Skip to main content
SpringerLink
Log in

Empirical Evaluation of Selected Algorithms for Complexity-Based Classification of Software Modules and a New Model

  • Chapter
Book cover Intelligent Systems: From Theory to Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 299))

Abstract

Software plays a major role in many organizations. Organizational success depends partially on the quality of softwares used. In recent years, many researchers have recognized that statistical classification techniques are well-suited to develop software quality prediction models. Different statistical software quality models, using complexity metrics as early indicators of software quality, have been proposed in the past. At a high-level the problem of software categorization is to classify software modules into fault prone and non-fault prone. Indeed, a learner is given a set of training modules and the corresponding class labels (i.e fault prone or non-fault-prone), and outputs a classifier. Then, the classifier takes an unlabeled module (i.e hitherto-unseen module) and assigns it to a class. The focus of this paper is to study some selected classification techniques widely used for software categorization. Indeed, practitioners are faced with a body of approaches and literature that give several conflicting advices about the usefulness of these classification approaches. The techniques evaluated in this paper include: principal component analysis, linear discriminant analysis, multiple linear regression, logistic regression, support vector machine and finite mixture models. Moreover, we propose a Bayesian approach based on finite Dirichlet mixture models. We evaluate experimentally these approaches using a real data set. Our experimental results show that different algorithms lead to different statistically significant results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Porter, A.A., Selby, R.W.: Empirically guided software development using metric-based classification trees. IEEE Software 7(2), 46–54 (1990)

    Article  Google Scholar 

  2. Mayer, A., Sykes, A.M.: Statistical Methods for the Analysis of Software Metrics Data. Software Quality Journal 1(4), 209–223 (1992)

    Article  Google Scholar 

  3. Narayanan, A.: A Note on Parameter Estimation in the Multivariate Beta Distribution. Computer Mathematics and Applications 24(10), 11–17 (1992)

    Article  MATH  Google Scholar 

  4. Curtis, B., Sheprad, S.B., Milliman, H., Borst, M.A., Love, T.: Measuring the Psychlogical Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics. IEEE Transactions on Software Engineering SE-5(2), 96–104 (1979)

    Article  Google Scholar 

  5. Boehm, B.W., Papaccio, P.N.: Understanding and Controlling Software Costs. IEEE Transactions on Software Engineering 14(10), 1462–1477 (1988)

    Article  Google Scholar 

  6. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  7. Ebert, C.: Classification Techniques for Metric-Based Development. Software Quality Journal 5(4), 255–272 (1996)

    Article  Google Scholar 

  8. Ebert, C., Baisch, E.: Industrial Application of Criticality Predictions in Software Development. In: Proc. of the 8th IEEE International Symposium on Software Reliability Engineering, pp. 80–89 (1998)

    Google Scholar 

  9. Wallace, C.S.: Statistical and Inductive Inference by Minimum Message Length. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  10. Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis, 3rd edn. Wiley-Interscience, Hoboken (2001)

    MATH  Google Scholar 

  11. Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley-Interscience Publication, Hoboken (2000)

    Book  MATH  Google Scholar 

  12. Zhang, D., Tsai, J.J.P.: Machine Learning and Software Engineering. Software Quality Journal 11(2), 87–119 (2003)

    Article  Google Scholar 

  13. Weyuker, E.J.: Evaluating software complexity measures. IEEE Transactions on Software Engineering 14(9), 1357–1365 (1988)

    Article  MathSciNet  Google Scholar 

  14. Brooks, F.: No Silver Bullet-Essense and Accidents of Software Engineering. IEEE Computer 20(4), 10–19 (1987)

    MathSciNet  Google Scholar 

  15. Lanubile, F.: Why Software Reliability Predictions Fail. IEEE Software 13(4), 131–132, 137 (1996)

    Article  Google Scholar 

  16. Lanubile, F., Visaggio, G.: Evaluating Predictive Quality Models Derived from Software Measures: Lessons Learned. Journal of Systems and Software 38(3), 225–234 (1997)

    Article  Google Scholar 

  17. Xing, F., Guo, P., Lyu, M.R.: A Novel Method for Early Software Quality Prediction Based on Support Vector Machine. In: Proc. of the 16th IEEE International Symposium on Software Reliability Engineering, pp. 213–222 (2005)

