Performance Prediction for Lock-based Programs DOI
Dongwen Zhang, Tongtong Wang, Yang Zhang

et al.

Published: Oct. 22, 2023

Using different locking mechanisms affects parallel programs' performance differently, and its impact on program is difficult to assess, which hinders researchers from rationally utilizing mechanisms. Moreover, there are few studies the prediction of To address this issue, paper proposes a combination deep feedforward neural network (FNN) Random Forest (RF) method LockPerf predict programs with The predicted in execution time program. In paper, extracting static characteristics first, then sets variables such as number threads, lock type, read/write ratio by switch statement, finally runs collect multiple samples construct configurable data set. A total 9 projects employed evaluate effectiveness Experimental results show that average mean relative errors 5.47, standard 95% confidence intervals 0.13. experiments effectively predicts

Language: Английский

DeleSmell: Code smell detection based on deep learning and latent semantic analysis DOI
Yang Zhang,

Chuyan Ge,

Shuai Hong

et al.

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 255, P. 109737 - 109737

Published: Aug. 22, 2022

Language: Английский

Citations

35

A study of dealing class imbalance problem with machine learning methods for code smell severity detection using PCA-based feature selection technique DOI Creative Commons

Rajwant Singh Rao,

Seema Dewangan,

Alok Mishra

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Sept. 27, 2023

Abstract Detecting code smells may be highly helpful for reducing maintenance costs and raising source quality. Code facilitate developers or researchers to understand several types of design flaws. with high severity can cause significant problems the software challenges system's maintainability. It is quite essential assess detected in software, as it prioritizes refactoring efforts. The class imbalance problem also further enhances difficulties smell detection. In this study, four datasets (Data class, God Feature envy, Long method) are selected detect severity. work, an effort made address issue imbalance, which, Synthetic Minority Oversampling Technique (SMOTE) balancing technique applied. Each dataset's relevant features chosen using a feature selection based on principal component analysis. determined five machine learning techniques: K-nearest neighbor, Random forest, Decision tree, Multi-layer Perceptron, Logistic Regression. This study obtained 0.99 accuracy score forest tree approach method smell. model's performance compared its three other measurements (Precision, Recall, F-measure) estimate classification models. impact presented without applying SMOTE. results promising beneficial paving way studies area.

Language: Английский

Citations

20

On the relative value of imbalanced learning for code smell detection DOI
Fuyang Li,

Kuan Zou,

Jacky Keung

et al.

Software Practice and Experience, Journal Year: 2023, Volume and Issue: 53(10), P. 1902 - 1927

Published: June 26, 2023

Summary Machine learning‐based code smell detection (CSD) has been demonstrated to be a valuable approach for improving software quality and enabling developers identify problematic patterns in code. However, previous researches have shown that the datasets commonly used train these models are heavily imbalanced. While some recent studies explored use of imbalanced learning techniques CSD, they only evaluated limited number thus their conclusions about most effective methods may biased inconclusive. To thoroughly evaluate effect machine we examine 31 with seven classifiers build CSD on four data sets. We employ evaluation metrics assess performance Wilcoxon signed‐rank test Cliff's . The results show (1) Not all significantly improve performance, but deep forest outperforms other (2) SMOTE (Synthetic Minority Over‐sampling TEchnique) is not technique resampling (3) best‐performing top‐3 little time cost detection. Therefore, provide practical guidelines. First, researchers practitioners should select appropriate (e.g., forest) ameliorate class imbalance problem. In contrast, blind application could harmful. Then, better than selected preprocess

Language: Английский

Citations

14

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature DOI Open Access
Peter Kokol

Published: March 11, 2024

Artificial intelligence (AI) has witnessed an exponential increase in its use various applications. Recently, the academic community started to research and inject new AI-based approaches provide solutions traditional software engineering problems. However, a comprehensive holistic understanding of current status is missing. To close above gap, synthetic knowledge synthesis was used induce landscape contemporary literature on AI engineering. The resulted 15 categories five themes, namely natural language processing engineering, artificial management development life cycle, machine learning fault/defect prediction effort estimation, employment deep intelligent code management, mining repositories improve quality. most productive country China (n=2042), followed by United States (n=1193), India (n=934), Germany (n=445), Canada (n=381). A high percentage (n=47.4%) papers were funded, showing strong interest this topic. convergence can significantly reduce needed resources, quality, user experience, well-being developers.

Language: Английский

Citations

5

A survey on machine learning techniques applied to source code DOI Creative Commons
Tushar Sharma, Maria Kechagia, Stefanos Georgiou

et al.

Journal of Systems and Software, Journal Year: 2023, Volume and Issue: 209, P. 111934 - 111934

Published: Dec. 19, 2023

The advancements in machine learning techniques have encouraged researchers to apply these a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such large number studies hinders the community from understanding current research landscape. This paper aims summarize knowledge applied for analysis. We review belonging twelve categories corresponding techniques, tools, datasets been solve them. To do so, we conducted an extensive literature search identified 494 studies. our observations findings with help Our suggest analysis is consistently increasing. synthesize commonly used steps overall workflow each task employed. identify comprehensive list available tools useable this context. Finally, discusses perceived challenges area, including availability standard datasets, reproducibility replicability, hardware resources. Editor's note: Open Science material was validated by Journal Systems Software Board.

Language: Английский

Citations

13

Automatic detection of code smells using metrics and CodeT5 embeddings: a case study in C# DOI
Aleksandar Kovačević, Nikola Luburić, Јелена Сливка

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(16), P. 9203 - 9220

Published: Feb. 24, 2024

Language: Английский

Citations

4

Graph Neural Network-based Long Method and Blob Code Smell Detection DOI Creative Commons

Minnan Zhang,

Jingdong Jia, Luiz Fernando Capretz

et al.

Science of Computer Programming, Journal Year: 2025, Volume and Issue: unknown, P. 103284 - 103284

Published: Feb. 1, 2025

Language: Английский

Citations

0

Data preprocessing for machine learning based code smell detection: A systematic literature review DOI
Fábio do Rosario Santos, Ricardo Choren Noya

Information and Software Technology, Journal Year: 2025, Volume and Issue: unknown, P. 107752 - 107752

Published: April 1, 2025

Language: Английский

Citations

0

CBReT: A Cluster-Based Resampling Technique for dealing with imbalanced data in code smell prediction DOI

Praveen Singh Thakur,

Mahipal Jadeja, Satyendra Singh Chouhan

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 286, P. 111390 - 111390

Published: Jan. 21, 2024

Language: Английский

Citations

3

Small-scale aircraft detection in remote sensing images based on Faster-RCNN DOI
Yang Zhang, Chenglong Song, Dongwen Zhang

et al.

Multimedia Tools and Applications, Journal Year: 2022, Volume and Issue: 81(13), P. 18091 - 18103

Published: March 8, 2022

Language: Английский

Citations

14