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Integrating human judgment and data analysis to identify factors influencing software development productivity

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Managing software development productivity and effort are key issues in software organizations. Identifying the most relevant factors influencing project performance is essential for implementing business strategies by selecting and adjusting proper improvement activities. There is, however, a large number of potential influencing factors. This paper proposes a novel approach for identifying the most relevant factors influencing software development productivity. The method elicits relevant factors by integrating data analysis and expert judgment approaches by means of a multi-criteria decision suport technique. Empirical evaluation of the method in an industrial context has indicated that it delivers a different set of factors compared to individual data- and expert-based factor selection methods. Moreover, application of the integrated method significantly improves the performance of effort estimation in terms of accuracy and precision. Finally, the study did not replicate the observation of similar investigations regarding improved estimation performance on the factor sets reduced by a data-based selection method.
Rocznik
Strony
41--59
Opis fizyczny
Bibliogr. 37 poz.
Twórcy
autor
autor
autor
autor
  • Fraunhofer Institute for Experimental Software Engineering (Germany)
Bibliografia
  • [1] D. Aha and R. L. Bankert. A comparative evaluation of sequential feature selection algorithms.In D. Fisher and H.-J. Lenz, editors, Artificial Intelligence and Statistics, chapter Chapter 4,pages 199–206. Springer-Verlag, 1996.
  • [2] M. Auer, A. Trendowicz, B. Graser, E. J. Haunschmid, and S. Biffl. Optimal project feature weights in analogy-based cost estimation: Improvement and limitations. IEEE Transactions on Software Engineering, 32(2):83–92, Fabruary 2006.Adam Trendowicz, Michael Ochs, Axel Wickenkamp, Jürgen Münch, Yasushi Ishigai, Takashi Kawaguchi
  • [3] B. W. Boehm. Software Engineering Economics. Prentice-Hall, Englewood Cliffs, NJ, 1981.
  • [4] B. W. Boehm, C. Abts, A. W. Brown, S. Chulani, B. K. Clark, E. Horowitz, R. Madachy, D. Reifer, and B. Steece. Software Cost Estimation with CoCoMo II. Prentice-Hall, 2000.
  • [5] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Advanced Books & Software. Chapman and Hall, New-York, 1984.
  • [6] L. Briand, V. Basili, and W. Thomas. A pattern recognition approach for software engineering data analysis. IEEE Transactions on Software Engineering, 18(1):931–942, November 1992.
  • [7] L. Briand and I. Wieczorek. Software resource estimation. In J. Marciniak, editor, Encyclopedia of Software Engineering, volume 2, pages 1160–1196. John Wiley & Sons, 2002.
  • [8] Z. Chen, T. Menzies, D. Port, and B. Boehm. Finding the right data for software cost modeling. IEEE Software, 22(6):38–46, November/December 2005.
  • [9] S. Conte, H. Dunsmore, and V. Shen. Software Engineering Metrics and Models. CA: Benjamin Cummings, 1986.
  • [10] M. Dash and H. Liu. Feature selection methods for classifications. An International Journal on Intelligent Data Analysis, 1(3):131–156, 1997.
  • [11] A. E. and V. Kann. On the approximation of mini-mizing non zero variables or unsatisfied relations in linear systems. Theoretical Computer Science, Volume 209(1-2):237–260, 1998.
  • [12] T. S. Group. Chaos chronicles. Technical report, The Standish Group, West Yarmouth, MA, 2003.
  • [13] P. Jönsson and C. Wohlin. An evaluation of k-nearest neighbour imputation using likert data. In Proceedings of the 10th IEEE International Software Metrics Symposium, pages 108–118 IEEE Computer Society, 2004.
  • [14] J.R.Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
  • [15] C. Kirsopp, M. Shepperd, and J. Hart. Search heuristics, case-based reasoning and software project effort prediction. In Proceedings of the Genetic and Evolutionary Computation Conference, page 1367–1374, 2002.