    Google Scholar 

  18. Le Gall, G., Adam, M.-F., Derriennic, H., Moreau, B., Valette, N.: Studies on Measuring Software. IEEE Journal on Selected Areas in Communications 8(2), 234–246 (1990)

    Article  Google Scholar 

  19. Ronning, G.: Maximum Likelihood Estimation of Dirichlet Distributions. Journal of Statistical Computation and Simulation 32, 215–221 (1989)

    Article  MATH  Google Scholar 

  20. Schwarz, G.: Estimating the Dimension of a Model. The Annals of Statistics 6(2), 461–464 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  21. Russel, G.W.: Experience With Inspection in Ultralarge-Scale Developments. IEEE Software 8(1), 25–31 (1991)

    Article  Google Scholar 

  22. McLachlan, G.J., Peel, D.: Finite Mixture Models. Wiley, New York (2000)

    Book  MATH  Google Scholar 

  23. Akaike, H.: A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control AC-19(6), 716–723 (1974)

    Article  MathSciNet  Google Scholar 

  24. Jensen, H., Vairavan, K.: An Experimental Study of Software Metrics for Real-time Software. IEEE Transaction on Software Engineering SE-11(4), 231–234 (1994)

    Google Scholar 

  25. Zuse, H.: Comments to the Paper: Briand, Eman, Morasca: On the Application of Measurement Theory in Software Engineering. Empirical Software Engineering 2(3), 313–316 (1997)

    Article  Google Scholar 

  26. Munson, J.C., Khoshgoftaar, T.M.: The Dimensionality of Program Complexity. In: Proc. of Eleventh International Conference on Software Engineering, pp. 245–253 (1989)

    Google Scholar 

  27. Gaffney, J.: Estimating the Number of Faults in Code. IEEE Transactions on Software Engineering 10(4), 459–464 (1984)

    Article  MathSciNet  Google Scholar 

  28. Henry, J., Henry, S., Kafura, D., Matheson, L.: Improving Software Maintenance at Martin Marietta. IEEE Software 11(4), 67–75 (1994)

    Article  Google Scholar 

  29. Mayrand, J., Coallier, F.: System Acquisition Based on Software Product Assessment. In: Proc. of 18th International Conference on Software Engineering, pp. 210–219 (1996)

    Google Scholar 

  30. Troster, J., Tian, J.: Measurement and Defect Modeling for a Legacy Software System. Annals of Software Engineering 1(1), 95–118 (1995)

    Article  Google Scholar 

  31. Munson, J.C.: Handbook of Software Reliability Engineering. IEEE Computer Society Press/McGraw-Hill Book Company (1999)

    Google Scholar 

  32. Munson, J.C., Khoshgoftaar, T.M.: The Detection of Fault-Prone Programs. IEEE Transactions on Software Engineering 18(5), 423–433 (1992)

    Article  Google Scholar 

  33. Briand, L., EL Emam, K., Morasca, S.: On the Application of Measurement Theory in Software Engineering. Empirical Software Engineering 1(1), 61–88 (1996)

    Article  Google Scholar 

  34. Briand, L.C., Basili, V.R., Hetmanski, C.J.: Developing Interpretable Models with Optimized Set Reduction for Identifying High-Risk Software Components. IEEE Transactions on Software Engineering 19(11), 1028–1044 (1993)

    Article  Google Scholar 

  35. Briand, L.C., Basili, V.R., Thomas, W.M.: A Pattern Recognition Approach for Software Engineering Data Analysis. IEEE Transactions on Software Engineering 18(11), 931–942 (1992)

    Article  Google Scholar 

  36. Briand, L.C., Thomas, W.M., Hetmanski, C.J.: Modeling and Managing Risk Early in Software Development. In: Proc. of 15th International Conference on Software Engineering, pp. 55–65 (1993)

    Google Scholar 

  37. Guo, L., Ma, Y., Cukic, B., Singh, H.: Robust Prediction of Fault-Proneness by Random Forests. In: Proc. of the 15th IEEE International Symposium on Software Reliability Engineering, pp. 417–428 (2004)

    Google Scholar 

  38. Ottenstein, L.M.: Quantitative Estimates of Debugging Requirements. IEEE Transactions on Software Engineering SE-5(5), 504–514 (1979)

