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Various Test Case Generation in Code Smell Detection Tools and Testing Methods: Review

by Deepika Tewatia, Devender Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 35
Year of Publication: 2020
Authors: Deepika Tewatia, Devender Kumar
10.5120/ijca2020919849

Deepika Tewatia, Devender Kumar . Various Test Case Generation in Code Smell Detection Tools and Testing Methods: Review. International Journal of Computer Applications. 177, 35 ( Feb 2020), 22-27. DOI=10.5120/ijca2020919849

@article{ 10.5120/ijca2020919849,
author = { Deepika Tewatia, Devender Kumar },
title = { Various Test Case Generation in Code Smell Detection Tools and Testing Methods: Review },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2020 },
volume = { 177 },
number = { 35 },
month = { Feb },
year = { 2020 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number35/31132-2020919849/ },
doi = { 10.5120/ijca2020919849 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:47:48.809484+05:30
%A Deepika Tewatia
%A Devender Kumar
%T Various Test Case Generation in Code Smell Detection Tools and Testing Methods: Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 35
%P 22-27
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Test case generation in terms of code smell refers to the features of the software that recognizes a code and design issues which make software hard to realize, evolve and preserve code. Generally, the maintenance and detection of the software applications become more difficult due to the presence of the smell. Programmers are unable to identify the source code applications and face issues to understand the source code of the project. Simultaneously, a code smell face problem to refractors and developers for upgrading and maintenance of the source code. Present, research is active in the automated detection and testing of the bad code smell. Without the knowledge of the code smell with diverse refactoring, and efficient tool make the detection of the code difficult. Particularly, the code smell in software is based on the programming of the source code, that may lead to difficulty in detection of bad code smell. This paper analysis the detection tools, method of code smells and methods for the detection of bad test code smell. The categories of different test code smells are described which includes applications, classes and different method-level code smells. Moreover, detail definition of the bad smell in source code and its types in source code is also elaborated. In addition to that, bad code smell detection is described which includes automated detection and machine learning methods for identifying the bad code smell. Additionally, the automated tools which are given as, a check style, décor, infusion, deodorant and iplasma. The detection methods are decision tree (DT), Learning group rules, Multilayer perceptron (MLP), Naïve Bayes (NB), Support Vector Machine (SVM), Radial basis system (RBS) network.

