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Multi-criteria Web Services Selection: Balancing the Quality of Design and Quality of Service

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Published:28 September 2021Publication History
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Abstract

Web service composition allows developers to create applications via reusing available services that are interoperable to each other. The process of selecting relevant Web services for a composite service satisfying the developer requirements is commonly acknowledged to be hard and challenging, especially with the exponentially increasing number of available Web services on the Internet. The majority of existing approaches on Web Services Selection are merely based on the Quality of Service (QoS) as a basic criterion to guide the selection process. However, existing approaches tend to ignore the service design quality, which plays a crucial role in discovering, understanding, and reusing service functionalities. Indeed, poorly designed Web service interfaces result in service anti-patterns, which are symptoms of bad design and implementation practices. The existence of anti-pattern instances in Web service interfaces typically complicates their reuse in real-world service-based systems and may lead to several maintenance and evolution problems. To address this issue, we introduce a new approach based on the Multi-Objective and Optimization on the basis of Ratio Analysis method (MOORA) as a multi-criteria decision making (MCDM) method to select Web services based on a combination of their (1) QoS attributes and (2) QoS design. The proposed approach aims to help developers to maintain the soundness and quality of their service composite development processes. We conduct a quantitative and qualitative empirical study to evaluate our approach on a Quality of Web Service dataset. We compare our MOORA-based approach against four commonly used MCDM methods as well as a recent state-of-the-art Web service selection approach. The obtained results show that our approach outperforms state-of-the-art approaches by significantly improving the service selection quality of top-k selected services while providing the best trade-off between both service design quality and desired QoS values. Furthermore, we conducted a qualitative evaluation with developers. The obtained results provide evidence that our approach generates a good trade-off for what developers need regarding both QoS and quality of design. Our selection approach was evaluated as “relevant” from developers point of view, in improving the service selection task with an average score of 3.93, compared to an average of 2.62 for the traditional QoS-based approach.

References

  1. [n.d.]. ISO: ISO/IEC 25010: 2011, Systems and software engineering—Systems and software Quality Requirements and Evaluation (SQuaRE)—System and software quality models. 34 pages.Google ScholarGoogle Scholar
  2. Github. 2019. Dataset used in the study. Retrieved from https://github.com/ouniali/WSS.Google ScholarGoogle Scholar
  3. Esra Aytaç Adalı and Ayşegül Tuş Işık. 2017. The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem. J. Industr. Eng. Int. 13, 2 (2017), 229–237.Google ScholarGoogle ScholarCross RefCross Ref
  4. Eyhab Al-Masri and Qusay H. Mahmoud. 2007. Qos-based discovery and ranking of web services. In Proceedings of the 16th International Conference on Computer Communications and Networks (ICCCN’07). IEEE, 529–534.Google ScholarGoogle ScholarCross RefCross Ref
  5. Hamzeh Mohammd Alabool and Ahmad Kamil Mahmood. 2013. Trust-based service selection in public cloud computing using fuzzy modified VIKOR method. Austral. J. Basic Appl. Sci. 7, 9 (2013), 211–220.Google ScholarGoogle Scholar
  6. Dionysis Athanasopoulos, Apostolos V. Zarras, George Miskos, Valerie Issarny, and Panos Vassiliadis. 2014. Cohesion-driven decomposition of service interfaces without access to source code. IEEE Trans. Serv. Comput. 8, 4 (2014), 550–562.Google ScholarGoogle ScholarCross RefCross Ref
  7. Nhien Pham Hoang Bao, Shuo Xiong, and Hiroyuki Iida. 2017. Reaper tournament system. In Proceedings of the International Conference on Intelligent Technologies for Interactive Entertainment. 16–33.Google ScholarGoogle Scholar
  8. S. Bharathan, Chandrasekharan Rajendran, and R. P. Sundarraj. 2017. Penalty based mathematical models for web service composition in a geo-distributed cloud environment. In Proceedings of the IEEE International Conference on Web Services (ICWS’17). 886–889.Google ScholarGoogle Scholar
