Stability Aware Software Refactoring Using Hybrid Search Based Techniques

Vimaladevi, M.; Zayaraz, G.

doi:10.1109/ictacc.2017.18

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…Bashir et al [16] focused on designing a refactoring assessment method to help developers making strengthen software with reliability, maintainability, understandability, modifiability, and analyzability. The paper [17] on stability-oriented refactoring estimation used four attributes: abstraction, cohesion, coupling, and inheritance. They applied a hybrid Gravitational Search Algorithm and Artificial Bee Colony algorithm (GSA-ABC) to assess refactoring likelihood.…”

Section: Literature Surveymentioning

confidence: 99%

“…They applied a hybrid Gravitational Search Algorithm and Artificial Bee Colony algorithm (GSA-ABC) to assess refactoring likelihood. Vimaladevi [17] used the stability of the code as the fitness function. Krishna et al [18] evaluated whether refactoring could impact the quality of the code.…”

Section: Literature Surveymentioning

confidence: 99%

“…Moreover, the connecting weights in between the ith hidden layer and kth output layer is given by β ik . The G(a i , b i , X) can be defined for other hidden neurons as shown in Equation (17).…”

Section: 𝑦(𝑡mentioning

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%

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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“…After this, a total of 110 papers are retrieved from these 500. Out of the 110, 90 are obtained using the keyword criteria [1][40] while the remaining 20 papers are obtained through snowballing [21][41] [29]. After this, these 110 papers are further reviewed and out of these 36 are selected.…”

Section: Selection Criteriamentioning

confidence: 99%

“…Determine refactoring points [19] 3. Decrease code complexity [29] 4. Manage refactoring sequence [30] There are following six major challenges of refactoring:…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review on Software- refactoring Techniques, Challenges, and Practices

Akhtar,

Nazir,

Ali

et al. 2022

VFAST trans. softw. eng.

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Software-refactoring improves the quality and reduces the complexity during the whole life cycle of the software system. The objective of this work is to elicit the existing challenges and refactoring techniques and practices to counter those challenges. This study presents a systematic literature review based upon the scientific literature published since January 2000. After rigorously applying the SLR protocol defined by Barbara Kitchenham, we identify 36 articles discussing software- refactoring challenges, techniques, and practices. We point out complexity, understandability, and maintainability as three challenges of traditional software engineering that are resolved by software-refactoring. This SLR identifies assertions, program slicing, graph transformation, software metrics, and metamodeling, as five commonly used refactoring techniques. Moreover, eight main challenges of software-refactoring, namely determining refactoring and code location, selecting refactoring type and technique, managing refactoring dependency, consistency maintenance, refactoring ordering and increase in testing cost, are also highlighted. The findings of this work suggest that software-refactoring techniques can resolve challenges of traditional software engineering.

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A systematic review of refactoring opportunities by software antipattern detection

Kalhor,

Keyvanpour,

Salajegheh

2024

Autom Softw Eng

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Stability Aware Software Refactoring Using Hybrid Search Based Techniques

Cited by 8 publications

References 3 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 Systematic Literature Review on Software- refactoring Techniques, Challenges, and Practices

A systematic review of refactoring opportunities by software antipattern detection

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