References
- S. L. Pfleeger and J. M. Atlee, Software Engineering: Theory and Practice, Prentice Hall; 4 edition, (2009)
- Ian Sommerville, Software Engineering, Pearson; 9e (2010)
- A. Bovenzi, D. Cotroneo, R. Pietrantuono, S. Russo, Workload Characterization for Software Aging Analysis, in: Proc. IEEE Intl. Symp. On Software Reliability Engineering, pp. 240-249 (2011)
- Y. Huang, C. Kintala, N. Kolettis, N. Fulton, Software Rejuvenation: Analysis, Module and Applications, in: Proc. Intl. Symp. on Fault-Tolerant Computing, pp. 381-390, (1995)
- M. Balakrishnan, A. Puliaffito, K. Trivedi, I. Viniotisz, Buffer Losses vs. Deadline Violations for ABR Traffic in an ATM Switch: A Computational Approach, Telecommunication Systems 7 (1), 105-123, (1997) https://doi.org/10.1023/A:1019164110511
- E. Marshall, Fatal Error: How Patriot Overlooked a Scud, Science 255 (5050), 1347, (1992) https://doi.org/10.1126/science.255.5050.1347
- M. Grottke, L. Li, K. Vaidyanathan, K. S. Trivedi, Analysis of Software Aging in a Web Server, IEEE Trans. on Reliability 55 (3) , 411-420, (2006) https://doi.org/10.1109/TR.2006.879609
- M. Grottke, R. Matias, K. Trivedi, The Fundamentals of Software Aging, in: Proc. 1st IEEE Intl. Workshop on Software Aging and Rejuvenation, pp. 1-6, (2008)
- Ahamad S., Study of software aging issues and prevention solutions. International Journal of Computer Science and Information Security, Aug 1;14(8):307-313, (2016)
- Padhy, N., Singh, R. P., & Satapathy, S. C. Enhanced evolutionary computing based artificial intelligence model for web-solutions software reusability estimation. Cluster Computing, 22(4), 9787-9804, (2019). https://doi.org/10.1007/s10586-017-1558-0
- Kaur, H., Ahamad, S., & Verma, G. N., Elements of Legacy Program Complexity. International Journal of Research in Engineering and Technology, 4(3), 501-505, (2015) https://doi.org/10.15623/ijret.2015.0403085
- M. Grottke, L. Li, K. Vaidyanathan, K. S. Trivedi, Analysis of Software Aging in a Web Server, IEEE Trans. on Reliability 55 (3), 411-420. (2006) https://doi.org/10.1109/TR.2006.879609
- D. Cotroneo, S. Orlando, R. Pietrantuono, S. Russo, A Measurement based Aging Analysis of the JVM, Software Testing, Verification and Reliability. doi:10.1002/stvr.467.
- D. Cotroneo, R. Natella, R. Pietrantuono, S. Russo, Software Aging Analysis of the Linux Operating System, in: Proc. IEEE 21st Intl. Symp. on Software Reliability Engineering, pp. 71-80. (2010)
- Grottke, M., K. Trivedi, Software faults, software aging and software rejuvenation, Journal of the Reliability Engineering Association of Japan 27 (7), 425-438. (2005)
- S. Garg, A. Pulia_to, K. S. Trivedi, Analysis of Software Rejuvenation using Markov Regenerative Stochastic Petri Net, in: Proc. 6th Intl. Symp. on Software Reliability Engineering, pp. 180-187. (1995)
- K. J. Cassidy, K. C. Gross, A. Malekpour, Advanced Pattern Recognition for Detection of Complex Software Aging Phenomena in Online Transaction Processing Servers, in: Proc. IEEE/IFIP Intl. Conf. on Dependable Systems and Networks, pp. 478-482. (2002)
- K. Vaidyanathan, K. S. Trivedi, A Measurement-Based Model for Estimation of Resource Exhaustion in Operational Software Systems, in: Proc. 10th Intl. Symp. on Software Reliability Engineering, pp.84-93. (1999)
- W. Li and S. Henry, "Maintenance metrics for the Object-Oriented paradigm," in Proceedings of First International Software Metrics Symposium, pp. 52-60, (1993)
- M. Grottke, K. Trivedi, Fighting Bugs: Remove, Retry, Replicate, and Rejuvenate, IEEE Computer 40 (2), 107-109. (2007)
- R. Matias, P. J. Freitas Filho, An Experimental Study on Software Aging and Rejuvenation in Web Servers, in: Proc. 30th Annual Intl. Computer Software and Applications Conf., pp. 189-196, (2006)
- Chug, A., Dhall, S., "Software defect prediction using supervised learning algorithm and unsupervised learning algorithm, "Confluence 2013: The Next Generation Information Technology Summit, pp.173-179, 26-27 Sept. (2013).
- F. B. E. Abreu, R. Carapuca, "Object-Oriented software engineering: Measuring and controlling the development process," in Proceedings of the 4th International Conference on Software Quality, vol. 186, (1994)
- B. K. Kang and J. M. Bieman, "Cohesion and reuse in an Object-Oriented system," in Proceedings of the ACM SIGSOFT Symposium on software reusability, pp. 259-262, Seattle, March (1995)
- L. C. Briand, J. Wust, J. W. Daly, D. V. Porter, "Exploring the relationships between design measures and software quality in Object-Oriented systems," The Journal of Systems and Software, vol. 51, pp. 245-273, (2000) https://doi.org/10.1016/S0164-1212(99)00102-8
- M. Halstead, Elements of Software Science. New York, USA: Elsevier Science, (1977)
- B. Henderson-Sellers, Software Metrics. Prentice-Hall, (1996)
- T. J. McCabe, "A complexity measure," IEEE Transactions on Software Engineering, vol. 2, pp. 308-320, (1976) https://doi.org/10.1109/TSE.1976.233837
- D. P. Tegarden, S. D. Sheetz, D. E. Monarchi, "A software complexity model of Object-Oriented systems," Decision Support Systems, vol. 13, no. 3, pp. 241-262, (1995) https://doi.org/10.1016/0167-9236(93)E0045-F
- M. Lorenz and J. Kidd, Object-Oriented Software Metrics. NJ, Englewood: Prentice-Hall, (1994)
- S. R. Chidamber and C. F. Kemerer, "A metrics suite for Object-Oriented design," IEEE Transactions on Software Engineering on June 1994, vol. 20, pp. 476-493, (1994) https://doi.org/10.1109/32.295895
- http://openscience.us/repo/software-aging/
- Huang GB, Zhu QY, Siew CK. Extreme Learning Machine: Theory and Applications. Neuro computing, 70(1-3): 489-501, (2006)
- Amir Ahmad, predicting software aging related bugs from imbalanced datasets by using data mining techniques, IOSR Journal of Computer Engineering (IOSR-JCE), Volume 18, Issue 1, Ver. III, PP 27-35. (2016) https://doi.org/10.9790/0661-1805052731
- Xiaozhi Du, Huimin Lu, Gang Liu, Software Aging Prediction based on Extreme Learning Machine, TELKOMNIKA, Vol.11, No.11, pp. 6547-6555 (2013)