References
  1. Sharma, P and Kaur, E. A , “Design of testing framework for code smell detection (OOPS) using BFO algorithm,” International Journal of Engineering & Technology, vol 7(2.27), pp 161-166, 2018.
  2. Garousi, V and Küçük, B , “Smells in software test code: A survey of knowledge in industry and academia,” Journal of systems and software, 138, pp 52-81, 2018.
  3. Tufano, M., Palomba, F., Bavota, G., Di Penta, M., Oliveto, R., De Lucia, A and Poshyvanyk, D, “ Towards Automated Tools for Detecting Test Smells: An Empirical Investigation into the Nature of Test Smells,”.
  4. Khomh, F., Di Penta, M. and Gueheneuc, Y. G , “An exploratory study of the impact of code smells on software change-proneness,” In 2009 16th Working Conference on Reverse Engineering (pp. 75-84), IEEE, 2009.
  5. Neukirchen, H and Bisanz, M. , “Utilising code smells to detect quality problems in TTCN-3 test suites,” In Testing of Software and Communicating Systems ,pp. 228-243, Springer, Berlin, Heidelberg, 2007.
  6. Danphitsanuphan, P and Suwantada, T , “Code smell detecting tool and code smell-structure bug relationship,” In 2012 Spring Congress on Engineering and Technology (pp. 1-5). IEEE, 2012.
  7. Chatzigeorgiou, A.and Manakos, A. , “Investigating the evolution of bad smells in object-oriented code,” In 2010 Seventh International Conference on the Quality of Information and Communications Technology (pp. 106-115), IEEE, 2010.
  8. Chatzigeorgiou, A and Manakos, A , “Investigating the evolution of code smells in object-oriented systems,”  Innovations in Systems and Software Engineering, 10(1), 3-18, 2014.
  9. Grigera, J., Garrido, A and Rivero, J. M. , “A tool for detecting bad usability smells in an automatic way,” In International Conference on Web Engineering (pp. 490-493). Springer, Cham, 2014.
  10. Van Rompaey, B., Du Bois, B., Demeyer, S and Rieger, M , “On the detection of test smells: A metrics-based approach for general fixture and eager test,” IEEE Transactions on Software Engineering, 33(12), 800-817, 2007.
  11. Usha, K., Poonguzhali, N and Kavitha, E , “A quantitative approach for evaluating the effectiveness of refactoring in software development process,” In 2009 Proceeding of International Conference on Methods and Models in Computer Science (ICM2CS) (pp. 1-7). IEEE, 2009.
  12. Van Deursen, A., Moonen, L., Van Den Bergh, A and Kok, G , “ Refactoring test code,” In Proceedings of the 2nd international conference on extreme programming and flexible processes in software engineering (XP2001) ,pp. 92-95, 2001.
  13. Guerra, E. M. and Fernandes, C. T , “Refactoring test code safely,” In International Conference on Software Engineering Advances (ICSEA 2007) (pp. 44-44), IEEE, 2007.
  14. Atkins, F. J and Coe, P. J , “An ARDL bounds test of the long-run Fisher effect in the United States and Canada,” Journal of Macroeconomics, 24(2), 255-266, 2002.
  15. Chen, W. K. and Wang, J. C , “Bad smells and refactoring methods for gui test scripts,” In 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing , pp. 289-294, IEEE, . 2012.
  16. Palomba, F., Di Nucci, D., Panichella, A., Oliveto, R and De Lucia, A , “On the diffusion of test smells in automatically generated test code: An empirical study,” In Proceedings of the 9th international workshop on search-based software testing (pp. 5-14),ACM, 2016.
  17. Fontana, F. A., Dietrich, J., Walter, B., Yamashita, A and Zanoni, M , “Antipattern and code smell false positives: Preliminary conceptualization and classification,” In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) ,Vol. 1, pp. 609-613, IEEE, 2016.
  18. Danphitsanuphan, P and Suwantada, T., “Code smell detecting tool and code smell-structure bug relationship,” In 2012 Spring Congress on Engineering and Technology ,pp. 1-5, IEEE, 2012.
  19. Liu, H., Jin, J., Xu, Z., Bu, Y., Zou, Y and Zhang, L , “Deep learning based code smell detection,”  IEEE Transactions on Software Engineering, 2019.
  20. Hasanain, W., Labiche, Y and Eldh, S, “An analysis of complex industrial test code using clone analysis,” In 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS) (pp. 482-489). IEEE, 2018.
  21. Tahmid, A., Nahar, N and Sakib, K, “Understanding the evolution of code smells by observing code smell clusters,” In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) ,Vol. 4, pp. 8-11,IEEE, 2016.
  22. Guggulothu, T , “Code Smell Detection using Multilabel Classification Approach,” arXiv preprint arXiv:1902.03222, 2019.
  23. Rubin, J., Henniche, A. N., Moha, N., Bouguessa, M. and Bousbia, N , “ Sniffing Android Code Smells: An Association Rules Mining-Based Approach,” In 2019 IEEE/ACM 6th International Conference on Mobile Software Engineering and Systems (MOBILESoft), pp. 123-127,IEEE, 2019.
  24. Sae-Lim, N., Hayashi, S and Saeki, M , “How do developers select and prioritize code smells? a preliminary study,” In 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME) ,pp. 484-488), IEEE, 2017.
  25. Di Nucci, D., Palomba, F., Tamburri, D. A., Serebrenik, A and De Lucia, A , “Detecting code smells using machine learning techniques: are we there yet?,” In 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER) ,pp. 612-621, IEEE, 2018.
  26. Fernandes, E., Oliveira, J., Vale, G., Paiva, T and Figueiredo, E, “A review-based comparative study of bad smell detection tool,”. In Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering (p. 18). ACM, 2016.
  27. Di Nucci, D., Palomba, F., Tamburri, D. A., Serebrenik, A. and De Lucia, A , “Detecting code smells using machine learning techniques: are we there yet?,” In 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER) , pp. 612-621, IEEE, 2018.
  28. Fontana, F. A., Braione, P and Zanoni, M , “ Automatic detection of bad smells in code: An experimental assessment,”  Journal of Object Technology, 11(2), 5-1, 2012.
  29. Mathur, N and Reddy, Y. R , “Correctness of Semantic Code Smell Detection Tools,” In QuASoQ/WAWSE/CMCE@ APSEC (pp. 17-22), 2015.
  30. Caram, F. L., Rodrigues, B. R. D. O., Campanelli, A. S and Parreiras, F. S , “Machine Learning Techniques for Code Smells Detection: A Systematic Mapping Study,” International Journal of Software Engineering and Knowledge Engineering, 29(02), 285-316, 2019.
  31. Umesh, I. M and Srinivasan,” G. N , “A study on bad code smell,” International Journal of Latest Technology in Engineering, Management & Applied Science,vol 4(5), 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Code smell Test case generation Machine learning Bad smell detection Automated tools.

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