Skip to main content
SpringerLink
Log in

Theoretical and Empirical Validation of Coupling Metrics for Object-Oriented Data Warehouse Design

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The conceptual model of a data warehouse can be used to determine its quality during the early stages of design. Metrics have been proposed in the past to quantify the structural complexity of these models. A majority of these metrics focus on the internal quality attributes of size and complexity. Unfortunately, not many measures have been proposed to assess the magnitude of coupling in the data warehouse multidimensional models. Coupling has a significant impact on the complexity and, in turn, quality of these models. In our previous work, we had put forward measures to determine the scope of inheritance and aggregation coupling between classes present in the object-oriented conceptual model of the data warehouse. The proposed measures take conformed dimensions into account, which is a notable feature of the data warehouse. However, the proposed metrics had not been validated. Therefore, the main aim of this study is to corroborate the proposed coupling metrics theoretically against Briand’s property-based framework, as well as empirically, using advanced statistical and machine learning techniques. The results indicate that the metrics are well-founded coupling measures and hence significantly contribute towards the structural complexity of the models which further impacts their understandability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Inmon, W.H.: Building the Data Warehouse. Wiley, Hoboken (2005)

    Google Scholar 

  2. Bouzeghoub, M.; Kedad, Z.: Quality in data warehousing. In: Piattini, M.G.; Calero, C.; Genero, M. (eds.) Information and Database Quality, pp. 163–198. Kluwer Academic Publishers, Dordrecht (2002)

  3. Calero, C.; Pascual, C.; Piattini, M.; Serrano, M.A.: Towards data warehouse quality metrics. In: Proceedings of the International Workshop on Design and Management of Data Warehouses, pp. 1–10 (2001)

  4. Genero, M.; Piattini, M.; Calero, C.: Early measures for UML class diagrams. L’Objet 6, 489–515 (2000)

    Google Scholar 

  5. Kchaou, D.; Bouassida, N.; Ben-Abdallah, H.: Managing the impact of UML design changes on their consistency and quality. Arab. J. Sci. Eng. 41, 2863–2881 (2016)

    Article  Google Scholar 

  6. Briand, L.C.; Wust, J.; Ikonomovski, S.V.; Lounis, H.: Investigating quality factors in object-oriented designs: an industrial case study. In: Proceedings of the 1999 International Conference on Software Engineering. pp. 345–354 (1999)

  7. Sabharwal, S.; Nagpal, S.; Aggarwal, G.: Coupling metrics for object-oriented data warehouse design. In: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 918–922 (2015)

  8. Briand, L.; Devanbu, P.; Melo, W.: An investigation into coupling measures for C++. In: Proceedings of the (19th) International Conference on Software Engineering, pp. 412–421 (1997)

  9. Briand, L.C.; Morasca, S.; Basili, V.R.: Property-based software engineering measurement. IEEE Trans. Softw. Eng. 22, 68–86 (1996)

    Article  Google Scholar 

  10. Catal, C.; Sevim, U.; Diri, B.: Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm. Expert Syst. Appl. 38, 2347–2353 (2011)

    Article  Google Scholar 

  11. Lanubile, F.; Visaggio, G.: Evaluating predictive quality models derived from software measures: lessons learned. J. Syst. Softw. 38, 225–234 (1997)

    Article  Google Scholar 

  12. Gyimothy, T.; Ferenc, R.; Siket, I.: Empirical validation of object-oriented metrics on open source software for fault prediction. Softw. Eng. IEEE Trans. 31, 897–910 (2005)

    Article  Google Scholar 

  13. Basili, V.R.; Briand, L.C.; Melo, W.L.: A validation of object-oriented design metrics as quality indicators. IEEE Trans. Softw. Eng. 22, 751–761 (1996)

    Article  Google Scholar 

  14. Serrano, M.; Calero, C.; Piattini, M.: Validating metrics for data warehouses. IEE Proc.-Softw. 149, 161–166 (2002)

    Article  Google Scholar 

  15. Serrano, M.A.; Calero, C.; Sahraoui, H.A.; Piattini, M.: Empirical studies to assess the understandability of data warehouse schemas using structural metrics. Softw. Qual. J. 16, 79–106 (2008)

  16. Serrano, M.; Calero, C.; Piattini, M.: An experimental replication with data warehouse metrics. Int. J. Data Warehous. Min. 1, 1–21 (2005)

    Article  Google Scholar 

  17. Gosain, A.; Sabharwal, S.; Nagpal, S.: Assessment of quality of data warehouse multidimensional model. Int. J. Inf. Qual. 2, 344–358 (2011)

    Article  Google Scholar 

  18. Gaur, H.; Kumar, M.: Assessing the understandability of a data warehouse logical model using a decision-tree approach. ACM SIGSOFT Softw. Eng. Notes 39, 1–6 (2014)

    Article  Google Scholar 

  19. Gosain, A.; Nagpal, S.; Sabharwal, S.: Validating dimension hierarchy metrics for the understandability of multidimensional models for data warehouse. IET Softw. 7, 93–103 (2013)

    Article  Google Scholar 

  20. Nagpal, S.; Gosain, A.; Sabharwal, S.: Theoretical and empirical validation of comprehensive complexity metric for multidimensional models for data warehouse. Int. J. Syst. Assur. Eng. Manag. 4, 193–204 (2013)

