158
Views
7
CrossRef citations to date
0
Altmetric
Reliability Engineering

An imperfect software debugging model considering irregular fluctuation of fault introduction rate

References

  • Akaike, H. 1974. A new look at statistical model identification. IEEE Transactions on Automatic Control 19:716–23.
  • Bregon, C. J. Alonso-Gonzalez, and B. Pulido. 2014. Integration of simulation and state observers for online fault detection of nonlinear continuous systems. IEEE Transactions on Systems Man & Cybernetics Systems 44 (12):1553–68.
  • Chiu, K. C., Y. S. Huang, and T. Z. Lee. 2008. A study of software reliability growth from the perspective of learning effects. Reliability Engineering and System Safety 93 (10):1410–21.
  • Goel, A. L., and K. Okumoto. 1979. Time dependent error detection rate model for software reliability and other performance measures. IEEE Transactions on Reliability R-28 (3):206–11.
  • Hossain, S. A., and R. C. Dahiya. 1993. Estimating the parameters of a non-homogeneous Poisson process model for software reliability. IEEE Transactions on Reliability 42:604–12.
  • Huang, C. Y., and M. R. Lyu. 2011. Estimation and analysis of some generalized multiple change-point software reliability models. IEEE Transactions on Reliability 60 (2):498–514.
  • Jelinski, Z., and B. P. Moranda. 1972. Software. Reliability research, Statistical computer performance evaluation, pp. 465–484. New York: Academic.
  • Kapur, P. K., D. Gupta, A. Gupta, and P. C. Jha. 2008. Effect of introduction of faults and imperfect debugging on release time. Ratio Mathematics 18:62–90.
  • Kapur, P. K., H. Pham, S. Anand, and K. Yadav. 2011. A unified approach for developing software reliability growth models in the presence of imperfect debugging and error generation. IEEE Transactions on Reliability 60 (1):331–40.
  • Lawless, J. F. 1982. Statistical models and methods for lifetime data. New York: Wiley.
  • Li, P. L., J. Herbsleb, and M. Shaw. 2005. Forecasting field defect rates sing a combined time-based and metrics-based approach: A case study of OpenBSD. In Proceedings of the 16th IEEE International Symposium on Softw, pp. 193–202, Reliability Engineering, Chicago, IL.
  • Li, X., M. Xie, and S. H. Ng. 2010. Sensitivity analysis of release time of software reliability models incorporating testing effort with multiple change-points. Applied Mathematical Modelling 34:3560–70.
  • Lyu, M. R. 1996. Handbook of software reliability engineering. New York: McGraw-Hill.
  • Musa, J. D. 1980. Software reliability data. Rome, NY: Data and Analysis Center for Software, Rome Air Development Center.
  • Musa, J. D. 1999. Software reliability engineering: More reliable software, faster development and testing. New York: McGraw-Hill.
  • Musa, J. D., A. Iannino, and K. Okumoto. 1987. Software reliability: Measurement, prediction and application. New York: McGraw-Hill.
  • Ohba, M. 1984. Inflection S-shaped software reliability growth models. In Stochastic models in reliability theory, ed. S. Osaki and Y. Hatoyama, pp. 144–62. Berlin: Springer-Verlag.
  • Olivier FOURDAN. Xfce—Desktop Environment. http://www.xfce.org/
  • Peng, R., Y. F. Li, J. G. Zhang, and X. Li. 2015. A risk-reduction approach for optimal software release time determination with the delay incurred cost. International Journal of Systems Science 46 (9):1628–37.
  • Peng, R., Y. F. Li, W. J. Zhang, and Q. P. Hu. 2014. Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction. Reliability Engineering & System Safety 126:37–43.
  • Pham, H. 2006. System software reliability. London: Springer.
  • Pham, H., L. Nordmann, and X. Zhang. 1999. A general Imperfect software debugging model with S-shaped fault detection rate. IEEE Transactions on Reliability R-48 (2):169–175.
  • Pham, H., and X. Zhang. 1997. An NHPP software reliability models and its comparison. International Journal of Reliability, Quality and Safety Engineering 4:269–282.
  • Singh, P., N. R. Pal, S. Verma, and O. P. Vyas. 2017. Fuzzy rule-based approach for software fault prediction. IEEE Transactions on Systems Man & Cybernetics Systems 47 (5):826–37.
  • Teng, X. L., and H. Pham. 2006. A new methodology for predicting software reliability in the random field environments. IEEE Transactions Reliability 55 (3):458–68.
  • Wang, J., and Z. Wu. 2016. Study of the nonlinear imperfect software debugging model. Reliability Engineering & System Safety 153:180–192.
  • Wang, J., Z. Wu, and Y. Shu. 2013. Analysis of the debugging model based on probabilistic state transaction. Journal of Software 8 (11):2697–2705.
  • Wang, J., Z. Wu, Y. Shu, and Z. Zhang. 2014. A general imperfect software debugging model considering the nonlinear process of fault introduction. In Proceedings of the 14th International Conference on Quality Software (QSIC 2014), Dallas, TX, October 2–3 pp. 222–27.
  • Wang, J., Z. Wu, Y. Shu, and Z. Zhang. 2015a. An imperfect software debugging model based on stochastic differential equation. In Proceedings of 1st International Workshop on Complex faults and Failures in Large Software Systems (Workshop of ICSE 2015), Florence, Italy, May 16–24, pp. 62–68.
  • Wang, J., Z. Wu, Y. Shu, and Z. Zhang. 2015b. An imperfect software debugging model considering log-logistic distribution fault content function. Journal of Systems and Software 100:167–81.
  • Xie, M., Q. P. Hu, Y. P. Wu, and S. H. Ng. 2007. A study of the modeling and analysis of software fault-detection and fault-correction processes. Quality and Reliability Engineering International 23 (4):459–70.
  • Yamada, S., M. Ohba, and S. Osaki. 1983. S-shaped reliability growth modeling for software error detection. IEEE Transactions on Reliability 32:475–84.
  • Yamada, S., K. T. Okuno, and S. Osaki. 1992. Imperfect debugging models with fault introduction rate for software reliability assessment. International Journal of. System Science 23 (12):2253–64.
  • Yamada, S., and S. Osaki. 1985. Software reliability growth modeling: Models and applications. IEEE Transactions Software Engineering 11:431–37.
  • Yang, B., H. Hu, and L. Jia. 2008. A study of uncertainty in software cost and its impact on optimal software release time. IEEE Transactions on Software Engineering 34:813–835.
  • Yin, X., and Z. Li. 2016. Reliable decentralized fault prognosis of discrete-event systems. IEEE Transactions on Systems Man & Cybernetics Systems 46 (11):1598–603.
  • Zhang, X., and H. Pham. 2000. Comparisons of nonhomogeneous Poisson process software reliability models and its applications. Int. J. Systems Science 31 (9):1115–23.
  • Zhang, X., X. Teng, and H. Pham. 2003. Considering fault removal efficiency in software reliability assessment. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 33 (1):2241–52.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.