Guide to Advanced Empirical
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| Bibliography Albrecht, A. J. & GaffneyJr., J. E. (1983), Software function, source lines of code, and development effort prediction a software science validation, IEEE Trans. Software Eng. 9(6), An, K. H, Gustafson, DA Melton, AC, A model for software maintenance, in Proceedings of the Conference in Software Maintenance, Austin, Texas, pp. Atkins, D, Ball, T, Graves, T. & Mockus, A. (1999), Using version control data to evaluate the effectiveness of software tools, in ‘1999 International Conference on Software Engineering, ACM Press, pp. Barnard, J. & Rubin, DB, Small sample degrees of freedom with multiple imputation, Biometrika 86(4). Chidamber, SR Kemerer, CF, A metrics suite for object oriented design, IEEE Trans. Software Eng. 20(6), Fleming, TH Harrington, D. (1984), ‘Nonparametric estimation of the survival distribution in censored data, Comm. in Statistics 13, 2469–86. Goldenson, DR, Gopal, A. & Mukhopadhyay, T. (1999), Determinants of success in software measurement programs, in Sixth International Symoposium on Software Metrics, IEEE Computer Society, pp. Graves, TL, Karr, AF, Marron, J. S. & Siy, HP, Predicting fault incidence using software change history, IEEE Transactions on Software Engineering 26(7), Graves, TL Mockus, A. (1998), Inferring change effort from configuration management databases, in Metrics 98: Fifth International Symposium on Software Metrics, Bethesda, Maryland, pp. 267–273. Halstead, M. H. (1977), Elements of Software Science, Elsevier North-Holland. Herbsleb, JD Grinter, R. (1998), Conceptual simplicity meets organizational complexity Case study of a corporate metrics program, in ‘20th International Conference on Software Engineering, IEEE Computer Society, pp. 271–280. Herbsleb, JD, Krishnan, M, Mockus, A, Siy, HP Tucker, GT, Lessons from ten years of software factory experience, Technical report, Bell Laboratories. Jönsson, P. & Wohlin, C. (2004), An evaluation of k-nearest neighbour imputation using likert data, in ‘Proc. of the 10th Int. Symp. on Software Metrics, pp. 108–118. Kaplan, E. & Meyer, P. (1958), ‘Non-paramentric estimation from incomplete observations, J Am Stat Assoc pp. Kim, J. & Curry, J. (1977), The treatment of missing data in multivariate analysis, Social Methods and Research 6, Little, R. & Hyonggin, A. (2003), Robust likelihood-based analysis of multivariate data with missing values, Technical Report Working Paper 5, The University of Michigan Department of Biostatistics Working Paper Series. http://www.bepress.com/umichbiostat/paper5 Little, R. J. AA test of missing completely at random for multivariate data with missing values, Journal of the American Statistical Association 83(404), 1198–1202. 381 382 Bibliography Little, R. J. A. & Rubin, DB, Statistical Analysis with Missing Data, Willey Series in Probability and Mathematical Statistics, John Willey & Sons. Little, R. J. A. & Rubin, DB, The analysis of social science data with missing values, Sociological Methods and Research 18(2), 292–326. McCabe, TA complexity measure, IEEE Transactions on Software Engineering 2(4), 308–320. Mockus, A. (2006), Empirical estimates of software availability of deployed systems, in ‘2006 International Symposium on Empirical Software Engineering, ACM Press, Rio de Janeiro, Brazil, pp. 222–231. Mockus, A. (2007), Software support tools and experimental work, in V. Basili & et al, eds, Empirical Software Engineering Issues LNCS 4336:’, Springer, p. to appear. Mockus, A. & Votta, LG, Identifying reasons for software changes using historic databases, Technical Report BL, Bell Laboratories. Myrtveit, I, Stensrud, E. & Olsson, U. (2001), Analyzing data sets with missing data An empirical evaluation of imputation methods and likelihood-based methods, IEEE Transactions on Download 1.5 Mb. Share with your friends:
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