    Article  Google Scholar 

  21. Stevens, W.P.; Myers, G.J.; Constantive, L.L.: Structured design. IBM Syst. J. 13, 115–139 (1974)

    Article  Google Scholar 

  22. Briand, L.C.; Daly, J.W.; Wüst, J.K.: A unified framework for coupling measurement in object-oriented systems. IEEE Trans. Softw. Eng. 25, 91–121 (1999)

    Article  Google Scholar 

  23. Chidamber, S.R.; Kemerer, C.F.: A metrics suite for object-oriented design. IEEE Trans. Softw. Eng. 20, 476–493 (1994)

    Article  Google Scholar 

  24. Rathore, N.P.S.; Gupta, R.: A novel coupling metrics measure difference between inheritance and interface to find better OOP paradigm using C#. In: Proceedings of the 2011 World Congress on Information and Communication Technologies, WICT 2011, pp. 467–472 (2011)

  25. Gandhi, P.; Bhatia, P.K.: Optimization of object-oriented design using coupling metrics. Int. J. Comput. Appl. 27, 41–44 (2011)

    Google Scholar 

  26. Tegarden, D.P.; Sheetz, S.D.; Monarchi, D.E.: A software complexity model of object-oriented systems. Decis. Support Syst. 13, 241–262 (1995)

    Article  Google Scholar 

  27. Gupta, V.; Chhabra, J.K.: Package coupling measurement in object-oriented software. J. Comput. Sci. Technol. 24, 273–283 (2009)

    Article  Google Scholar 

  28. Harrison, R.; Counsell, S.J.; Nithi, R.V.: An evaluation of the MOOD set of object-oriented software metrics. IEEE Trans. Softw. Eng. 24, 491–496 (1998)

    Article  Google Scholar 

  29. Wohlin, C.; Runeson, P.; Höst, M.; Ohlsson, M.C.; Regnell, B.; Wesslén, A.: Experimentation in software engineering: an introduction. Springer, Berlin (2012)

  30. Basili, V.R.: Software modeling and measurement: the Goal/Question/Metric paradigm (1992)

  31. Basili, V.R.; Shull, F.; Lanubile, F.: Building knowledge through families of experiments. IEEE Trans. Softw. Eng. 25, 456–473 (1999)

    Article  Google Scholar 

  32. Martino, S.Di; Ferrucci, F.; Gravino, C.; Sarro, F.: A genetic algorithm to configure support vector machines for predicting fault-prone components. In: Proceedings of the 12th International Conference on Product-Focused Software Process Improvement, PROFES 2011, pp. 247–261. Springer, Berlin, Torre Canne, Italy (2011)

  33. Metz, C.E.: Basic principles of ROC analysis. Semin. Nucl. Med. 8, 283–298 (1978)

    Article  Google Scholar 

  34. Porter, A.A.; Selby, R.W.: Empirically guided software development using metric-based classification trees. IEEE Softw. 7, 46–54 (1990)

    Article  Google Scholar 

  35. Han, J.; Kamber, M.: Data mining: concepts and techniques. Morgan Kaufman, San Francisco (2007)

    MATH  Google Scholar 

  36. Serrano, M.; Trujillo, J.; Calero, C.; Piattini, M.: Metrics for data warehouse conceptual models understandability. Inf. Softw. Technol. 49, 851–870 (2007)

    Article  Google Scholar 

  37. Gosain, A.; Singh, J.: Quality metrics for data warehouse multidimensional models with focus on dimension hierarchy sharing. In: El-Alfy, E.S.; Thampi, S.; Takagi, H.; Piramuthu, S.; Hanne, T. (eds.) Advances in Intelligent Informatics, pp. 429–443. Springer International Publishing, Cham (2015)

  38. Berenguer, G.; Romero, R.; Trujillo, J.; Serrano, M.; Piattini, M.: A set of quality indicators and their corresponding metrics for conceptual models of data warehouses. In: Tjoa, A.M.; Trujillo, J. (eds.) Data Warehousing and Knowledge Discovery, pp. 95–104. Springer, Berlin (2005)

  39. Cherfi, S.S.; Prat, N.: Multidimensional schemas quality?: Assessing and balancing analyzability and simplicity. In: Proceedings of ER Workshops. Springer LNCS , pp. 140–151. Springer, Berlin (2003)

  40. Serrano, M.; Calero, C.; Trujillo, J.; Lujan, S.; Piattini, M.: Empirical validation of metrics for conceptual models of data warehouse. In: 16th International Conference on Advanced Information Systems Engineering (CAISE’04), pp. 506–520 (2004)

Download references

Author information

Authors and Affiliations

  1. Information Technology Department, NSIT, Dwarka, New Delhi, India

    Gargi Aggarwal

  2. Computer Science Department, NSIT, Dwarka, New Delhi, India

    Sangeeta Sabharwal & Sushama Nagpal

Authors
  1. Gargi Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sangeeta Sabharwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sushama Nagpal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gargi Aggarwal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aggarwal, G., Sabharwal, S. & Nagpal, S. Theoretical and Empirical Validation of Coupling Metrics for Object-Oriented Data Warehouse Design. Arab J Sci Eng 43, 675–691 (2018). https://doi.org/10.1007/s13369-017-2692-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-017-2692-y

Keywords

Search

Navigation