoring against the benefits gained. These managers need to estimate the cost and the efforts required to conduct these refactoring activities as well as to decide which flaws have higher priority to be refactored. Our research is based on a dataset used by other researchers that study the technical debt. It includes more than 18,000 refactoring operations performed on 33 apache java projects. We applied the COCOMO II:2000 model to calculate the refactoring cost in person-months units per release. Furthermore, we investigated the correlation between the refactoring efforts and two static code metrics of the refactored code. The research revealed a weak correlation between the refactoring efforts and the size of the project, and a moderate correlation with the code complexity. Finally, we applied the DesigniteJava tool to verify our research results. From the analysis we found a significant correlation between the ranking of the architecture smells and the ranking of refactoring efforts for each package. Using machine learning practices, we took the architecture smells level and the code metrics of each release as an input to predict the levels of the refactoring effort of the next release. We calculated the results using our model and found that we can predict the ‘High’ and ‘Very High’ levels, the most significant levels from managers’ perspective, with [Formula: see text] accuracy.

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Refactoring Cost Estimation (RCE) Model for Object Oriented System

Cited by 3 publications

References 9 publications

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

A clustering based on optimization for object oriented quality prediction

Refactoring Cost Estimation for Architectural Technical Debt

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