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Continuous Software Engineering pp 183–200Cite as

Industrial Self-Healing Measurement Systems

Abstract

Automated measurement programs (i.e., placeholders for large number of measurement systems) are an efficient way of collecting, processing, and visualizing measurements in large software development companies. The measurement programs rely both on the software for data collection, analysis, and visualization—measurement systems—and humans for reporting of the data, design, and maintenance of the measurement systems. As the outcome of the measurement program—visualized measurement data—is an important input for decision making in the companies, it needs to be trustworthy and up to date. In this paper we present an experience report on development, deployment, and use of a self-healing measurement systems infrastructure at Ericsson AB. The infrastructure has been in use for a number of years and handles over 4,000 measurement systems in a fully automated way. Monitoring and self-healing of the infrastructure lead to the availability of measurement systems 24/7 and reducing the costs of managing them.

Keywords

  • Measurement System
  • Measurement Program
  • Large Software Development Companies
  • Self-healing Mechanism
  • Measurement Team

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Pfleeger, S.L., Jeffery, R., Curtis, B., Kitchenham, B.: Status report on software measurement. IEEE Softw. 14(2), 33–43 (1997)

    CrossRef  Google Scholar 

  2. International Standard Organization and International Electrotechnical Commission: ISO/IEC 15939 software engineering – software measurement process. In: International Standard Organization/International Electrotechnical Commission, Geneva (2007)

    Google Scholar 

  3. Staron, M., Meding, W.: Ensuring reliability of information provided by measurement systems. In: Software Process and Product Measurement, pp. 1–16. Springer, Berlin, Heidelberg (2009)

    Google Scholar 

  4. Robinson, H., Sharp, H.: Organisational culture and XP: three case studies. In: Proceedings of Agile Conference, pp. 49–58 (2005)

    Google Scholar 

  5. Fenton, N.E., Pfleeger, S.L.: Software Metrics: A Rigorous and Practical Approach, vol. 2. International Thomson Computer Press, London (1996)

    Google Scholar 

  6. Williams, S., Williams, N.: Business intelligence readiness: prerequisites for leveraging business intelligence to improve profits. The Profit Impact of Business Intelligence, pp. 44–64. Morgan Kaufmann, San Francisco (2007)

    Google Scholar 

  7. Thomas, J.J., Cook, K.A.: A visual analytics agenda. IEEE Comput. Graph. Appl. 26, 10–13 (2006)

    CrossRef  Google Scholar 

  8. International Bureau of Weights and Measures: International vocabulary of basic and general terms in metrology = Vocabulaire international des termes fondamentaux et généraux de métrologie, 2nd edn. International Organization for Standardization, Genève (1993)

    Google Scholar 

  9. Association, I.S.: IEEE Std 15939–2007 I.E. Systems and Software Engineering—Measurement Process. IEEE–SA (2007)

    Google Scholar 

  10. Staron, M., Meding, W., Nilsson, C.: A framework for developing measurement systems and its industrial evaluation. Inf. Softw. Technol. 51, 721–737 (2008)

    CrossRef  Google Scholar 

  11. Bostock, M., Ogievetsky, V., Heer, J.: D3 data-driven documents. IEEE Trans. Vis. Comput. Graph. 17, 2301–2309 (2011)

    CrossRef  Google Scholar 

  12. Staron, M., Meding, W., Karlsson, G., Nilsson, C.: Developing measurement systems: an industrial case study. J. Softw. Maint. Evol. Res. Pract. 23, 89–107 (2010)

    CrossRef  Google Scholar 

  13. Lee, Y.W., Strong, D.M., Kahn, B.K., Wang, R.Y.: AIMQ: a methodology for information quality assessment. Inf. Manag. 40, 133–146 (2002)

    CrossRef  Google Scholar 

  14. Bellini, P., Bruno, I., Nesi, P., Rogai, D.: Comparing fault-proneness estimation models. In: Proceedings of 10th IEEE International Conference on Engineering of Complex Computer Systems, (ICECCS 2005), pp. 205–214 (2005)

    Google Scholar 

  15. Raffo, D.M., Kellner, M.I.: Empirical analysis in software process simulation modeling. J. Syst. Soft. 53, 31–41 (2000)

