Web service QoS prediction using improved software source code metrics


Autoři: Sarathkumar Rangarajan aff001;  Huai Liu aff002;  Hua Wang aff001
Působiště autorů: Victoria University, Melbourne, Australia aff001;  Swinburne University of Technology, Melbourne, Australia aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226867

Souhrn

Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services’ QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics.

Klíčová slova:

Computer software – Forecasting – Language – Linear regression analysis – Machine learning – Principal component analysis – Software development – Source code


Zdroje

1. Khalil F, Li J, Wang H. An integrated model for next page access prediction. IJ Knowledge and Web Intelligence. 2009;1(1/2):48–80. doi: 10.1504/IJKWI.2009.027925

2. Khalil F, Wang H, Li J. Integrating markov model with clustering for predicting web page accesses. In: Proceeding of the 13th Australasian World Wide Web Conference (AusWeb07). AusWeb; 2007. p. 63–74.

3. Al-Shammary D, Khalil I, Tari Z, Zomaya AY. Fractal self-similarity measurements based clustering technique for SOAP Web messages. Journal of Parallel and Distributed Computing. 2013;73(5):664–676. doi: 10.1016/j.jpdc.2013.01.005

4. Li J, Liu C, Zhou R, Wang W. XML keyword search with promising result type recommendations. World wide web. 2014;17(1):127–159. doi: 10.1007/s11280-012-0198-9

5. Peng M, Zeng G, Sun Z, Huang J, Wang H, Tian G. Personalized app recommendation based on app permissions. World Wide Web. 2018;21(1):89–104. doi: 10.1007/s11280-017-0456-y

6. Li J, Liu C, Xu J. XBridge-Mobile: efficient XML keyword search on mobile web data. Computing. 2014;96(7):631–650. doi: 10.1007/s00607-013-0315-3

7. Li M, Sun X, Wang H, Zhang Y, Zhang J. Privacy-aware access control with trust management in web service. World Wide Web. 2011;14(4):407–430. doi: 10.1007/s11280-011-0114-8

8. Su K, Xiao B, Liu B, Zhang H, Zhang Z. TAP: a personalized trust-aware QoS prediction approach for web service recommendation. Knowledge-Based Systems. 2017;115:55–65. doi: 10.1016/j.knosys.2016.09.033

9. Huang X. Usageqos: Estimating the qos of web services through online user communities. ACM Transactions on the Web (TWEB). 2013;8(1):1. doi: 10.1145/2532635

10. Huang X, Huang W, Lai W. Uip: Estimating true rating scores of services through online user communities. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE; 2016. p. 1–7.

11. Chen Z, Shen L, Li F, You D. Your neighbors alleviate cold-start: On geographical neighborhood influence to collaborative web service QoS prediction. Knowledge-Based Systems. 2017;138:188–201. doi: 10.1016/j.knosys.2017.10.001

12. Gupta R, Kamal R, Suman U. A QoS-supported approach using fault detection and tolerance for achieving reliability in dynamic orchestration of web services. International Journal of Information Technology. 2018;10(1):71–81. doi: 10.1007/s41870-017-0066-z

13. Li S, Wen J, Luo F, Gao M, Zeng J, Dong ZY. A New QoS-Aware Web Service Recommendation System Based on Contextual Feature Recognition at Server-Side. IEEE Transactions on Network and Service Management. 2017;14(2):332–342. doi: 10.1109/TNSM.2017.2693324

14. Zhang Y, Lyu MR. QoS-Aware Web Service Searching. In: QoS Prediction in Cloud and Service Computing. Springer; 2017. p. 81–103.

15. Xu J, Zhu C, Xie Q. An Online Prediction Framework for Dynamic Service-Generated QoS Big Data. In: International Conference on Database Systems for Advanced Applications. Springer; 2017. p. 60–74.

16. Lin SY, Lai CH, Wu CH, Lo CC. A trustworthy QoS-based collaborative filtering approach for web service discovery. Journal of Systems and Software. 2014;93:217–228. doi: 10.1016/j.jss.2014.01.036

17. Chen Z, Shen L, You D, Li F, Ma C. Alleviating Data Sparsity in Web Service QoS Prediction by Capturing Region Context Influence. In: International Conference on Collaborative Computing: Networking, Applications and Worksharing. Springer; 2016. p. 540–556.

