Application of LSSVM and SMOTE on Seven Open Source Projects for Predicting Refactoring at Class Level

Kumar, Lov; Sureka, Ashish

doi:10.1109/apsec.2017.15

Cited by 19 publications

(12 citation statements)

References 13 publications

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“…Finally, Kurbatova et al [168] generated synthetic data by moving methods to other classes to prepare a dataset for feature envy smell. The rest of the studies in this category [32,164,165], used the tera-promise dataset containing various metrics for open-source projects where the classes that need refactoring are tagged.…”

Section: Dataset Preparationmentioning

confidence: 99%

“…Similarly, Kumar et al [164] computed 25 different code quality metrics using the SourceMeter tool; these metrics include cyclomatic complexity, class class and clone complexity, loc, outgoing method invocations, and so on. Some of the studies [32,165] calculated a large number of metrics. Specifically, Kumar and Sureka [165] computed 102 metrics and then applied pca to reduce the number of features to 31, while Aribandi et al [32] used 125 metrics.…”

Section: Dataset Preparationmentioning

confidence: 99%

“…Some of the studies [32,165] calculated a large number of metrics. Specifically, Kumar and Sureka [165] computed 102 metrics and then applied pca to reduce the number of features to 31, while Aribandi et al [32] used 125 metrics. Some other studies did not limit themselves to only code quality metrics.…”

Section: Dataset Preparationmentioning

confidence: 99%

“…Kosker et al [160] trained a Naive Bayes-based classifier to identify classes that need refactoring. Kumar and Sureka [165] used Least Square-Support Vector Machine (ls-svm) along with smote as classifier. They found that ls-svm with Radial Basis Function (rbf) kernel gives the best results.…”

Section: Model Trainingmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Machine Learning Techniques for Source Code Analysis

Sharma¹,

Kechagia²,

Georgiou³

et al. 2021

Preprint

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Context:The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis such as testing and vulnerabilities detection. A large number of studies poses challenges to the community to understand the current landscape. Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis. Method: We investigate studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we carried out an extensive literature search and identified 364 primary studies published between 2002 and 2021. We summarize our observations and findings with the help of the identified studies. Results: Our findings suggest that the usage of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task, and summarize the employed machine learning techniques. Additionally, we collate a comprehensive list of available datasets and tools useable in this context. Finally, we summarize the perceived challenges in this area that include availability of standard datasets, reproducibility and replicability, and hardware resources. CCS Concepts: • Software and its engineering → Software libraries and repositories; Software maintenance tools; Software post-development issues; Maintaining software; • Computing methodologies → Machine learning.

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Section: Dataset Preparationmentioning

confidence: 99%

Section: Dataset Preparationmentioning

confidence: 99%

Section: Dataset Preparationmentioning

confidence: 99%

Section: Model Trainingmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Machine Learning Techniques for Source Code Analysis

Sharma¹,

Kechagia²,

Georgiou³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, machine learning is harnessed in the area of prediction and shows noticeable performance in terms of prediction in various fields as computer vision, defect prediction, natural language processing, code comprehension, bioinformatics, speech recognition, and finance [17][18][19][20][21][22][23][24]. Several machine learning algorithms are utilized in code refactoring prediction at class and method level as well [25,26].…”

Section: Introductionmentioning

confidence: 99%

Harnessing deep learning algorithms to predict software refactoring

2020

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During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying the targeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers' skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring prediction at the class level. The effectiveness of the proposed model is evaluated using a very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction.

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