TY - GEN
T1 - A Machine Learning-Based Approach to Detect Web Service Design Defects
AU - Ouni, Ali
AU - Daagi, Marwa
AU - Kessentini, Marouane
AU - Bouktif, Salahn
AU - Gammoudi, Mohamed Mohsen
N1 - Funding Information:
Acknowledgment. This Work is supported by the Research Start-up (2) 2016 Grant G00002211 funded by UAE University, and based in part upon support of the National Science Foundation under Grant Number 1661422. REFERENCES
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - Design defects are symptoms of poor design and implementation solutions adopted by developers during the development of their software systems. While the research community devoted a lot of effort to studying and devising approaches for detecting the traditional design defects in object-oriented (OO) applications, little knowledge and support is available for an emerging category of Web service interface design defects. Indeed, it has been shown that service designers and developers tend to pay little attention to their service interfaces design. Such design defects can be subjectively interpreted and hence detected in different ways. In this paper, we propose a novel approach, named WS3D, using machine learning techniques that combines Support Vector Machine (SVM) and Simulated Annealing (SA) to learn from real world examples of service design defects. WS3D has been empirically evaluated on a benchmark of Web services from 14 different application domains. We compared WS3D with the state-of-theart approaches which rely on traditional declarative techniques to detect service design defects by combining metrics and threshold values. Results show that WS3D outperforms the the compared approaches in terms of accuracy with a precision and recall scores of 91% and 94%, respectively.
AB - Design defects are symptoms of poor design and implementation solutions adopted by developers during the development of their software systems. While the research community devoted a lot of effort to studying and devising approaches for detecting the traditional design defects in object-oriented (OO) applications, little knowledge and support is available for an emerging category of Web service interface design defects. Indeed, it has been shown that service designers and developers tend to pay little attention to their service interfaces design. Such design defects can be subjectively interpreted and hence detected in different ways. In this paper, we propose a novel approach, named WS3D, using machine learning techniques that combines Support Vector Machine (SVM) and Simulated Annealing (SA) to learn from real world examples of service design defects. WS3D has been empirically evaluated on a benchmark of Web services from 14 different application domains. We compared WS3D with the state-of-theart approaches which rely on traditional declarative techniques to detect service design defects by combining metrics and threshold values. Results show that WS3D outperforms the the compared approaches in terms of accuracy with a precision and recall scores of 91% and 94%, respectively.
KW - Service interface
KW - Web service design
KW - design defects
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U2 - 10.1109/ICWS.2017.62
DO - 10.1109/ICWS.2017.62
M3 - Conference contribution
AN - SCOPUS:85032359378
T3 - Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017
SP - 532
EP - 539
BT - Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017
A2 - Chen, Shiping
A2 - Altintas, Ilkay
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Web Services, ICWS 2017
Y2 - 25 June 2017 through 30 June 2017
ER -