Machine learning based code smell detection through WekaNose

Azadi, Umberto; Fontana, Francesca Arcelli; Zanoni, Marco

doi:10.1145/3183440.3194974

Cited by 15 publications

(6 citation statements)

References 5 publications

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“…This research paper [17] offers a novel approach to data mining-based code smell detection by recommending the use of WekaNose. While the approach is different, it is still experimental, and the article was missing technicities and analysis required for experiments.…”

Section: Applying Data Mining To Detect Any Kind Of Code Smellsmentioning

confidence: 99%

Detecting Security Bad Smells of Software by using Data Mining

Alraddadi

2023

Preprint

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Bad smells of code can lead to significant software vulnerabilities that negatively affect the security attribute of software and make the software easily attacked. Method implementation is the lowest code level that produces bad smells in software. Several programming guidelines have been introduced to reduce the number of bad code smells. Some are related to security, while others are related to other software quality attributes. We can detect some of these guidelines by using quality metrics (e.g., the number of lines of code and the number of parameters). This project proposes applying data mining classifiers to automatically detect security code smells and non-security code smells based on software quality metrics. We developed eight models using eight classifiers: Logistic Regression, Random Forest, XG Boost, J48, SVM, Naive Bayes, LightGBM, and Neural Network. We evaluated the eight models using several performance measurements (e.g., accuracy and confusion metrics). The results showed that it could detect the security code smell from the non-security code smell with the highest accuracy of 95\%.

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Section: Applying Data Mining To Detect Any Kind Of Code Smellsmentioning

confidence: 99%

Detecting Security Bad Smells of Software by using Data Mining

Alraddadi

2023

Preprint

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“…A tool called WekaNose was introduced [7] to perform experiments using ML techniques to detect smells from v-code. This method intentionally sets the rules for obtaining the trained algorithms in order to categorize an instance (method or class) as affected or not by a CS.…”

Section: Literature Surveymentioning

confidence: 99%

Extension of Deep Learning Based Feature Envy Detection for Misplaced Fields and Methods

2022

IJIES

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Code smells (CS) are a severe symptom in software source code that leads to a serious problem in software maintenance and evolution. Feature Envy (FE) is a type of CS that refers to methods that are misplaced in the source code. Many tools like infusion, JDeodorant and JspIRIT have been used to identify the CS. The conversion of source code measurements into predictions is based on manually constructed heuristics. But, manually selecting good features and constructing optimal heuristics is a challenging issue. So, automatic detection of CS is required. The Deep Learning (DL) methods play an important role in achieving the automatic detection of FE(DLFED). The FE detection approach based on DL is intended to discover misplaced methods. The DL technique automatically identifies the source code characteristics for FE identification as well as automatically improves the difficult mapping structure between the features and their prediction. But, a DL-based automatic detection of CS requires a significant number of features with labeled training datasets to provide better prediction results. Therefore, an extension of the DL-based FE detection technique (EDLFED-FM) is proposed in this article to detect both misplaced methods and fields. The semantic relationship between identifiers in the source code program is used to extract the features of misplaced fields and methods. The new decomposition slice method is proposed to convert the extracted features of misplaced methods and fields into the slicing vector composition. This sliced feature helps deep learners understand the relationship between misplaced methods and fields. This automated method is capable of producing labeled training data for DL techniques-based classifiers without the requirement for human intervention. Finally, the experimental outcomes proved that precision rate of suggested method outperforms the JDeodorant and DLFED methods by 14.49% and 10.29 % for free plane, 14.72 % and 12.2 % for Junit, and 21.84% and 14.68 % for JExcelapi applications. Hence, it has been demonstrated that the predicted misplaced methods and fields are moved to the refactoring class for positive testing of source code program with less training data.

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“…Although, all the ML techniques have certain pros and cons but the selection of the most suitable technique depends on the type of dataset being constructed or employed. In general, decision trees appeared to be highly employed among the articles due to its simplicity and strong classification and regression capabilities [9,65,16].…”

Section: Q13 ML Type and Techniquesmentioning

confidence: 99%

Machine Learning for Software Engineering: A Systematic Mapping

Shafiq,

Mashkoor,

Mayr-Dorn

et al. 2020

Preprint

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The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for improving the software engineering life cycle itself is yet to be discovered, i.e., up to what extent machine learning can help reducing the effort/complexity of software engineering and improving the quality of resulting software systems. To date, no comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. Objective: This article addresses the aforementioned problem and aims to present a state-of-the-art on the growing number of uses of machine learning in software engineering. Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering.Results: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages. Overall, 227 articles were rigorously selected and analyzed as a result of this study. Conclusion: From the selected articles, we explore a variety of aspects that should be helpful to academics and practitioners alike in understanding the potential of adopting machine learning techniques during software engineering projects.

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Machine learning based code smell detection through WekaNose

Cited by 15 publications

References 5 publications

Detecting Security Bad Smells of Software by using Data Mining

Detecting Security Bad Smells of Software by using Data Mining

Extension of Deep Learning Based Feature Envy Detection for Misplaced Fields and Methods

Machine Learning for Software Engineering: A Systematic Mapping

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