Hideaki, H., & Osamu, M. (2010). An Integrated Approach to Detect Fault-Prone Modules Using Complexity and Text Feature Metrics. the international conference on Advances in computer science and information technology, (pp. 457–۴۶۸). Berlin Heidelberg.
Hudepohl, J. P., Aud, S. J., Khoshgoftaar, T. M., Allen, E. B., & Mayrand, J. (1996). Emerald: Software Metrics and Models on the Desktop. IEEE Software , vol. 13, No. ۵,(pp. 56-60).
Jiang, Y., Cuki, B., Menzies, T., & Bartlow, N. (2008). Comparing design and code metrics for software quality prediction. The 4th international workshop on Predictor models in software engineering (pp. 11-18). Leipzig, Germany: ACM New York, NY, USA.
Kafura, D., & Henry, S. (1981). Software Structure Metrics based on Information Flow. IEEE Transactions on Software Engineering , vol.7, (pp. 510-518).
Kamyabi, J., Maleki, F. & S., A., (2012). Software defect prediction using transitive dependencies on software dependency graph. International Conference of Computer Science and its Applications. vol. ۱۱۴, No. ۱, (pp. 241-249). Jeju, South Korea: Springer Netherlands.
Karp, R. M. (1975). Richard M. Karp. The Journal of Symbolic Logic , vol 40, No. 4, (pp. 618-619).
Khoshgoftaar, T., Allen, E., Goel, N., Nandi, A., & McMullan, J. (1996). Detection of software modules with high debug code churn in a very large legacy system. Seventh International Symposium on Software Reliability Engineering, (pp. 364-371). White Plains, NY , USA .
McCabe, T. J. (1976). A complexity measure. the 2nd international conference on Software, (p.p. 407).
Menzies, T., Greenwald, J., & Frank, A. (2007). Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Transactions on Software Engineering , vol 33, No. 1, (pp. 2-13).
Menzies, T., Milton, Z., Turhan, B., Cukic, B., Jiang, Y., & Bener, A. (2010). Defect prediction from static code features: current results, limitations, new approaches. Emperical Software Engineering , Vol 17, No 4.
Nagappan, N., & Ball, T. (2005). Use of relative code churn measures to predict system defect density. ۲۷th International Conference on Software Engineering, (pp. 284-292). St. Louis, Missouri, USA.
Nagappan, N., & Ball, T. (2007). Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study. International Symposium on Empirical Software Engineering and Measurement, (pp. 364-373).
Nagappan, N., Ball, T., & Murphy, B. (2006). Using Historical In-Process and Product Metrics for Early Estimation of Software Failures. ۱۷th International Symposium on Software Reliability Engineering, (pp. 62-74). downtown Raleigh, North Carolina.
Nagappan, N., Ball, T., & Zeller, A. (2006). Mining Metrics to Predict Component Failures. International Conference on Software Engineering, (pp. pp.452-461). Shanghai, China.
Ohlsson, N., & Alberg, H. (1996). Predicting fault-prone software modules in telephone switches. IEEE Transaction on Software Engineering , ۲۲ (۱۲) , ۸۸۶-۸۹۴٫
Ostand, T. J., Weyuker, E. J., & Bell, R. M. (2004). Where the Bugs Are. The 2004 ACM SIGSOFT international symposium on Software testing and analysis, (pp. 86-96). Newport Beach, CA, USA.
Ostrand, T., Weyuker, E., & Bell, R. (2005). Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering , ۳۱ (۴) , ۳۴۰-۳۵۵٫
Pinzger, M., Gall, H., & Fischer, M. (2005). Towards an Inte-grated View on Architecture and its Evolution. Electronic Notes in Theoretical Computer Science, (pp. 183-196).

موضوعات: بدون موضوع  لینک ثابت


فرم در حال بارگذاری ...