Improve the accuracy and time complexity of code smell detection using SVM and Decision-Tree with Multi-Label Classification

Authors

  • Manpreet Kaur PTU, Mohali, Panjab
  • Deepinder Kaur PTU, Mohali, Panjab

DOI:

https://doi.org/10.26438/ijcse/v8i12.6669

Keywords:

CODE SMELLS, VECTOR MACHINE

Abstract

Code smell refers to an anomaly in the source code that shows violation of basic design principles such as abstraction, hierarchy, encapsulation, modularity. In this research we are using SVM (support vector Machine) and decision Tree for code smell detection. In this research we improving the accuracy and time complexity of error in code with the help of Multi-Label classification.

References

[1] Thirupathi Guggulothu, Salman Abdul Moiz_Code Smell Detection using Multilabel Classi_cation Approach,School of Computer and Information Sciences, University of Hyderabad, Hyderabad-500 046, Telangana, India

[2] DT : a detection tool to automatically detect code smell in software project Xinghua Liu1, a and Cheng Zhang2, b 1 School of Computer Science and Technology?Anhui University, China 2 School of Computer Science and Technology?Anhui University, China a xinghua.liu@ahu.edu.cn?b cheng.zhang@ahu.edu.cn

[3] Information and Software Technology,Volume 108, April 2019, Pages 115-138 “Machine learning techniques for code smell detection: A systematic literature review and meta-analysis” Muhammad IlyasAzeemabFabioPalombadLinShiabQingWangabc,Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

[4] “On the evaluation of code smells and detection tools” ,Thanis Paiva, Amanda Damasceno, Eduardo Figueiredo & Cláudio Sant’Anna ,Journal of Software Engineering Research and Development volume 5, Article number: 7 (2017)

[5]An experience report on using code smells detection tools Francesca Ar[5]Università of Milano Bicocca Department of Computer Science Milano, Italy arcelli@disco.unimib.it ,Andrea Morniroli, Raul Sormani, Alberto Tonello ,University of Milano Bicocca Department of ComputerScience Milano, Italy a.morniroli@campus.unimib.it

[6]https://becominghuman.ai/decision-trees-in-machine-learning-f362b296594a

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Published

2020-12-31
CITATION
DOI: 10.26438/ijcse/v8i12.6669
Published: 2020-12-31

How to Cite

[1]
M. Kaur and D. Kaur, “Improve the accuracy and time complexity of code smell detection using SVM and Decision-Tree with Multi-Label Classification”, Int. J. Comp. Sci. Eng., vol. 8, no. 12, pp. 66–69, Dec. 2020.

Issue

Vol. 8 No. 12 (2020): IJCSE December Edition

Section

Research Article

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