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Statische Analyse von Java-Anwendungen — Eignen sich Lines-of-Code-Metrik und Halstead-Länge?

Static analysis of Java applications — are lines-of-code metric and Halstead length suitable?

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Wirtschaftsinformatik

Abstract

Many of the recently developed software systems are implemented in Java. For these systems, activities presently are mainly related to software development tasks rather than to dedicated software maintenance tasks. For these Java systems, therefore, experimental confirmation of established metrics for measuring code quantities that are related to software maintenance is not available. This also includes very basic size measures such as the LOC metric and the Halstead length. In this article, the application of these metrics for Java systems as well as some of the associated difficulties are outlined. The presented results are based on experimental data and include empirical correlations between the basic size metrics as well as newly derived scaling laws which are suitable for maintenance related software measurement.

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Literatur

  1. Albrecht, A. L.; Gaffney, J. E.: Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation. In: IEEE Trans. Software Engineering 9 (1983), S. 639.

    Google Scholar 

  2. Anderson, O.: Industrial Applications of Software Measurements. In: Information and Software Technology 34 (1992), S. 681.

    Google Scholar 

  3. Azuma M., Mole, D.: Software Management Practice and Metrics in the European Community and Japan: Some Results of a Survey. In: Journal of Systems and Software 26 (1994), S. 5.

    Google Scholar 

  4. Basili, V. R.; Briand, L. C.; Melo, W. L.: A Validation of Object-Oriented Design Metrics as Quality Indicators. In: IEEE Trans. Software Engineering 22 (1996), S. 751.

    Google Scholar 

  5. Banker, R. D.; Datar, S. M.; Kemerer, C. F.: A Model to Evaluate Variables Impacting the Productivity of Software Maintenance Projects. In: Management Science 37 (1991), S. 1.

    Google Scholar 

  6. Balzert, H.: Lehrbuch der Software-Technik. Spektrum Verlag, Heidelberg 1998.

    Google Scholar 

  7. Basili, V. R.; Perricone, B. T.: Software Errors and Compexity: An Empirical Investigation. In: Communications of the ACM 27 (1984), S. 42.

    Google Scholar 

  8. Baumann, P.; Richter, L.: Wie groß ist die Aussagekraft heutiger Software-Metriken?. In: WIRTSCHAFTSINFORMATIK 6 (1992) S. 624.

    Google Scholar 

  9. Basili, V. R.; Selby, R. W. Jr.; Phillips, T.-Y.: Metric Analysis and Data Validation Across Fortran Projects. In: IEEE Trans. Software Engineering 9 (1983), S. 652.

    Google Scholar 

  10. Beizer, B.: Software Testing Techniques. Van Nostrand Reinhold, New York 1990.

    Google Scholar 

  11. Bieman, J. M.: Metric Development for Object-Oriented Software. In: Melton, A. (Ed.): Software Measurement. International Thompson Computer Press, London 1996, S. 75.

    Google Scholar 

  12. Boehm, B.: Software Engineering Economics. In: IEEE Trans. Software Engineering 10 (1984), S. 4.

    Google Scholar 

  13. Boehm, B. W.: Improving Software Productivity. In: Computer 20 (1987), S. 43.

    Google Scholar 

  14. Boehm, B.: Risk Management. IEEE Computer Society Press 1989.

    Google Scholar 

  15. Briand, L. C.: Property-Based Software Engineering Measurement. In: IEEE Trans. Software Engineering 22 (1996), S. 68.

    Google Scholar 

  16. CC GmbH: CC-AUDITOR. http://www.caseconsult.com/files/flyer/Flyer_AUDITOR_en.pdf, Abruf am 2002–11–15.

  17. Chulani, B.; Boehm B.; Steece, B.: Bayesian Analysis of Empirical Software Engineering Cost Models. In: IEEE Trans. Software Engineering 25 (1999), S. 573.

