Machine learning based code smell detection through WekaNose

2018 ◽  
Author(s):  
Umberto Azadi ◽  
Francesca Arcelli Fontana ◽  
Marco Zanoni
Keyword(s):  
Machine Learning ◽  
Code Smell

A Review on Machine-Learning Based Code Smell Detection Techniquesin Object-Oriented Software System(s)

2020 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur ◽  
Sushma Jain ◽  
Shivani Goel ◽  
Gaurav Dhiman
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Software Quality ◽  
Empirical Studies ◽  
Statistical Testing ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Code Smells ◽  
Detection Techniques ◽  
Code Smell

Context: Code smells are symptoms, that something may be wrong in software systems that can cause complications in maintaining software quality. In literature, there exists many code smells and their identification is far from trivial. Thus, several techniques have also been proposed to automate code smell detection in order to improve software quality. Objective: This paper presents an up-to-date review of simple and hybrid machine learning based code smell detection techniques and tools. Methods: We collected all the relevant research published in this field till 2020. We extracted the data from those articles and classified them into two major categories. In addition, we compared the selected studies based on several aspects like, code smells, machine learning techniques, datasets, programming languages used by datasets, dataset size, evaluation approach, and statistical testing. Results: Majority of empirical studies have proposed machine- learning based code smell detection tools. Support vector machine and decision tree algorithms are frequently used by the researchers. Along with this, a major proportion of research is conducted on Open Source Softwares (OSS) such as, Xerces, Gantt Project and ArgoUml. Furthermore, researchers paid more attention towards Feature Envy and Long Method code smells. Conclusion: We identified several areas of open research like, need of code smell detection techniques using hybrid approaches, need of validation employing industrial datasets, etc.

scholarly journals Automatic detection of Long Method and God Class code smells through neural source code embeddings

2021 ◽  
Author(s):  
Aleksandar Kovačević ◽  
Jelena Slivka ◽  
Dragan Vidaković ◽  
Katarina-Glorija Grujić ◽  
Nikola Luburić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Negative Impact ◽  
Source Code ◽  
Systematic Evaluation ◽  
Small Scale ◽  
Code Smells ◽  
Code Metrics ◽  
Code Smell ◽  
F Measure

<p>Code smells are structures in code that often have a negative impact on its quality. Manually detecting code smells is challenging and researchers proposed many automatic code smell detectors. Most of the studies propose detectors based on code metrics and heuristics. However, these studies have several limitations, including evaluating the detectors using small-scale case studies and an inconsistent experimental setting. Furthermore, heuristic-based detectors suffer from limitations that hinder their adoption in practice. Thus, researchers have recently started experimenting with machine learning (ML) based code smell detection. </p><p>This paper compares the performance of multiple ML-based code smell detection models against multiple traditionally employed metric-based heuristics for detection of God Class and Long Method code smells. We evaluate the effectiveness of different source code representations for machine learning: traditionally used code metrics and code embeddings (code2vec, code2seq, and CuBERT).<br></p><p>We perform our experiments on the large-scale, manually labeled MLCQ dataset. We consider the binary classification problem – we classify the code samples as smelly or non-smelly and use the F1-measure of the minority (smell) class as a measure of performance. In our experiments, the ML classifier trained using CuBERT source code embeddings achieved the best performance for both God Class (F-measure of 0.53) and Long Method detection (F-measure of 0.75). With the help of a domain expert, we perform the error analysis to discuss the advantages of the CuBERT approach.<br></p><p>This study is the first to evaluate the effectiveness of pre-trained neural source code embeddings for code smell detection to the best of our knowledge. A secondary contribution of our study is the systematic evaluation of the effectiveness of multiple heuristic-based approaches on the same large-scale, manually labeled MLCQ dataset.<br></p>

Comparing within- and cross-project machine learning algorithms for code smell detection

2021 ◽  
Author(s):  
Manuel De Stefano ◽  
Fabiano Pecorelli ◽  
Fabio Palomba ◽  
Andrea De Lucia
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Code Smell ◽  
Cross Project

Code Smell Detection: Towards a Machine Learning-Based Approach

2013 ◽  
Author(s):  
Francesca Arcelli Fontana ◽  
Marco Zanoni ◽  
Alessandro Marino ◽  
Mika V. Mantyla
Keyword(s):  
Machine Learning ◽  
Code Smell

scholarly journals Automatic detection of Long Method and God Class code smells through neural source code embeddings

2021 ◽  
Author(s):  
Aleksandar Kovačević ◽  
Jelena Slivka ◽  
Dragan Vidaković ◽  
Katarina-Glorija Grujić ◽  
Nikola Luburić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Negative Impact ◽  
Source Code ◽  
Systematic Evaluation ◽  
Small Scale ◽  
Code Smells ◽  
Code Metrics ◽  
Code Smell ◽  
F Measure

<p>Code smells are structures in code that often have a negative impact on its quality. Manually detecting code smells is challenging and researchers proposed many automatic code smell detectors. Most of the studies propose detectors based on code metrics and heuristics. However, these studies have several limitations, including evaluating the detectors using small-scale case studies and an inconsistent experimental setting. Furthermore, heuristic-based detectors suffer from limitations that hinder their adoption in practice. Thus, researchers have recently started experimenting with machine learning (ML) based code smell detection. </p><p>This paper compares the performance of multiple ML-based code smell detection models against multiple traditionally employed metric-based heuristics for detection of God Class and Long Method code smells. We evaluate the effectiveness of different source code representations for machine learning: traditionally used code metrics and code embeddings (code2vec, code2seq, and CuBERT).<br></p><p>We perform our experiments on the large-scale, manually labeled MLCQ dataset. We consider the binary classification problem – we classify the code samples as smelly or non-smelly and use the F1-measure of the minority (smell) class as a measure of performance. In our experiments, the ML classifier trained using CuBERT source code embeddings achieved the best performance for both God Class (F-measure of 0.53) and Long Method detection (F-measure of 0.75). With the help of a domain expert, we perform the error analysis to discuss the advantages of the CuBERT approach.<br></p><p>This study is the first to evaluate the effectiveness of pre-trained neural source code embeddings for code smell detection to the best of our knowledge. A secondary contribution of our study is the systematic evaluation of the effectiveness of multiple heuristic-based approaches on the same large-scale, manually labeled MLCQ dataset.<br></p>

scholarly journals A large empirical assessment of the role of data balancing in machine-learning-based code smell detection

2020 ◽  
Vol 169 ◽  
pp. 110693 ◽  
Author(s):  
Fabiano Pecorelli ◽  
Dario Di Nucci ◽  
Coen De Roover ◽  
Andrea De Lucia
Keyword(s):  
Machine Learning ◽  
Empirical Assessment ◽  
Code Smell
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