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| Tim Menzies, Justin S. Di Stefano, "More Success and Failure Factors in Software Reuse," IEEE Transactions on Software Engineering, vol. 29, no. 5, pp. 474-477, May, 2003. | |||
| BibTex | x | ||
| @article{ 10.1109/TSE.2003.1199076, author = {Tim Menzies and Justin S. Di Stefano}, title = {More Success and Failure Factors in Software Reuse}, journal ={IEEE Transactions on Software Engineering}, volume = {29}, number = {5}, issn = {0098-5589}, year = {2003}, pages = {474-477}, doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.2003.1199076}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Software Engineering TI - More Success and Failure Factors in Software Reuse IS - 5 SN - 0098-5589 SP474 EP477 EPD - 474-477 A1 - Tim Menzies, A1 - Justin S. Di Stefano, PY - 2003 KW - Reuse KW - machine learning. VL - 29 JA - IEEE Transactions on Software Engineering ER - | |||
Abstract—Numerous discrepancies exist between expert opinion and empirical data reported in Morisio et al.'s recent TSE article. The differences related to what factors encouraged successful reuse in software organizations. This note describes how those differences were detected and comments on their methodological implications.
[1] M. Morisio, M. Ezran, and C. Tully, “Success and Failure Factors in Software Reuse,” IEEE Trans. Software Eng., vol. 28, no. 4, pp. 340-357, 2002.
[2] L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, “Classification and Regression Trees,” technical report, Wadsworth Int'l, Monterey, Calif., 1984.
[3] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proc. 1993 ACM-SIGMOD Int'l Conf. Management of Data, pp. 207-216, May 1993.
[4] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 1999,
[5] T. Menzies, E. Chiang, M. Feather, Y. Hu, and J.D. Kiper, “Condensing Uncertainty via Incremental Treatment Learning,” Annals of Software Eng., 2002, available fromwww.eccc.uni-trier.de/eccc/http://tim.menzies.com/ pdf02itar2.pdf.
[6] J.R. Quinlan, C4.5: Programs for Machine Learning,San Mateo, Calif.: Morgan Kaufman, 1992.
[7] C. Abts, B. Clark, S. Devnani-Chulani, E. Horowitz, R. Madachy, D. Reifer, R. Selby, and B. Steece, “COCOMO II Model Definition Manual,” technical report, Center for Software Eng., Univ. Southern Calif., 1998, http://sunset.usc.edu/COCOMOIIcocomox.html#downloads .
[8] R. Lutz and C. Mikulski, “Operational Anomalies as a Cause of Safety-Critical Requirements Evolution,” J. Systems and Software (to appear), 2003, available from.
[9] R. Lutz and C. Mikulski, “Empirical Analysis of Safety-Critical Anomalies During Operations,” 2003, submitted for publication.
[10] T. Menzies, R. Lutz, and C. Mikulski, “Better Analysis of Defect Data at NASA,” Proc. 27th NASA SEL Workshop Software Eng., 2002, available fromhttp://www.cs.iastate.edu/rlutz/publications/ JSS02.pshttp://tim.menzies.com/pdf 02moreodc.pdf.