Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

Classification and Analysis of Static Metric Based Antipattern Detection in Service Computing

Author(s): Shivani Saluja* and Usha Batra

Volume 13, Issue 4, 2020

Page: [747 - 757] Pages: 11

DOI: 10.2174/2213275912666190809105751

Price: $65

Abstract

Background: Design Pattern is regarded as an essential component for enhancement of system design which can further improve the reusability and maintainability whereas antipattern degrades the quality of the program. Antipatterns are sub-optimal implementation choices which initially appears to be a good concept but later on leads to low code maintainability and higher maintenance costs.

Objective: The identification of antipatterns which lead to performance degradation plays an important role in the reduction of expensive work and cost involved in maintenance, redesign, reimplementation, and redeployment. The main motivation is to refactor the source code in order to reduce maintenance efforts. Antipatterns impact reliability, testability and maintainability, but they still lack clear identification because of different interpretations and definition of each antipattern. There is a need for a benchmark to analyze the result generated by antipatterns.

Methods: Static and dynamic analysis individually do not suffice for antipattern .A hybrid approach is proposed by combining rule based static analysis with dynamic run time analysis. Before applying the hybrid approach a simple manual validation was done to exclude the type of input which had the least probability of containing antipattern. The approach aims at optimizing the results by inclusion of response time metric measure which can be evaluated at runtime execution of the web service.

Results and Conclusion: The paper focusses on detection of antipatterns from web services based on code level and interface level static metrics .Only a brief overview of dynamic approach for detection is proposed. The future scope of this paper will focus on detection of antipattern based on more number of dynamic metrics and also a comparative analysis of the results generated from static, dynamic and hybrid approach.

Keywords: Antipatterns, software quality, service computing, performance, dynamic analysis, hybrid approach.

Graphical Abstract

[1]
J. Yli-Huumo, A. Maglyas, and K. Smolander, "How do software development teams manage technical debt?–An empirical study", J. Syst. Softw., vol. 120, pp. 195-218, 2016.
[http://dx.doi.org/10.1016/j.jss.2016.05.018]
[2]
M. Aneke, and M. Wang, "Energy storage technologies and real life applications–A state of the art review", Appl. Energy, vol. 179, pp. 350-377, 2016.
[http://dx.doi.org/10.1016/j.apenergy.2016.06.097]
[3]
A. Koenig, “Patterns and antipatterns”, In The patterns handbook: techniques, strategies, and applications , vol. 13 . . 1998, p. 383.
[4]
K. Beck, M. Fowler, and G. Beck, “Bad smells in code”, In Refactoring: Improving the design of existing code , vol. 75. . 1999, p. 88.
[5]
J. Sheng, Y. Wang, P. Hu, and B. Wang, "A novel approach to describing and detecting performance anti-patterns", J. Phys. Conf. Ser. IOP Publishing, vol. 887, no. 1, p. 012019, , . 2017
[http://dx.doi.org/10.1088/1742-6596/887/1/012019]
[6]
F. Palma, N. Moha, G. Tremblay, and Y.G. Guéhéneuc, Specification and Detection of SOA Antipatterns in Web Services.Software Architecture. ECSA 2014. Lecture Notes in Computer Science.vol. 8627, Springer: Cham, , 2014
[http://dx.doi.org/10.1007/978-3-319-09970-5_6]
[7]
F. Palma, N. Moha, and Y.G. Guéhéneuc, "UniDoSA: The Unified Specification and Detection of Service Antipatterns", IEEE Trans. Softw. Eng., vol. 55, no. 10, . 2018, pp.1024- 1053.
[8]
M. Kessentini, W. Kessentini, H. Sahraoui, M. Boukadoum, and A. Ouni, "Design defects detection and correction by example", In , IEEE 19th Int. Conf. on Program Comprehension, pp. 81-90, 2011.
[http://dx.doi.org/10.1109/ICPC.2011.22]
[9]
A. Ouni, M. Kessentini, K. Inoue, and M.O. Cinnéide, "Search-based web service antipatterns detection", IEEE Trans. Serv. Comput., vol. 10, no. 4, pp. 603-617, 2017.
[http://dx.doi.org/10.1109/TSC.2015.2502595]
[10]
Reference available from: , http://eil.cs.txstate.edu/ServiceXplorer/results.php
[11]
T. Erl, "SOA Principles of Service Design."Prentice Hall Press, Upper Saddle River , 2008
[12]
Reference available from: , www.scitepress.org
[13]
Reference available from: , https://docs.microsoft.com/en-us/azure/architecture/patterns/
[14]
L.D.C. Santos, R.M. Saraiva, M. Perkusich, H.O. Almeida, and A. Perkusich, "An empirical study on the influence of context in computing thresholds for Chidamber and Kemerer metrics", In , The 29th International Conference on Software Engineering & Knowledge Engineering , 2017, pp. 357 -362
[http://dx.doi.org/10.18293/SEKE2017-044]
[15]
C. Trubiani, A. Bran, A. van Hoorn, A. Avritzer, and H. Knoche, "Exploiting load testing and profiling for Performance Antipattern Detection", Inf. Softw. Technol., vol. 95, pp. 329-345, 2018.
[http://dx.doi.org/10.1016/j.infsof.2017.11.016]
[16]
M. Abbes, F. Khomh, Y.G. Gueheneuc, and G. Antoniol, "An empirical study of the impact of two antipatterns, blob and spaghetti code, on program comprehension."In , 15th ECSMR., IEEE, 2011, pp. 181-190.
[http://dx.doi.org/10.1109/CSMR.2011.24]
[17]
N. Roperia, "J Smell: A Bad Smell detection tool for Java Systems."California State University: Long Beach, , 2009.
[18]
H. Hamza, S. Counsell, G. Loizou, and T. Hall, "Code smell eradication and associated refactoring", In , Proceedings of the European Computing Conference (ECC), 2008.

Rights & Permissions Print Cite
Article Metrics
3
© 2024 Bentham Science Publishers | Privacy Policy