    Article  Google Scholar 

  39. Mark, L., Jeff, K.: Object-Oriented Software Metrics. Prentice-Hall, Englewood Cliffs (1994)

    Google Scholar 

  40. Ohlsson, M.C., Wohlin, C.: Identification of Green, Yellow and Red Legacy Components. In: Proc. of the International Conference on Software Maintenance, pp. 6–15 (1998)

    Google Scholar 

  41. Ohlsson, M.C., Runeson, P.: Experience from Replicating Empirical Studies on Prediction Models. In: Proc. of the Eighth IEEE Symposium on Software Metrics, pp. 217–226 (2002)

    Google Scholar 

  42. Halstead, M.H., Leroy, A.M.: Elements of Software Science. Elseviser, New York (1977)

    MATH  Google Scholar 

  43. Hitz, M., Montazeri, B.: Chidamber and Kemerer’s Metrics Suite: A Measurement Theory Perspective. IEEE Transactions on Software Engineering 22(4), 267–271 (1996)

    Article  Google Scholar 

  44. Shepperd, M., Kadoda, G.: Comparing Software Prediction Techniques Using Simulation. IEEE Transactions on Software Engineering 27(11), 1014–1022 (2001)

    Article  Google Scholar 

  45. Bouguila, N., Ziou, D.: Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach. IEEE Transactions on Knowledge and Data Engineering 18(8), 993–1009 (2006)

    Article  Google Scholar 

  46. Bouguila, N., Ziou, D.: Unsupervised Learning of a Finite Discrete Mixture: Applications to Texture Modeling and Image Databases Summarization. Journal of Visual Communication and Image Representation 18(4), 295–309 (2007)

    Article  Google Scholar 

  47. Bouguila, N., Ziou, D., Monga, E.: Practical Bayesian Estimation of a Finite Beta Mixture Through Gibbs Sampling and its Applications. Statistics and Computing 16(2), 215–225 (2006)

    Article  MathSciNet  Google Scholar 

  48. Bouguila, N., Ziou, D., Vaillancourt, J.: Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS (LNAI), vol. 2734, pp. 172–181. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  49. Bouguila, N., Ziou, D., Vaillancourt, J.: Unsupervised Learning of a Finite Mixture Model Based on the Dirichlet Distribution and its Application. IEEE Transactions on Image Processing 13(11), 1533–1543 (2004)

    Article  Google Scholar 

  50. Bouguila, N., Wang, J.H., Ben Hamza, A.: A Bayesian Approach for Software Quality Prediction. In: Proc. of the IEEE International Conference on Intelligent Systems, pp. 49–54 (2008)

    Google Scholar 

  51. Schneidewind, N.F.: Validating Software Metrics: Producing Quality Discriminators. In: Proc. of Second International Symposium on Software Reliability Engineering, pp. 225–232 (1991)

    Google Scholar 

  52. Schneidewind, N.F.: Methodology For Validating Software Metrics. IEEE Transactions on Software Engineering 18(5), 410–422 (1992)

    Article  Google Scholar 

  53. Schneidewind, N.F.: Minimizing risk in applying metrics on multiple projects. In: Proc. of Third International Symposium on Software Reliability Engineering, pp. 173–182 (1992)

    Google Scholar 

  54. Schneidewind, N.F.: Software metrics validation: Space Shuttle flight software example. Annals of Software Engineering 1(1), 287–309 (1995)

    Article  Google Scholar 

  55. Schneidewind, N.F.: Software metrics model for integrating quality control and prediction. In: Proc. of the Eighth International Symposium on Software Reliability Engineering, pp. 402–415 (1997)

    Google Scholar 

  56. Schneidewind, N.F.: Investigation of Logistic Regression as a Discriminant of Software Quality. In: Proc. of the Seventh IEEE Symposium on Software Metrics, pp. 328–337 (2001)

    Google Scholar 

  57. Fenton, N.: Software Measurement: A Necessary Scientific Basis. IEEE Transactions on Software Engineering 20(3), 199–206 (1994)

    Article  Google Scholar 

  58. Ohlisson, N., Zhao, M., Helander, M.: Application of Multivariate Analysis for Software Fault Prediction. Software Quality Journal 7(1), 51–66 (1998)

