Abstract
Controlling the development of large and complex software is usually done in a quantitative manner using software metrics as the foundation for decision making. Large projects usually collect large amounts of metrics although present only a few key ones for daily project, product, and organization monitoring. The process of collecting, analyzing and presenting the key information is usually supported by automated measurement systems. Since in this process there is a transition from a lot of information (data) to a small number of indicators (metrics with decision criteria), the usual question which arises during discussions with managers is whether the stakeholders can “trust” the indicators w.r.t. the correctness of information and its timeliness. In this paper we present a method for addressing this question by assessing information quality for ISO/IEC 15939-based measurement systems. The method is realized and used in measurement systems at one of the units of Ericsson. In the paper, we also provide a short summary of the evaluation of this method through its use at Ericsson.
Keywords
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Umarji, M., Emurian, H.: Acceptance issues in metrics program implementation. In: 11th IEEE International Symposium Software Metrics (2005)
Iversen, J., Iversen, J., Mathiassen, L.: Lessons from implementing a software metrics program. In: Proceedings of the 33rd Annual Hawaii International Conference (2000)
Kilpi, T.: Implementing a Sw. Metrics Program at Nokia. IEEE Sw. 18(6), 72–77 (2001)
De Panfilis, S., Kitchenham, B., Morfuni, N.: Experiences introducing a measurement program. Computer Standards & Interfaces 21(2), 165–166 (1999)
Kitchenham, B., Linkman, S.: Design metrics in practice. Information and Software Technology 32(4), 304–310 (1990)
Garcia, F., et al.: Towards a consistent terminology for software measurement. Information and Software Technology 48(8), 631–644 (2006)
ISO/IEC International vocabulary of basic and general terms in metrology, 2nd edn., 59 p. ISO, Genève, Switzerland (1993)
Yingxu, W.: The measurement theory for software engineering. In: Canadian Conference on Electrical and Computer Engineering, 2003. IEEE CCECE 2003 (2003)
International Standard Organization, Software product evaluation 14598-1:1999 (1999)
International Standard Organization and International Electrotechnical Commission, ISO/IEC 15939 Software engineering – Software measurement process, Geneva (2007)
Staron, M., Meding, W., Nilsson, C.: A Framework for Developing Measurement Systems and Its Industrial Evaluation. Inf. and Sw. Technology 51(4), 721–737 (2008)
Lee, Y.W., et al.: AIMQ: a methodology for information quality assessment. Information & Management 40(2), 133–146 (2002)
Kahn, B.K., Strong, D.M., Wang, R.Y.: Information Quality Benchmarks: Product and Service Performance. Communications of the ACM 45(5), 184–192 (2002)
Mayer, D.M., Willshire, M.J.: A Data Quality Engineering Framework. In: International Conference on Information Quality (1997)
Goodhue, D.L., Thompson, R.L.: Task-technology fit and individual performance. MIS Quarterly 19(2), 213–237 (1995)
Serrano, M., Calero, C., Trujillo, J., Luján-Mora, S., Piattini, M.: Empirical Validation of Metrics for Conceptual Models of Data Warehouses. In: Persson, A., Stirna, J. (eds.) CAiSE 2004. LNCS, vol. 3084, pp. 506–520. Springer, Heidelberg (2004)
Price, R., Shanks, G.: A semiotic information quality framework: development and comparative analysis. Journal of Information Technology 2005(20), 88–102 (2005)
Caballero, I., et al.: A Data Quality Meas. Inf. Model Based On ISO/IEC 15939 (2007)
Berry, M., Jeffery, R., Aurum, A.: Assessment of software measurement: an information quality study. In: Proceedings of 10th Int. Symposium on Software Metrics (2004)
Bellini, P., et al.: Comparing fault-proneness estimation models (2005)
Raffo, D.M., Kellner, M.I.: Empirical analysis in software process simulation modeling. Journal of Systems and Software 53(1), 31–41 (2000)
Stensrud, E., et al.: An empirical validation of the relationship between the magnitude of relative error and project size (2002)
Yuming, Z., Hareton, L.: Emp. Analysis of OO Design Metrics for Predicting High and Low Severity Faults. IEEE Trans. on Sw. Eng. 32(10), 771–789 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Staron, M., Meding, W. (2009). Ensuring Reliability of Information Provided by Measurement Systems. In: Abran, A., Braungarten, R., Dumke, R.R., Cuadrado-Gallego, J.J., Brunekreef, J. (eds) Software Process and Product Measurement. IWSM 2009. Lecture Notes in Computer Science, vol 5891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05415-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-05415-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05414-3
Online ISBN: 978-3-642-05415-0
eBook Packages: Computer ScienceComputer Science (R0)