    Google Scholar 

  18. Chidamber, S. R.; Darcy, D. P.; Kemerer, C. F.: Managerial Use of Metrics for Object Oriented Software: An explanatory Analysis. In: IEEE Trans. Software Engineering 24 (1998), S. 629.

    Google Scholar 

  19. Christensen, K.; Fitos, G. P.; Smith, C. P.: A Perspective on Software Science. In: IBM Syst. Journal 20 (1981), S. 372.

    Google Scholar 

  20. Chidamber, S. R.; Kemerer, C. F.: A Metrics Suite for Object Oriented Design. In: IEEE Trans. Software Engineering 20 (1994), S. 476.

    Google Scholar 

  21. Coulter, N. S.: Software Science and Cognitive Psychology. In: IEEE Trans. Software Engineering 9 (1983), S. 166.

    Google Scholar 

  22. Cusumano, M. A.; Kemerer, C. F.: A Quantitative Analysis of U.S. and Japanese Practice and Performance in Software Development. In: Management Science 36 (1990), S. 384.

    Google Scholar 

  23. Curtis, B.; Sheppard, S. B.; Milliman P.: Third Time Charm: Stronger Prediction of Programmer Performance by Software Complexity Metrics. In: Proc. of the 4th Int. Conf. on Software Engineering, Munich, September 1997, S. 356.

    Google Scholar 

  24. Dunn, R.; Ullman, R.: Quality Assurance for Computer Software. McGraw-Hill, New York 1982.

    Google Scholar 

  25. Ebert, C.; Dumke, R. (Hrsg.): Software- Metriken in der Praxis. Springer, Berlin 1996.

    Google Scholar 

  26. Elshoff, J. L.: An Analysis of Some Commercial PL/I Programs. In: IEEE Trans. Software Engineering 2 (1976), S. 113.

    Google Scholar 

  27. Elshoff, J. L.: Measuring Commercial PL/I Programs using Halsteads Criteria. In: ACM Sigplan Notices 7 (1976), S. 38.

    Google Scholar 

  28. Elshoff, J. L.: An Investigation into the Effects of the Counting Method Used in Software Science Measurements. In: ACM Sigplan Notices 13 (1978), S. 30.

    Google Scholar 

  29. Fenton, N.: Software Metrics — A Rigurous Approach. Chapman & Hall, London 1991.

    Google Scholar 

  30. Gaffney, J. E., Jr.: Estimating the Number of Faults in Code. In: IEEE Trans Software Engineering 10 (1984), S. 459.

    Google Scholar 

  31. Halstead, M.: Elements of Software Science. Elsevier North-Holland, New York 1977.

    Google Scholar 

  32. Harrison, W.: An Entropy-Based Measure of Software Complexity. In: IEEE Trans. Software Engineering 18 (1992), S. 1025.

    Google Scholar 

  33. Heinrich, L. J.: Management von Informatik- Projekten. R. Oldenburg, München 1996.

    Google Scholar 

  34. Jensen, H. A.; Vairavan, K.: An Experimental Study of Software Metrics for Real-Time Software. In: IEEE Trans. Software Engineering 11 (1985), S. 231.

    Google Scholar 

  35. Kemerer, C. F.: An Empirical Validation of Software Cost Estimation Models. In: Communications of the ACM 30 (1987), S. 416.

    Google Scholar 

  36. Laing, V.; Coleman, C.: Principal Components of Orthogonal Object-Oriented Metrics. NASA Software Assurance Technology Center, White Paper 323-08-14, NASA Goddard Space Flight Center, 2001. http://satc.gsfc.nasa.gov/support/OSMASAS_SEP01/Principal_Components_of_Orthogonal_Object_Oriented_Metrics.pdf, Abruf am 2002–11–15.

    Google Scholar 

  37. Lassez, J., van der Knijff, D., Sheppard, J.: A Critical Examination of Software Science. In: Journal of Systems and Software 2 (1981), S. 105.