    Article  Google Scholar 

  59. Ohlsson, N., Alberg, H.: Predicting Fault-Prone Software Modules in Telephone Switches. IEEE Transactions on Software Engineering 22(12), 886–894 (1996)

    Article  Google Scholar 

  60. Congdon, P.: Applied Bayesian Modelling. John Wiley and Sons, Chichester (2003)

    Book  MATH  Google Scholar 

  61. Frankl, P., Hamlet, D., Littlewood, B., Strigini, L.: Evaluating Testing Methods by Delivered Reliability. IEEE Transactions on Software Engineering 24(8), 586–601 (1998)

    Article  Google Scholar 

  62. Guo, P., Lyu, M.R.: Software Quality Prediction Using Mixture Models with EM Algorithm. In: Proc. First Asia-Pacific Conference on Quality Software, pp. 69–78 (2000)

    Google Scholar 

  63. Szabo, R.M., Khoshgoftaar, T.M.: An assessment of software quality in a C++ environment. In: Proc. of the Sixth International Symposium on Software Reliability Engineering, pp. 240–249 (1995)

    Google Scholar 

  64. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)

    MATH  Google Scholar 

  65. Pressman, R.S.: Software Engineering: A Practioner’s Approach, 5th edn. McGraw-Hill, New York (2001)

    Google Scholar 

  66. Takahashi, R., Muraoka, Y., Nakamura, Y.: Building Software Quality Classification Trees: Approach, Experimentation, Evaluation. In: Proc. of the 8th IEEE International Symposium on Software Reliability Engineering, pp. 222–233 (1997)

    Google Scholar 

  67. Selby, R.W.: Empirically based analysis of failures in software systems. IEEE Transactions on Reliability 39(4), 444–454 (1990)

    Article  Google Scholar 

  68. Selby, R.W., Porter, A.A.: Learning From Examples: Generation and Evaluation of Decision Trees for Software Ressource Analysis. IEEE Transactions on Software Engineering 14(12), 1743–1757 (1988)

    Article  Google Scholar 

  69. Kass, R.E., Raftery, A.E.: Bayes Factors. Journal of the American Statistical Association 90, 773–795 (1995)

    Article  MATH  Google Scholar 

  70. Rissanen, J.: Modeling by Shortest Data Description. Automatica 14, 465–471 (1978)

    Article  MATH  Google Scholar 

  71. Biyani, S., Santhanam, P.: Exploring Defect Data from Development and Customer Usage on Software Modules over Multiple Releases. In: Proc. of the 8th IEEE International Symposium on Software Reliability Engineering, pp. 316–320 (1998)

    Google Scholar 

  72. Conte, S.D.: Metrics and Models in Software Quality Engineering. Addison-Wesley Professional, Reading (1996)

    Google Scholar 

  73. Crawford, S.G., McIntosh, A.A., Pregibon, D.: An Analysis of Static Metrics and Faults in C Software. Journal of Systems and Software 15(1), 37–48 (1985)

    Article  Google Scholar 

  74. Stockman, S.G., Todd, A.R., Robinson, G.A.: A Framework for Software Quality Measurement. IEEE Journal on Selected Areas in Communications 8(2), 224–233 (1990)

    Article  Google Scholar 

  75. Henry, S., Wake, S.: Predicting maintainability with software quality metrics. Journal of Software Maintenance: Research and Practice 3(3), 129–143 (1991)

    Article  Google Scholar 

  76. Pfleeger, S.L.: Lessons Learned in Building a Corporate Metrics Program. IEEE Software 10(3), 67–74 (1993)

    Article  Google Scholar 

  77. Pfleeger, S.L., Fitzgerald, J.C., Rippy, D.A.: Using multiple metrics for analysis of improvement. Software Quality Journal 1(1), 27–36 (1992)

    Article  Google Scholar 

  78. Chidamber, S.R., Kemerer, C.F.: A Metrics Suite for Object-Oriented Design. IEEE Transactions on Software Engineering 20(6), 476–493 (1994)

    Article  Google Scholar 

  79. Gokhale, S.S., Lyu, M.R.: Regression Tree Modeling for the Prediction of Software Quality. In: Proc. of the third ISSAT International Conference on Reliability and Quality in Design, pp. 31–36 (1997)