  9. Sabrine Boukharata, Ali Ouni, Marouane Kessentini, Salah Bouktif, and Hanzhang Wang. 2019. Improving web service interfaces modularity using multi-objective optimization. Auto. Softw. Eng. 26, 2 (2019), 275–312. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Jean-Pierre Brans, Ph Vincke, and Bertrand Mareschal. 1986. How to select and how to rank projects: The PROMETHEE method. Eur. J. Operation. Res. 24, 2 (1986), 228–238.Google ScholarGoogle ScholarCross RefCross Ref
  11. Willem K. Brauers and Edmundas Kazimieras Zavadskas. 2006. The MOORA method and its application to privatization in a transition economy. Control Cybernet. 35 (2006), 445–469.Google ScholarGoogle Scholar
  12. Willem Karel M. Brauers and Edmundas Kazimieras Zavadskas. 2012. Robustness of MULTIMOORA: A method for multi-objective optimization. Informatica 23, 1 (2012), 1–25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. William H. Brown, Raphael C. Malveau, Hays W. McCormick, and Thomas J. Mowbray. 1998. AntiPatterns: Refactoring Software, Architectures, and Projects in Crisis. John Wiley & Sons. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Zahira Chouiref, Abdelkader Belkhir, Karim Benouaret, and Allel Hadjali. 2016. A fuzzy framework for efficient user-centric Web service selection. Appl. Soft Comput. 41 (2016), 51–65. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Norman Cliff. 1993. Dominance statistics: Ordinal analyses to answer ordinal questions.Psychol. Bull. 114, 3 (1993), 494.Google ScholarGoogle ScholarCross RefCross Ref
  16. José Luis Ordiales Coscia, Cristian Mateos, Marco Crasso, and Alejandro Zunino. 2011. Avoiding wsdl bad practices in code-first web services. In Proceedings of the 12th Argentine Symposium on Software Engineering (ASSE’11). 1–12.Google ScholarGoogle Scholar
  17. Marwa Daagi, Ali Ouniy, Marouane Kessentini, Mohamed Mohsen Gammoudi, and Salah Bouktif. 2017. Web service interface decomposition using formal concept analysis. In Proceedings of the IEEE International Conference on Web Services (ICWS’17). 172–179.Google ScholarGoogle ScholarCross RefCross Ref
  18. Afaf Dadda and Ibrahim Ouhbi. 2014. A decision support system for renewable energy plant projects. In Proceedings of the International Conference on Next Generation Networks and Services (NGNS’14). 356–362.Google ScholarGoogle ScholarCross RefCross Ref
  19. Bill Dudney, Stephen Asbury, Joseph K. Krozak, and Kevin Wittkopf. 2003. J2EE Antipatterns. John Wiley & Sons. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. John Estdale. 2016. App stores & ISO/IEC 25000: product certification at last. In Proceedings of the 24th Systems Quality Conference: Trends and Practices (SQM’16). 37–48.Google ScholarGoogle Scholar
  21. Hong Feng et al. 2011. Multiple attribute decision making with intervals for QoS-based web service selection. In Proceedings of the IEEE 13th International Conference on Communication Technology. 1041–1045.Google ScholarGoogle Scholar
  22. Yan Guo and Shangguang Wang. 2016. Skyline service selection based on QoS prediction. In Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER’16). 150–151.Google ScholarGoogle ScholarCross RefCross Ref
  23. Zhenan He and Gary G. Yen. 2016. Many-objective evolutionary algorithm: Objective space reduction and diversity improvement. IEEE Trans. Evolution. Comput. 20, 1 (2016), 145–160.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Caroline Herssens, Ivan J. Jureta, and Stéphane Faulkner. 2008. Dealing with quality tradeoffs during service selection. In Proceedings of the International Conference on Autonomic Computing. 77–86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Matías Hirsch, Ana Rodriguez, Juan Manuel Rodriguez, Cristian Mateos, and Alejandro Zunino. 2018. Spotting and Removing WSDL anti-pattern root causes in code-first web services using NLP techniques: A thorough validation of impact on service discoverability. Comput. Stand. Interfaces 56 (2018), 116–133. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Ching-Lai Hwang and Kwangsun Yoon. 1981. Methods for multiple attribute decision making. In Multiple Attribute Decision Making. Springer, 58–191.Google ScholarGoogle Scholar
  27. San-Yih Hwang, Chien-Ching Hsu, and Chien-Hsiang Lee. 2015. Service selection for web services with probabilistic QoS. IEEE Trans. Serv. Comput. 8, 1 (2015), 1–1.Google ScholarGoogle ScholarCross RefCross Ref
  28. Raed Karim, Chen Ding, and Chi-Hung Chi. 2011. An enhanced PROMETHEE model for QoS-based web service selection. In Proceedings of the IEEE International Conference on Services Computing (SCC’11). 536–543. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Mehdi Keshavarz Ghorabaee, Edmundas Kazimieras Zavadskas, Laya Olfat, and Zenonas Turskis. 2015. Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26, 3 (2015), 435–451.Google ScholarGoogle ScholarCross RefCross Ref
