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Profiling Prerelease Software Product and Organizational Performance

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Continuous Software Engineering

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Abstract

Background: Large software development organizations require effective means of quantifying excellence of products and improvement areas. A good quantification of excellence supports organizations in retaining market leadership. In addition, a good quantification of improvement areas is needed to continuously increase performance of products and processes.

Objective: In this chapter we present a method for developing product and organizational performance profiles. The profiles are a means of quantifying prerelease properties of products and quantifying performance of software development processes.

Method: We conducted two case studies at three companies—Ericsson, Volvo Group Truck Technology, and Volvo Car Corporation. The goal of first case study is to identify risky areas of source code. We used a focus group to elicit and evaluate measures and indicators at Ericsson. Volvo Group Truck Technology was used to validate our profiling method.

Results: The results of the first case study showed that profiling of product performance can be done by identifying risky areas of source code using combination of two measures—McCabe complexity and number of revisions of files. The results of second case study show that profiling change frequencies of models can help developers identify implicit architectural dependencies.

Conclusions: We conclude that profiling is an effective tool for supporting improvements of product and organizational performance. The key for creating useful profiles is the close collaboration between research and development organizations.

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References

  1. Glass, R.L.: Sorting out software complexity. Commun. ACM 45, 19–21 (2002)

    Google Scholar 

  2. Kahn, B.K., Strong, D.M., Wang, R.Y.: Information quality benchmarks: product and service performance. Commun. ACM 45, 184–192 (2002)

    Article  Google Scholar 

  3. Issaverdis, J.: The pursuit of excellence: Benchmarking, accreditation, best practice and auditing. In: The Encyclopedia of Ecotourism, pp. 579–594. CAB International, Oxon (2001)

    Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. Staron, M.: Critical role of measures in decision processes: managerial and technical measures in the context of large software development organizations. Inf. Softw. Technol. 54, 887–899 (2012)

    Article  Google Scholar 

  6. Sandberg, A., Pareto, L., Arts, T.: Agile collaborative research: action principles for industry–academia collaboration. IEEE Softw. 28, 74–83 (2011)

    Article  Google Scholar 

  7. Feldt, R., Staron, M., Hult, E., Liljegren, T.: Supporting software decision meetings: Heatmaps for visualising test and code measurements. Presented at the 39th Euromicro conference on software engineering and advanced applications, Santander, 2013

    Google Scholar 

  8. Robillard, P.N., Coupal, D., Coallier, F.: Profiling software through the use of metrics. Softw. Pract. Exp. 21, 507–518 (1991)

    Article  Google Scholar 

  9. Kitson, D.H., Masters, S.M.: An analysis of SEI software process assessment. In: Proceedings of the 15th International Conference on Software Engineering, pp. 68–77 (1993)

    Google Scholar 

  10. Petersen, K., Wohlin, C.: Software process improvement through the Lean Measurement (SPI-LEAM) method. J. Syst. Softw. 83, 1275–1287 (2010)

    Article  Google Scholar 

  11. Staron, M., Meding, W., Söderqvist, B.: A method for forecasting defect backlog in large streamline software development projects and its industrial evaluation. Inf. Softw. Technol. 52, 1069–1079 (2010)

    Article  Google Scholar 

  12. Wettel, R., Lanza, M.: Visual exploration of large-scale system evolution. In: 15th Working Conference on Reverse Engineering, pp. 219–228 (2008)

    Google Scholar 

  13. Voinea, L., Lukkien, J., Telea, A.: Visual assessment of software evolution. Sci. Comput. Program. 65, 222–248 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  14. Boehm, B.W.: Software engineering economics. IEEE Trans. Softw. Eng. SE-10, 4–21 (1984)

    Article  Google Scholar 

  15. Ruhe, G.: Software engineering decision support – A new paradigm for learning software organizations. In: Henninger, S., Maurer, F. (eds.) Advances in Learning Software Organizations, vol. 2640, pp. 104–113. Springer, Berlin (2003)

    Chapter  Google Scholar 

  16. Lawler, J., Kitchenham, B.: Measurement modeling technology. IEEE Softw. 20, 68–75 (2003)

    Article  Google Scholar 

  17. International Standard Organization and International Electrotechnical Commission. ISO/IEC 15939 Software Engineering – Software Measurement Process. International Standard Organization/International Electrotechnical Commission, Geneva (2007)

    Google Scholar 

  18. Elbashir, M.Z., Collier, P.A., Davern, M.J.: Measuring the effects of business intelligence systems: the relationship between business process and organizational performance. Int. J. Account. Inf. Syst. 9, 135–153 (2008)

    Article  Google Scholar 

  19. Milis, K., Mercken, R.: The use of the balanced scorecard for the evaluation of information and communication technology projects. Int. J. Proj. Manag. 22, 87–97 (2004)

    Article  Google Scholar 

  20. Visser, J.K., Sluiter, E.: Performance measures for a telecommunications company. In: AFRICON Conference, pp. 1–8 (2007)

    Google Scholar 

  21. Bourne, M., Franco-Santos, M., Cranfield School of Management. Centre for Business Performance: Corporate Performance Management. SAS Institute, Cary (2004)

    Google Scholar 

  22. Wade, D., Recardo, R.J.: Corporate Performance Management: How to Build a Better Organization Through Measurement-Driven Strategic Alignment. Butterworth–Heinemann, Boston (2001)

    Google Scholar 

  23. Staron, M.: Critical role of measures in decision processes: managerial and technical measures in the context of large software development organizations. Inf. Softw. Technol. 54(8), 887–899 (2012)

    Article  Google Scholar 

  24. 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)

    Article  Google Scholar 

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

    Chapter  Google Scholar 

  26. Kahn, B.K., Strong, D.M., Wang, R.Y.: Information quality benchmarks: product and service performance. Commun. ACM 45, 184–192 (2002)

    Article  Google Scholar 

  27. Mayer, D.M., Willshire, M.J.: A data quality engineering framework. In: International Conference on Information Quality, pp. 1–8 (1997)

    Google Scholar 

  28. Goodhue, D.L., Thompson, R.L.: Task-technology fit and individual performance. MIS Q. 19, 213–237 (1995)

    Article  Google Scholar 

  29. Serrano, M., Calero, C., Trujillo, J., Lujan-Mora, S., Piattini, M.: Empirical validation of metrics for conceptual models of data warehouses. In: International Conference on Information Systems Engineering CAiSE, pp. 506–520 (2004)

    Google Scholar 

  30. Burkhard, R., Spescha, G., Meier, M.: “A-ha!”: how to visualize strategies with complementary visualizations. In: Conference on Visualising and Presenting Indicator Systems, pp. 1–9 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Gothenburg, Gothenburg, Sweden

    Vard Antinyan & Miroslaw Staron

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

    Wilhelm Meding

Authors
  1. Vard Antinyan
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  2. Miroslaw Staron
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  3. Wilhelm Meding
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Correspondence to Vard Antinyan .

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Editors and Affiliations

  1. Chalmers University of Technology, Gothenburg, Sweden

    Jan Bosch

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© 2014 Springer International Publishing Switzerland

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Antinyan, V., Staron, M., Meding, W. (2014). Profiling Prerelease Software Product and Organizational Performance. In: Bosch, J. (eds) Continuous Software Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-11283-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-11283-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11282-4

  • Online ISBN: 978-3-319-11283-1

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