    Google Scholar 

  80. Khoshgoftaar, T.M., Allen, E.B.: Early Quality Prediction: A Case Study in Telecommunications. IEEE Software 13(4), 65–71 (1996)

    Article  Google Scholar 

  81. Khoshgoftaar, T.M., Lanning, D.L., Pandya, A.S.: A Comparative Study of Pattern Recognition Techniques for Quality Evaluation of Telecommunications Software. IEEE Journal on Selected Areas in Communications 12(2), 279–291 (1994)

    Article  Google Scholar 

  82. Khoshgoftaar, T.M., Allen, E.B., Jones, W.D., Hudepohl, J.P.: Return on Investment of Software Quality Predictions. In: Proc. of the IEEE Workshop on Application-Specific Software Engineering Technology, pp. 145–150 (1998)

    Google Scholar 

  83. Khoshgoftaar, T.M., Geleyn, E., Nguyen, L.: Empirical Case Studies of Combining Software Quality Classification Models. In: Proc. of the Third International Conference on Quality Software, pp. 40–49 (2003)

    Google Scholar 

  84. Khoshgoftaar, T.M., Munson, J.C., Lanning, D.L.: A comparative Study of Predictive Models for Program Changes During System Testing and Maintenance. In: Proc. of the IEEE Conference on Software Maintenance, pp. 72–79 (1993)

    Google Scholar 

  85. Khoshgoftaar, T.M., Munson, J.C., Bhattacharya, B.B., Richardson, G.D.: Predictive Modeling Techniques of Software Quality from Software Measures. IEEE Transactions on Software Engineering 18(11), 979–987 (1992)

    Article  Google Scholar 

  86. Dietterich, T.G.: Approximate Statistical Test For Comparing Supervised Classification Learning Algorithms. Neural Computation 10(7), 1895–1923 (1998)

    Article  Google Scholar 

  87. McCabe, T.J.: A Complexity Measure. IEEE Transactions on Software Engineering SE-2(4), 308–320 (1976)

    Article  MathSciNet  Google Scholar 

  88. Khoshgoftaar, T.M., Allen, E.B.: Multivariate Assessment of Complex Software Systems: A comparative Study. In: Proc. of First International Conference on Engineering of Complex Computer Systems, pp. 389–396 (1995)

    Google Scholar 

  89. Khoshgoftaar, T.M., Allen, E.B.: The Impact of Costs of Misclassification on Software Quality Modeling. In: Proc. of Fourth International Software Metrics Symposium, pp. 54–62 (1997)

    Google Scholar 

  90. Khoshgoftaar, T.M., Allen, E.B.: Classification of Fault-Prone Software Modules: Prior Probabilities, Costs, and Model Evaluation. Empirical Software Engineering 3(3), 275–298 (1998)

    Article  Google Scholar 

  91. Khoshgoftaar, T.M., Allen, E.B.: A Comparative Study of Ordering and Classification of Fault-Prone Software Modules. Empirical Software Engineering 4(2), 159–186 (1999)

    Article  Google Scholar 

  92. Khoshgoftaar, T.M., Allen, E.B.: Predicting Fault-Prone Software Modules in Embedded Systems with Classification Trees. In: Proc. of High-Assurance Systems Engineering Workshop, pp. 105–112 (1999)

    Google Scholar 

  93. Khoshgoftaar, T.M., Allen, E.B.: Controlling Overfitting in Classification-Tree Models of Software Quality. Empirical Software Engineering 6(1), 59–79 (2001)

    Article  MATH  Google Scholar 

  94. Khoshgoftaar, T.M., Allen, E.B.: Ordering Fault-Prone Software Modules. Software Quality Journal 11(1), 19–37 (2003)

    Article  Google Scholar 

  95. Khoshgoftaar, T.M., Allen, E.B.: A Practical Classification-Rule for Software-Quality Models. IEEE Transactions on Reliability 49(2), 209–216 (2000)

    Article  Google Scholar 

  96. Khoshgoftaar, T.M., Munson, J.C.: Predicting Software Development Errors Using Software Complexity Metrics. IEEE Journal on Selected Areas in Communications 8(2), 253–261 (1990)

    Article  Google Scholar 

  97. Khoshgoftaar, T.M., Halstead, R.: Process Measures for Predicting Software Quality. In: Proc. of High-Assurance Systems Engineering Workshop, pp. 155–160 (1997)

