Study of Code Smells: A Review and Research Agenda-Reference-Cited by-同舟云学术

Study of Code Smells: A Review and Research Agenda

Published:2024-06-01 Issue:3 Volume:9 Page:472-498
ISSN:2455-7749
Container-title:International Journal of Mathematical, Engineering and Management Sciences
language:en
Short-container-title:Int. j. math. eng. manag. sci.

Author:

Tandon Stuti¹,Kumar Vijay²,Singh V. B.³

Affiliation:

1. Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India.

2. Department of Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, Uttar Pradesh, India.

3. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India.

Abstract

Code Smells have been detected, predicted and studied by researchers from several perspectives. This literature review is conducted to understand tools and algorithms used to detect and analyze code smells to summarize research agenda. 114 studies have been selected from 2009 to 2022 to conduct this review. The studies are deeply analyzed under the categorization of machine learning and non-machine learning, which are found to be 25 and 89 respectively. The studies are analyzed to gain insight into algorithms, tools and limitations of the techniques. Long Method, Feature Envy, and Duplicate Code are reported to be the most popular smells. 38% of the studies focused their research on the enhancement of tools and methods. Random Forest and JRip algorithms are found to give the best results under machine learning techniques. We extended the previous studies on code smell detection tools, reporting a total 87 tools during the review. Java is found to be the dominant programming language during the study of smells.

Publisher

Ram Arti Publishers

Reference121 articles.

1. Abebe, S.L., Haiduc, S., Tonella, P., & Marcus, A. (2009). Lexicon bad smells in software. In 2009 16th Working Conference on Reverse Engineering (pp. 95-99). IEEE. Lille, France.

2. Abidi, M., Rahman, M.S., Openja, M., & Khomh, F. (2021). Are multi-language design smells fault-prone? An empirical study. ACM Transactions on Software Engineering and Methodology, 30(3), 1-56.

3. Ahmed, I., Brindescu, C., Mannan, U.A., Jensen, C., & Sarma, A. (2017). An empirical examination of the relationship between code smells and merge conflicts. In 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 58-67). IEEE. Toronto, Canada.

4. Aivaloglou, E., & Hermans, F. (2016). How kids code and how we know: An exploratory study on the Scratch repository. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 53-61). Association for Computing Machinery. Melbourne, Australia.

5. Alazba, A., & Aljamaan, H. (2021). Code smell detection using feature selection and stacking ensemble: An empirical investigation. Information and Software Technology, 138, 106648.

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