Application of machine learning algorithms for code smell prediction using object-oriented software metrics

Agnihotri, Mansi; Chug, Anuradha

doi:10.1080/09720510.2020.1799576

Cited by 12 publications

(17 citation statements)

References 17 publications

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“…These metrics include class-level metrics (such as lines of code, lack of cohesion among methods, number of methods, fan-in and fan-out) and method-level metrics (such as parameter count, lines of code, cyclomatic complexity, and depth of nested conditional). We observed that some of the attempts use a relatively small number of metrics (Thongkum and Mekruksavanich [313] and Agnihotri and Chug [5] used 10 and 16 metrics, respectively). However, some of the authors chose to experiment with a large number of metrics.…”

Section: Code Smell Detectionmentioning

confidence: 97%

“…Feature extraction: The majority of the articles [5,28,31,41,82,84,102,103,121,124,153,166,193,218,235,251,313] in this category use object-oriented metrics as features. These metrics include class-level metrics (such as lines of code, lack of cohesion among methods, number of methods, fan-in and fan-out) and method-level metrics (such as parameter count, lines of code, cyclomatic complexity, and depth of nested conditional).…”

Section: Code Smell Detectionmentioning

confidence: 99%

“…These models include Decision Tree, Support Vector Machine, Random Forest, Naive Bayes, Logistic Regression, Linear Regression, Polynomial Regression, Bagging, and Multilayer Perceptron. The majority of studies [5,82,84,91,102,103,121,166,200,235,242,251,313] in this category compared the performance of various ml models. Some of the authors experimented with individual ml models; for example, Kaur et al [153] and Amorim et al [28] used Support Vector Machine and Decision Tree, respectively, for smell detection.…”

Section: Model Trainingmentioning

confidence: 99%

“…A typical ml model trained to classify samples into either smelly or non-smelly samples. The majority of the studies focused on a relatively small set of known code smellsgod class [5,31,41,61,82,103,117,121,124,153,200,235], feature envy [5,31,41,82,102,103,124,153,283], long method [31,35,82,102,103,117,121,124,153], data class [31,102,103,117,153,235], and complex class [121,200,235]. Results of these efforts vary significantly; F1 score of the ml models vary between 0.3 to 0.99.…”

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: Code Smell Detectionmentioning

confidence: 97%

Section: Code Smell Detectionmentioning

confidence: 99%

Section: Model Trainingmentioning

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

“…In addition, recent research has increasingly applied machine learning algorithms to related research on code evaluation including the code smell prediction technology that has attracted more attention in software quality in recent years. For example, M. Agnihotri and A. Chug in the study from 2020 introduced the use of random forest machine learning algorithms to perform object-oriented software measurement of code smell prediction [21]. Alshaaby A. and others also analyzed the machine learning technology for detecting code smell in 2020 and compared the performance of several algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Experimental Study on Attribute Validity of Code Quality Evaluation Model

et al. 2022

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Regarding the practicality of the quality evaluation model, the lack of quantitative experimental evaluation affects the effective use of the quality model, and also a lack of effective guidance for choosing the model. Aiming at this problem, based on the sensitivity of the quality evaluation model to code defects, a machine learningbased quality evaluation attribute validity verification method is proposed. This method conducts comparative experiments by controlling variables. First, extract the basic metric elements; then, convert them into quality attributes of the software; finally, to verify the quality evaluation model and the effectiveness of medium quality attributes, this paper compares machine learning methods based on quality attributes with those based on text features, and conducts experimental evaluation in two data sets. The result shows that the effectiveness of quality attributes under control variables is better, and leads by 15% in AdaBoostClassifier; when the text feature extraction method is increased to 50 -150 dimensions, the performance of the text feature in the four machine learning algorithms overtakes the quality attributes; but when the peak is reached, quality attributes are more stable. This also provides a direction for the optimization of the quality model and the use of quality assessment in different situations.

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Severity Factor (SF): An aid to developers for application of refactoring operations to improve software quality

Agnihotri

Chug

2023

J Software Evolu Process

Self Cite

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Bad smells are certain flaws in the structure of the code that might not disturb the normal functioning of a program but negatively affects the software quality. Developers use refactoring as a corrective measure for the treatment of bad smells. The current study aids the developers in the application of refactoring by identifying the critical classes, that is, classes that are challenging to maintain and are of degraded quality. In this study, 10 quality metrics and 10 bad smells have been selected to conduct an investigation on different releases of five open‐source systems. A new metric, severity factor (SF) has been introduced that categorizes the classes of the selected systems into four criticality levels—severe, major, mid, and low. Also, the relationship between SF, criticality levels, and the refactoring operations has been analyzed. The findings show that 60% of the total classes have been affected by bad smells, and long statement is the most dominant smell present in 27.6% of the classes. The results show 84% of the refactoring operations have been performed on highly critical classes. Thus, the SF metric plays a crucial role in driving the developer's attention to the critical classes that need to be treated urgently.

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Application of machine learning algorithms for code smell prediction using object-oriented software metrics

Cited by 12 publications

References 17 publications

A Survey on Machine Learning Techniques for Source Code Analysis

A Survey on Machine Learning Techniques for Source Code Analysis

An Experimental Study on Attribute Validity of Code Quality Evaluation Model

Severity Factor (SF): An aid to developers for application of refactoring operations to improve software quality

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