  30. Mojtaba Khezrian, Wan M. N. Wan Kadir, Suhaimi Ibrahim, Alaeddin Kalantari, et al. 2012. A hybrid approach for web service selection. Int. J. Comput. Eng. Res. 2, 1 (2012), 190–198.Google ScholarGoogle Scholar
  31. Jaroslav Kral and Michal Zemlicka. 2007. The most important service-oriented antipatterns. In Proceedings of the International Conference Software Engineering Advances. 29–29. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Jaroslav Král and Michal Žemlicka. 2009. Popular SOA antipatterns. In Proceedings of the Conference on Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns (COMPUTATIONWORLD’09). IEEE, 271–276. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Xinle Liang, A. Kai Qin, Ke Tang, and Kay Chen Tan. 2016. QoS-aware web service selection with internal complementarity. IEEE Trans. Serv. Comput. 12, 2 (2016), 276–289.Google ScholarGoogle ScholarCross RefCross Ref
  34. Rensis Likert. 1932. A technique for the measurement of attitudes. Arch. Psychol. 140 (1932), 5–55.Google ScholarGoogle Scholar
  35. Cristian Mateos, Marco Crasso, Juan M. Rodriguez, Alejandro Zunino, and Marcelo Campo. 2015. Measuring the impact of the approach to migration in the quality of web service interfaces. Enterprise Info. Syst. 9, 1 (2015), 58–85. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Vincent Mousseau, Luis C. Dias, and José Figueira. 2006. Dealing with inconsistent judgments in multiple criteria sorting models. 4OR 4, 2 (2006), 145–158.Google ScholarGoogle Scholar
  37. Serafim Opricovic and Gwo-Hshiung Tzeng. 2007. Extended VIKOR method in comparison with outranking methods. Eur. J. Operation. Res. 178, 2 (2007), 514–529.Google ScholarGoogle ScholarCross RefCross Ref
  38. Ali Ouni, Marwa Daagi, Marouane Kessentini, Salah Bouktif, and Mohamed Mohsen Gammoudi. 2017. A machine learning-based approach to detect Web service design defects. In Proceedings of the IEEE International Conference on Web Services (ICWS’17). 532–539.Google ScholarGoogle ScholarCross RefCross Ref
  39. Ali Ouni, Raula Gaikovina Kula, Marouane Kessentini, and Katsuro Inoue. 2015. Web service antipatterns detection using genetic programming. In Proceedings of the Annual Conference on Genetic and Evolutionary Computation (GECCO’15). ACM, New York, NY, 1351–1358. https://doi.org/10.1145/2739480.2754724 Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Ali Ouni, Marouane Kessentini, Katsuro Inoue, and Mel O. Cinnéide. 2017. Search-based web service antipatterns detection. IEEE Trans. Serv. Comput. 10, 4 (2017), 603–617.Google ScholarGoogle ScholarCross RefCross Ref
  41. Ali Ouni, Marouane Kessentini, Houari Sahraoui, Katsuro Inoue, and Kalyanmoy Deb. 2016. Multi-criteria code refactoring using search-based software engineering: An industrial case study. ACM Trans. Softw. Eng. Methodol. 25, 3 (2016), 1–53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Ali Ouni, Zouhour Salem, Katsuro Inoue, and Makram Soui. 2016. SIM: An automated approach to improve web service interface modularization. In Proceedings of the IEEE International Conference on Web Services (ICWS’16). 91–98.Google ScholarGoogle ScholarCross RefCross Ref
  43. Ali Ouni, Hanzhang Wang, Marouane Kessentini, Salah Bouktif, and Katsuro Inoue. 2018. A hybrid approach for improving the design quality of web service interfaces. ACM Trans. Internet Technol. 19, 1 (2018), 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Francis Palma, Naouel Moha, Guy Tremblay, and Yann-Gaël Guéhéneuc. 2014. Specification and detection of SOA antipatterns in web services. In Proceedings of the European Conference on Software Architecture. 58–73.Google ScholarGoogle ScholarCross RefCross Ref
  45. Mikhail Perepletchikov, Caspar Ryan, and Zahir Tari. 2010. The impact of service cohesion on the analyzability of service-oriented software. IEEE Trans. Serv. Comput. 3, 2 (2010), 89–103. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. L. Purohit and S. Kumar. 2018. A classification based web service selection approach. IEEE Trans. Serv. Comput. 14, 2 (2018), 315–328.Google ScholarGoogle ScholarCross RefCross Ref
  47. Jafar Rezaei. 2015. Best-worst multi-criteria decision-making method. Omega 53 (2015), 49–57.Google ScholarGoogle ScholarCross RefCross Ref
  48. Juan Manuel Rodriguez, Marco Crasso, Cristian Mateos, and Alejandro Zunino. 2013. Best practices for describing, consuming, and discovering web services: a comprehensive toolset. Softw.: Pract. Exp. 43, 6 (2013), 613–639.Google ScholarGoogle ScholarCross RefCross Ref