    Google Scholar 

  98. Khoshgoftaar, T.M., Allen, E.B., Goel, N.: The Impact of Software Evolution and Reuse on Software Quality. Empirical Software Engineering 1(1), 31–44 (1996)

    Article  Google Scholar 

  99. Khoshgoftaar, T.M., Allen, E.B., Hudepohl, J.P., Aud, S.J.: Applications of Neural Networks to Software Quality Modeling of a Very Large Telecommunications System. IEEE Transactions on Neural Networks 8(4), 902–909 (1997)

    Article  Google Scholar 

  100. Khoshgoftaar, T.M., Allen, E.B., Jones, W.D., Hudepohl, J.P.: Which Software Modules have Faults which will be Discovered by Customers? Journal of Software Maintenance: Research and Practice 11, 1–18 (1999)

    Article  Google Scholar 

  101. Khoshgoftaar, T.M., Allen, E.B., Jones, W.D., Hudepohl, J.P.: Classification-Tree Models of Software-Quality Over Multiple Release. IEEE Transactions on Reliability 49(1), 4–11 (2000)

    Article  Google Scholar 

  102. Khoshgoftaar, T.M., Yuan, X., Allen, E.B.: Balancing Misclassification Rates in Classification-Tree Models of Software Quality. Empirical Software Engineering 5(4), 313–330 (2000)

    Article  MATH  Google Scholar 

  103. Khoshgoftaar, T.M., Yuan, X., Allen, E.B., Jones, W.D., Hudepohl, J.P.: Uncertain Classification of Fault-Prone Software Modules. Empirical Software Engineering 7(1), 297–318 (2002)

    Article  MATH  Google Scholar 

  104. Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (1999)

    Google Scholar 

  105. Basili, V.R., Hutchens, D.H.: An Empirical Study of a Syntactic Complexity Family. IEEE Transactions on Software Engineering SE-9(6), 664–672 (1983)

    Article  Google Scholar 

  106. Basili, V.R., Briand, L.C., Melo, W.L.: A Validation of Object-Oriented Design Metrics as Quality Indicators. IEEE Transactions on Software Engineering 22(10), 751–761 (1996)

    Article  Google Scholar 

  107. Rodriguez, V., Tsai, W.T.: Evaluation of Software Metrics Using Discriminant Analysis. Information and Software Technology 29(3), 245–251 (1987)

    Article  Google Scholar 

  108. Shen, V.Y., Conte, S.D., Dunsmore, H.E.: Software Science Revisited: A Critical Analysis of the Theory and its Empirical Support. IEEE Transactions on Software Engineering SE-9(2), 155–165 (1983)

    Article  Google Scholar 

  109. Shen, V.Y., Yu, T.-J., Thebaut, S.M., Paulsen, L.R.: Identifying Error-Prone Software- An Empirical Study. IEEE Transactions on Software Engineering 11(4), 317–324 (1985)

    Article  Google Scholar 

  110. Li, W., Henry, S.: Object-Oriented Metrics that Predict Maintainability. Journal of Systems and Software 23(2), 111–122 (1993)

    Article  Google Scholar 

  111. Evanco, W.M., Agresti, W.M.: A Composite Complexity Approach for Software Defect Modeling. Software Quality Journal 3(1), 27–44 (1994)

    Article  Google Scholar 

  112. Dillon, W.R., Goldstein, M.: Multivariate Analysis. Wiley, New York (1984)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, H3G 1T7, Canada

    Jian Han Wang, Nizar Bouguila & Taoufik Bdiri

Authors
  1. Jian Han Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nizar Bouguila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Taoufik Bdiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Information Techologies of the Bulgarian Academy of Sciences, Sofia, Bulgaria

    Vassil Sgurev  & Mincho Hadjiski  & 

  2. Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Wang, J.H., Bouguila, N., Bdiri, T. (2010). Empirical Evaluation of Selected Algorithms for Complexity-Based Classification of Software Modules and a New Model. In: Sgurev, V., Hadjiski, M., Kacprzyk, J. (eds) Intelligent Systems: From Theory to Practice. Studies in Computational Intelligence, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13428-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13428-9_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13427-2

  • Online ISBN: 978-3-642-13428-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation