Defect inflow prediction in large software project

• Autorzy: Staron M. , Meding W.
• Języki publikacji: EN

Abstrakty

EN

Performance of software projects can be improved by providing predictions of various project pcharacteristics. The predictions warn managers with information about potential problems and provide them with the possibility to prevent or avoid problems. Large software projects are characterized by a large number of factors that impact the project performance, which makes predicting project characteristics difficult. This paper presents methods for constructing prediction models of trends in defect inflow in large software projects based on a small number of variables. We refer to these models as short-term prediction models and long-term prediction models. The short-term prediction models are used to predict the number of defects discovered in the code up to three weeks in advance, while the long-term prediction models provide the possibility of predicting the defect inflow for the whole project. The initial evaluation of these methods in a large software project at Ericsson shows that the models are sufficiently accurate and easy to deploy.

Słowa kluczowe

EN

project performance; software projects; large software projects

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: e-Informatica Software Engineering Journal
• Rocznik: 2010
• Tom: Vol. 4, nr 1
• Strony: 89--107
• Opis fizyczny: Bibliogr. 24 poz.

Twórcy

autor

Staron M.

autor

Meding W.

• Department of Applied IT, Chalmers j University of Gothenburg Ericsson SW Research, Ericsson AB

Bibliografia

• [1] W. W. Agresti and W. M. Evanco. Projectingsoftware defects from analyzing ada designs.Software Engineering, IEEE Transactions on,18(11):988–997, 1992.
• [2] S. Amasaki, T. Yoshitomi, O. Mizuno, Y. Takagi,and T. Kikuno. A new challenge for applying time series metrics data to software quality estimation. Software Quality Journal, 13:177–193, 2005.
• [3] T. Ball and N. Nagappan. Static analysis tools as early indicators of pre-release defect density. In 27th International Conference on Software Engineering, pages 580–586, St. Louis, MO,USA, IEEE, 2005.
• [4] S. Chulani. Constructive quality for defect density prediction: COQUALMO. In International Symposium on Software Reliability Engineering,pages 3–5, 1999.
• [5] S. D. Conte, H. E. Dunsmore, and V. Y.Shen. Software Engineering Metrics and Models.Benjamin-Cummings, Menlo Park CA,1986.
• [6] G. H. Dunteman. Principal Component Analysis.SAGE Publications, 1989.
• [7] A. L. Goel. Software reliability models: Assumptions,limitations, and applicability.
• [8] D. Houston, D. Buettner, and M. Hecht. Dynamic COQUALMO: Defect profiling over development cycles. In ICSP, pages 161–172, 2009.
• [9] M. Klaes, H. Nakao, F. Elberzhager, and J. Munch. Support planning and controlling of early quality assurance by combining expert judgement and defect data – a case study. Empirical Software Engineering, 2009.
• [10] L. M. Laird and M. C. Brennan. Software measurement and estimation: a practical approach.John Wiley and Sons, Hoboken, N.J., 2006.
• [11] P. L. Li, J. Herbsleb, and M. Shaw. Forecasting field defect rates using a combined time-based and metrics-based approach: a case study of OpenBSD. In The 16th IEEE International Symposium on Software Reliability Engineering,page 10. IEEE, 2005.
• [12] Y. K. Malaiya and J. Denton. Module size distribution and defect density. In 11th International Symposium on Software Reliability Engineering, pages 62–71, San Jose, CA, USA, 2000.
• [13] K. Matsumoto, K. Inoue, T. Kikuno, and K. Torii. Experimental evaluation of software reliability growth models. In The 18th International Symposium on Fault-Tolerant Computing,1988, pages 148–153, Tokyo, Japan, IEEE, 1988.
• [14] P. Mohagheghi, R. Conradi, O. M. Killi, and H. Schwarz. An empirical study of software reuse vs. defect-density and stability. In 26th International Conference on Software Engineering, pages 282–291. IEEE, 2004.
• [15] M. Neil and N. Fenton. Predicting software quality using bayesian belief networks. In 21st Annual Software Engineering Workshop, pages 217–230, NASA Goddard Space Flight Centre,1996.
• [16] A. M. Neufelder. How to predict software defectdensity during proposal phase. In National Aerospace and Electronics Conference, pages 71–76, Dayton, OH, USA, 2000.
• [17] T. J. Ostrand, E. J. Weyuker, and R. M. Bell.Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering, 31(4):340–355, 2005.
• [18] H. Petersson, T. Thelin, P. Runeson, and C. Wohlin. Capture-recapture in software inspections after 10 years research: Theory, evaluation and application. Journal of Systems and Software, 72:249–264.
• [19] M. Staron and W. Meding. Predicting weekly defect inflow in large software projects based on project planning and test status. Information and Software Technology, 50(7–8):782–796,2009.
• [20] M. Staron, W. Meding, and C. Nilsson. A framework for developing measurement systems and its industrial evaluation. Information and Software Technology, 51(4):721–737, 2009.
• [21] P. Tomaszewski and L. Lundberg. Software development productivity on a new platform: an industrial case study. Information and Software Technology, 47(4):257–269, 2005.
• [22] P. Tomaszewski and L. Lundberg. The increase of productivity over time: an industrial case study. Information and Software Technology, 48(9):915–927, 2006.
• [23] R. E. Walpole. Probability and statistics for engineers and scientists. Prentice Hall, Upper Saddle River, NJ, 7th edition, 2002.
• [24] C.Wohlin, P. Runeson, M. H¨ost, M. C. Ohlsson,B. Regnell, and A. Wessl´en. Experimentation in Software Engineering: An Introduction. Kluwer Academic Publishing, 2000.