  49. Juan Manuel Rodriguez, Marco Crasso, Alejandro Zunino, and Marcelo Campo. 2010. Automatically detecting opportunities for web service descriptions improvement. In Proceedings of the Conference on e-Business, e-Services and e-Society. 139–150.Google ScholarGoogle ScholarCross RefCross Ref
  50. Jeanine Romano, Jeffrey D. Kromrey, Jesse Coraggio, and Jeff Skowronek. 2006. Appropriate statistics for ordinal level data: Should we really be using t-test and Cohen’sd for evaluating group differences on the NSSE and other surveys. In Proceedings of the Annual Meeting of the Florida Association of Institutional Research. 1–33.Google ScholarGoogle Scholar
  51. Arnon Rotem-Gal-Oz, Eric Bruno, and Udi Dahan. 2012. SOA Patterns. Manning, Shelter Island.Google ScholarGoogle Scholar
  52. Thomas L. Saaty. 2000. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process. Vol.  6. RWS Publications.Google ScholarGoogle Scholar
  53. S. Sadjadi and M. Karimi. 2018. Best-worst multi-criteria decision-making method: A robust approach. Decis. Sci. Lett. 7, 4 (2018), 323–340.Google ScholarGoogle ScholarCross RefCross Ref
  54. Islem Saidani, Ali Ouni, and Mohamed Wiem Mkaouer. 2020. Web Service API Anti-patterns Detection as a Multi-label Learning Problem. In Proceedings of the International Conference on Web Services. 114–132.Google ScholarGoogle ScholarCross RefCross Ref
  55. Walid Serrai, Abdelkrim Abdelli, Lynda Mokdad, and Youcef Hammal. 2016. An efficient approach for Web service selection. In Proceedings of the IEEE Symposium on Computers and Communication (ISCC’16). 167–172.Google ScholarGoogle ScholarCross RefCross Ref
  56. Walid Serrai, Abdelkrim Abdelli, Lynda Mokdad, and Youcef Hammal. 2017. Towards an efficient and a more accurate web service selection using MCDM methods. J. Comput. Sci. 22 (2017), 253–267.Google ScholarGoogle ScholarCross RefCross Ref
  57. Hanzhang Wang, Marouane Kessentini, Taghreed Hassouna, and Ali Ouni. 2017. On the value of quality of service attributes for detecting bad design practices. In Proceedings of the IEEE International Conference on Web Services (ICWS’17). IEEE, 341–348.Google ScholarGoogle ScholarCross RefCross Ref
  58. Hanzhang Wang, Marouane Kessentini, and Ali Ouni. 2016. Prediction of web services evolution. In Proceedings of the International Conference on Service-Oriented Computing. Springer, 282–297.Google ScholarGoogle ScholarCross RefCross Ref
  59. Hanzhang Wang, Marouane Kessentini, and Ali Ouni. 2017. Interactive refactoring of web service interfaces using computational search. IEEE Trans. Services Comput. 14, 1 (2017), 179–192.Google ScholarGoogle Scholar
  60. Shangguang Wang, Ching-Hsien Hsu, Zhongjun Liang, Qibo Sun, and Fangchun Yang. 2014. Multi-user web service selection based on multi-QoS prediction. Info. Syst. Front. 16, 1 (2014), 143–152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Frank Wilcoxon, S. K. Katti, and Roberta A. Wilcox. 1970. Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Select. Tables Math. Stat. 1 (1970), 171–259.Google ScholarGoogle Scholar
  62. David H. Wolpert, William G. Macready, et al. 1997. No free lunch theorems for optimization. IEEE Trans. Evolution. Comput. 1, 1 (1997), 67–82. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Jiajun Xu, Lin Guo, Ruxia Zhang, Yin Zhang, Hualang Hu, Fei Wang, and Zhiyuan Pei. 2017. Towards fuzzy QoS driven service selection with user requirements. In Proceedings of the International Conference on Progress in Informatics and Computing (PIC’17). 230–234.Google ScholarGoogle ScholarCross RefCross Ref
  64. Gary G. Yen and Zhenan He. 2013. Performance metric ensemble for multiobjective evolutionary algorithms. IEEE Trans. Evolution. Comput. 18, 1 (2013), 131–144.Google ScholarGoogle ScholarCross RefCross Ref
  65. Long-Chang Zhang, Zou Hua, and Yang Fang-Chun. 2011. Web service composition algorithm based on TOPSIS. J. China Univ. Posts Telecommun. 18, 4 (2011), 89–97.Google ScholarGoogle ScholarCross RefCross Ref
  66. Xinchao Zhao, Zichao Wen, and Xingmei Li. 2014. QoS-aware web service selection with negative selection algorithm. Knowl. Info. Syst. 40, 2 (2014), 349–373. Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolution. Comput. 8, 2 (2000), 173–195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Hua Zou, Longchang Zhang, Fangchun Yang, and Yao Zhao. 2010. A Web service composition algorithmic method based on TOPSIS supporting multiple decision-makers. In Proceedings of the 6th World Congress on Services. 158–159. Google ScholarGoogle ScholarDigital LibraryDigital Library

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