    CrossRef  Google Scholar 

  16. Stensrud, E., Foss, T., Kitchenham, B., Myrtveit, I.: An empirical validation of the relationship between the magnitude of relative error and project size. In: IEEE Metrics, 2002, pp. 3–12 (2002)

    Google Scholar 

  17. Yuming, Z., Hareton, L.: Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Trans. Soft. Eng. 32, 771–789 (2006)

    CrossRef  Google Scholar 

  18. Keromytis, A.D.: Characterizing self-healing software systems. In: Proceedings of the Computer Network Security: Fourth International Conference on Mathematical Methods, Models, and Architectures for Computer Network Security, MMM-ACNS 2007, St. Petersburg, September 13–15, 2007, pp. 22–33 (2007)

    Google Scholar 

  19. Kramer, J., Magee, J.: Self-managed systems: an architectural challenge. In: Future of Software Engineering, 2007. FOSE’07, pp. 259–268 (2007)

    Google Scholar 

  20. De Lemos, R., Giese, H., Müller, H.A., Shaw, M., Andersson, J., Litoiu, M., et al.: Software engineering for self-adaptive systems: a second research roadmap. In: Software Engineering for Self-Adaptive Systems II, pp. 1–32. Springer, Berlin, Heidelberg (2013)

    Google Scholar 

  21. Gomaa, H., Hussein, M.: Software reconfiguration patterns for dynamic evolution of software architectures. In: Proceedings of Fourth Working IEEE/IFIP Conference on Software Architecture, 2004. WICSA 2004, Oslo, Norway, pp. 79–88 (2004)

    Google Scholar 

  22. Staron, M.: Critical role of measures in decision processes: managerial and technical measures in the context of large software development organizations. Inf. Softw. Technol. (2012)

    Google Scholar 

  23. Shin, M.E.: Self-healing components in robust software architecture for concurrent and distributed systems. Sci. Comput. Program. 57, 27–44 (2005)

    CrossRef  MATH  Google Scholar 

  24. Shin, M.E., An, J.H.: Self-reconfiguration in self-healing systems. In: Proceedings of the Third IEEE International Workshop on Engineering of Autonomic and Autonomous Systems, 2006. EASe 2006, pp. 89–98 (2006)

    Google Scholar 

  25. Monperrus, M., Jezequel, J.-M., Champeau, J., Hoeltzel, B.: A model-driven measurement approach. Presented at the Model Driven Engineering Languages and Systems (MODELS), Tolouse (2008)

    Google Scholar 

  26. Garcia, F., Serrano, M., Cruz-Lemus, J., Ruiz, F., Pattini, M., ALARACOS Research Group: Managing software process measurement: a meta-model based approach. Inf. Sci. 177, 2570–2586 (2007)

    CrossRef  Google Scholar 

  27. Mora, B., Garcia, F., Ruiz, F., Piattini, M.: SMML: Software Measurement Modeling Language. Presented at the 8th OOPSLA workshop on domain-specific modeling, 2008

    Google Scholar 

  28. Chirinos, L., Losavio, F., Boegh, J.: Characterizing a data model for software measurement. J. Syst. Softw. 74, 207–226 (2005)

    CrossRef  Google Scholar 

  29. van Solingen, R.: The Goal/Question/Metric Approach: A Practical Handguide for Quality Improvement of Software Development. McGraw-Hill (1999)

    Google Scholar 

  30. van Solingen, R., Berghout, E.: Integrating goal-oriented measurement in industrial software engineering: industrial experiences with and additions to the Goal/Question/Metric method (GQM). In: 7th International Software Metrics Symposium, 2001, pp. 246–258 (2001)

    Google Scholar 

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Authors and Affiliations

  1. University of Gothenburg, Gothenburg, Sweden

    Miroslaw Staron

  2. Ericsson Software Research, Ericsson AB, Gothenburg, Sweden

    Wilhelm Meding

Authors
  1. Miroslaw Staron
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  2. Wilhelm Meding
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Correspondence to Miroslaw Staron .

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Editors and Affiliations

  1. Chalmers University of Technology, Gothenburg, Sweden

    Jan Bosch

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Staron, M., Meding, W. (2014). Industrial Self-Healing Measurement Systems. In: Bosch, J. (eds) Continuous Software Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-11283-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-11283-1_15

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