    Google Scholar 

  38. Lehner, F.: Nutzung und Wartung von Software. Carl Hanser, München 1989.

    Google Scholar 

  39. Li, W.; Henry S.: Object-Oriented Metrics that Predict Maintainability. In: Journal of Systems and Software 23 (1993), S. 111.

    Google Scholar 

  40. Lipow, M.: Number of Faults per Line of Code. In: IEEE Trans. Software Engineering 8 (1982), S. 437.

    Google Scholar 

  41. Lind, R. K.; Vairavan, K.: An Experimental Investigation of Software Metrics and their Relationship to Software Development Effort. In: IEEE Trans. Software Engineering 15 (1989), S. 649.

    Google Scholar 

  42. Lorenz, M.; Kidd, J.: Object-Oriented Software Metrics. Prentice Hall, Englewood Cliffs 1994.

    Google Scholar 

  43. Maxwell K. D.; Van Wassenhove, L.; Dutta, S.: Software Development Productivity of European Space, Military, and Industrial Applications. In: IEEE Trans. Software Engineering 22 (1996), S. 706.

    Google Scholar 

  44. Ottenstein, L. M.: Quantitative Estimates of Debugging Requirements. In: IEEE Trans. Software Engineering 5 (1979), S. 504.

    Google Scholar 

  45. Pant, Y.; Henderson-Sellers, B.; Verner, J. M.: Generalization of Object-Oriented Components for Reuse: Measurement of Effort and Size Change. In: Journal of Object Oriented Programming 9 (1996), S. 19.

    Google Scholar 

  46. Pressman, R. S.: Software Engineering. McGraw-Hill, New York 1997.

    Google Scholar 

  47. Rubey, R. J.; Dana J. A.; Biche, P. W.: Quantitative aspects of software validation. In: IEEE Trans. Software Engineering 1 (1975), S. 150.

    Google Scholar 

  48. Sachs, L.: Angewandte Statistik. Springer Verlag, Berlin 1992.

    Google Scholar 

  49. Shen, V. Y.; Conte, S. D.; Dunsmore, H. E.: Software Science Revisited: A Critical Analysis of the Theory and ist Empirical Support. In: IEEE Trans. Software Engineering 9 (1983), S. 155.

    Google Scholar 

  50. SUN Microsystems: Java™2 SDK. http://java.sun.com/j2se/1.3/download.html, Abruf am 2002–11–15.

  51. Tegarden, D.; Sheetz, S.; Monachi, D.: Effectiveness of Traditional Software Metrics for Object-Oriented Systems. In: Proc. 25th Hawaii Int. Conf. on System Sciences (HICSS-25), January 7—10, 1992, S. 359.

    Google Scholar 

  52. Walston, C. E.; Felix, C. P.: A Method of Programming Measurement and Estimation. In: IBM Syst. Journal 16 (1977), S. 54.

    Google Scholar 

  53. Wallmüller, E.: Software-Qualitätsmanagement in der Praxis. Carl Hanser, München 2001.

    Google Scholar 

  54. Wolle, B.: Die Integration von Online- CICS-Anwendungen in E-Business-Lö sungen. In: WIRTSCHAFTSINFORMATIK 44 (2002), S. 325.

    Google Scholar 

  55. Woodfield, S. N.: An Experiment on Unit Increase in Problem Complexity. In: IEEE Trans. Software Engineering 5 (1979), S. 76.

    Google Scholar 

  56. Zweben, S.; Halstead, M.: The Frequency Distribution of Operators in PL/I Programs. In: IEEE Trans. Software Engineering 5 (1979), S. 91.

    Google Scholar 

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    Björn Wolle

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Wolle, B. Statische Analyse von Java-Anwendungen — Eignen sich Lines-of-Code-Metrik und Halstead-Länge?. Wirtschaftsinf 45, 29–40 (2003). https://doi.org/10.1007/BF03250881

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