  • [16] J. Li and G. Ruhe. A comparative study of attribute weighting heuristics for effort estimation by analogy. In Proceedings of the International Symposium on Empirical Software Engineering, page 66–74, 2006.
  • [17] T. Liang and A. Noore. Multistage software estimation. In Proceedings of the 35th Southeastern Symposium on System Theory, page 232–236, 2003.
  • [18] R. Little and D. Rubin. Statistical Analysis with Missing Data. John Wiley & Sons, New York, 2nd edition edition, 2002.
  • [19] K. Maxwell, L. V. Wassenhove, and S. Dutta. Software development productivity of european space, military, and industrial applications. IEEE Transactions on Software Engineering,22(10):706–718, October 1996.
  • [20] I. Myrtveit, E. Stensrud, and U. Olsson. Analyzing data sets with missing data: An empirical evaluation of imputation methods and likelihood-based methods. IEEE Transactions on Software Engineering, 27(11):999–1013, November 2001.
  • [21] I. G. nad A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157–1182, 2003. Integrating Human Judgment and Data Analysis to Identify Factors Influencing Software Development Productivity 69
  • [22] M. Ochs. Using sw risk management for deriving method requirements for risk mitigation in cots assessment & selection. In Proceeding of the International Conference on Software Engineering and Knowledge Engineering, 2003.
  • [23] M. Ochs, D. Pfahl, G. Chrobok-Diening, and B. Nothhelfer-Kolb. A cots acquisition process: definition and application experience. In Proceedings of the 11th European Software Control and Metrics Conference, 2000.
  • [24] M. Ochs, D. Pfahl, G. Chrobok-Diening, and B. Nothhelfer-Kolb. A method for efficient measurement-based cots assessment & selection – method description and evaluation results. In Proceedings of the 7th International Syposium on Software Metrics, 2001.
  • [25] M. Robnik-Sikonja and I. Kononenko. Theoretical and empirical analysis of relieff and rrrelieff.The Machine Learning Journal, 53:23–69, 2003.
  • [26] T. Saaty. The Analytic Hierarchy Process. McGraw-Hill, New York, 1990.
  • [27] D. Schillinger. Entwicklung eines simulationsfähigen cots assessment und selection tools auf basis eines für software adequaten hierarchischen mcdm meta modells, 2006. Supervisors:Prof. Dr. D.H. Rombach, M. Ochs.
  • [28] M. Shepperd and C. Schofield. Estimating software project effort using analogies. IEEE Transactions on Software Engineering, 23(12):736–743, November 1997.
  • [29] D. Sheskin. Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, 2nd edition edition, 2000.
  • [30] Q. Song and M. Shepperd. A short note on safest default missingness mechanism assumptions. Empirical Software Engineering, 10(2), 2005.
  • [31] P. Spector. Summated Rating Scale Construction. Sage Publications, 1992.
  • [32] G. Subramanian and S. Breslawski. Dimensionality reduction in software development effort estimation. Journal of Systems and Software, 21(2):187–196, 1993.
  • [33] A. Trendowicz. Factors influencing software development productivity - state of the art and industrial experiences. Technical Report 08.07/E, Fraunhofer IESE, Kaiserslautern, Germany, 2007.
  • [34] A. Trendowicz. Software effort estimation - overview of current industrial practices and existing methods. Technical Report 06.08/E, Fraunhofer IESE, Kaiserslautern, Germany, 2008.
  • [35] A. Trendowicz, J. Heidrich, J. Münch, Y. Ishigai, K. Yokoyama, and N. Kikuchi. Development of a hybrid cost estimation model in an iterative manner. In Proceedings of the 28th International Conference on Software Engineering, page 331–340, 2006.
  • [36] P. Vincke. Multicriteria Decision-aid. John Wiley & Sons, Chichester, 1992.
  • [37] I. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 2005
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPW7-0013-0012
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