18. Alexander H, Khalil I, Cameron C, Tari Z, Zomaya A. Cooperative web caching using dynamic interest-tagged filtered bloom filters. IEEE Transactions on Parallel and Distributed Systems. 2014;26(11):2956–2969. doi: 10.1109/TPDS.2014.2363458

19. Coscia JLO, Crasso M, Mateos C, Zunino A, Misra S. Predicting web service maintainability via object-oriented metrics: a statistics-based approach. In: International Conference on Computational Science and Its Applications. Springer; 2012. p. 29–39.

20. Mordal K, Anquetil N, Laval J, Serebrenik A, Vasilescu B, Ducasse S. Software quality metrics aggregation in industry. Journal of Software: Evolution and Process. 2013;25(10):1117–1135.

21. Chidamber SR, Kemerer CF. A metrics suite for object oriented design. IEEE Transactions on software engineering. 1994;20(6):476–493. doi: 10.1109/32.295895

22. Sneed HM. Measuring web service interfaces. In: Web Systems Evolution (WSE), 2010 12th IEEE International Symposium on. IEEE; 2010. p. 111–115.

23. Baski D, Misra S. Metrics suite for maintainability of extensible markup language Web Services. IET software. 2011;5(3):320–341. doi: 10.1049/iet-sen.2010.0089

24. Mateos C, Crasso M, Zunino A, Coscia JLO. Detecting WSDL bad practices in code–first Web Services. International Journal of Web and Grid Services. 2011;7(4):357–387. doi: 10.1504/IJWGS.2011.044710

25. Kumar L, Kumar M, Rath SK. Maintainability prediction of web service using support vector machine with various kernel methods. International Journal of System Assurance Engineering and Management. 2017;8(2):205–222. doi: 10.1007/s13198-016-0415-5

26. Chen Z, Shen L, Li F. Exploiting Web service geographical neighborhood for collaborative QoS prediction. Future Generation Computer Systems. 2017;68:248–259. doi: 10.1016/j.future.2016.09.022

27. He P, Zhu J, Xu J, Lyu MR. A Hierarchical Matrix Factorization Approach for Location-Based Web Service QoS Prediction. In: Service Oriented System Engineering (SOSE), 2014 IEEE 8th International Symposium on. IEEE; 2014. p. 290–295.

28. Zhu J, He P, Zheng Z, Lyu MR. A privacy-preserving QoS prediction framework for web service recommendation. In: Web Services (ICWS), 2015 IEEE International Conference on. IEEE; 2015. p. 241–248.

29. Charrad M, Ayadi NY, Ahmed MB. A semantic and QoS-aware broker for service discovery. Journal of Research and Practice in Information Technology. 2012;44(4):387.

30. Kumar L, Krishna A, Rath SK. The impact of feature selection on maintainability prediction of service-oriented applications. Service Oriented Computing and Applications. 2017;11(2):137–161. doi: 10.1007/s11761-016-0202-9

31. Romano D, Pinzger M. Using source code metrics to predict change-prone java interfaces. In: 2011 27th IEEE International Conference on Software Maintenance (ICSM). IEEE; 2011. p. 303–312.

32. Lanza M, Marinescu R. Object-oriented metrics in practice: using software metrics to characterize, evaluate, and improve the design of object-oriented systems. Springer Science & Business Media; 2007.

33. Suh SD, Neamtiu I. Studying software evolution for taming software complexity. In: 2010 21st Australian Software Engineering Conference. IEEE; 2010. p. 3–12.

34. Lumpe M, Mahmud S, Vasa R. On the use of properties in java applications. In: 2010 21st Australian Software Engineering Conference. IEEE; 2010. p. 235–244.

35. Shatnawi R, Li W, Swain J, Newman T. Finding software metrics threshold values using ROC curves. Journal of software maintenance and evolution: Research and practice. 2010;22(1):1–16. doi: 10.1002/smr.404

36. Barkmann H, Lincke R, Löwe W. Quantitative evaluation of software quality metrics in open-source projects. In: 2009 International Conference on Advanced Information Networking and Applications Workshops. IEEE; 2009. p. 1067–1072.

37. Serebrenik A, van den Brand M. Theil index for aggregation of software metrics values. In: Software Maintenance (ICSM), 2010 IEEE International Conference on. IEEE; 2010. p. 1–9.

38. Vasa R, Lumpe M, Branch P, Nierstrasz O. Comparative analysis of evolving software systems using the Gini coefficient. In: 2009 IEEE International Conference on Software Maintenance. IEEE; 2009. p. 179–188.

39. Theil H. Economics and information theory. Studies in mathematical and managerial economics. North-Holland Pub. Co.; 1967. Available from: https://books.google.com.au/books?id=VVNVAAAAMAAJ.


Článek vyšel v časopise

PLOS One


2020 Číslo 1