Abstract
The existing software reliability growth model (SRGMs) usually assumes that the detected faults can be eliminated well when considering different types of software faults, to simplify the problem. Therefore, given these existing defects, we propose a new non-homogeneous Poisson process (NHPP) SRGM based on considering different fault severity. According to the complexity of the fault, we define the software fault as three levels: Level I is a simple fault, Level II is a general fault, and Level III is a severe fault. In the process of fault detection, the model comprehensively considers the tester’s ability to find problems and the number of remaining issues. In the process of debugging, the problems of imperfection and new fault introduction are considered. Two kinds of real data sets, fault classification and non-classification, were selected and we made simulation for the proposed model and other traditional SRGMs on the PyCharm platform. The experimental results show that the software reliability model considering fault severity has excellent performance of fault fitting and prediction on both types of data sets.
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References
Mengmeng, Z., Hoang, P.: A software reliability model incorporating martingale process with gamma-distributed environmental factors. Ann. Oper. Res., 1–22 (2018)
Jaiswal, A., Malhotra, R.: Software reliability prediction using machine learning techniques. Int. J. Syst. Assur. Eng. Manag. 9(1), 230–244 (2018)
Chatterjee, S., Shukla, A.: A unified approach of testing coverage based software reliability growth modelling with fault detection probability, imperfect debugging, and change point. J. Softw. Evol. Process 31(3), e2150 (2019)
Aihua, G., Chunyang, Z., Qingxin, H.: Improvement of G-O model of software reliability growth. J. Inner Mongolia Univ. Nat. Sci. Edn. 45(2), 84–87 (2014)
Kumar, R., Kumar, S., Tiwari, S.K.: A study of software reliability on big data open source software. Int. J. Syst. Assur. Eng. Manage. 10(2), 1–9 (2019)
Mengmeng, Z., Hoang, P.: A two-phase software reliability modeling involving with software fault dependency and imperfect fault removal. Comput. Lang. Syst. Struct. 53, 27–42 (2017)
Hwang, S., Pham, H.: Quasi-renewal time-delay fault-removal consideration in software reliability modeling. IEEE Trans. Syst. Man Cybern. 39(1), 200–209 (2009)
Garmabaki, A.H., Aggarwal, A.G., Kapur, P.K.: Multi up-gradation software reliability growth model with faults of different severity. In: Industrial Engineering and Engineering Management, pp. 1539–1543 (2011)
Goseva-Popstojanova, K., Trivedi, K.S.: Failure correlation in software reliability models. IEEE Trans. Reliab. 49(1), 37–48 (2000)
Singh, V.B., Sharma, M., Pham, H.: Entropy based software reliability analysis of multi-version open source software. IEEE Trans. Softw. Eng. 44(12), 1207–1223 (2018)
Yaghoobi, T.: Parameter optimization of software reliability models using improved differential evolution algorithm. Math. Comput. Simul. 17, 46–62 (2020)
Ohba, M.: Software reliability analysis models. IBM J. Res. Dev. 28(4), 428–443 (1984)
Nagaraju, V., Fiondella, L., Zeephongsekul, P., Jayasinghe, C.L., Wandji, T.: Performance optimized expectation conditional maximization algorithms for nonhomogeneous poisson process software reliability models. IEEE Trans. Reliab. 66(3), 722–734 (2017)
Vizarreta, P.: Assessing the maturity of SDN controllers with software reliability growth models. IEEE Trans. Netw. Serv. Manage. 15(3), 1090–1104 (2018)
Chatterjee, S., Singh, J.B., Roy, A.: NHPP-Based software reliability growth modeling and optimal release policy for N-Version programming system with increasing fault detection rate under imperfect debugging. Proc. Natl. Acad. Sci. India Sect. A Phys. Sci. 90, 11–26 (2020)
Peng, R., Ma, X., Zhai, Q.: Software reliability growth model considering first-step and second-step fault dependency. J. Shanghai Jiaotong Univ. (Sci.) 24(4), 477–479 (2019)
Li, Q., Pham, H.: A generalized software reliability growth model with consideration of the uncertainty of operating environments. IEEE Access 7, 84253–84267 (2019)
Utkin, L.V., Coolen, F.P.A.: A robust weighted SVR-based software reliability growth model. Reliab. Eng. Syst. Saf. 176(8), 93–101 (2018)
Briones, B.A.: Wiley encyclopedia of electrical and electronics engineering. Charleston Adv. 21(2), 51–54 (2019)
Dai, Y., Xie, M., Poh, K.: Modeling and analysis of correlated software failures of multiple types. IEEE Trans. Reliab. 54(1), 100–106 (2005)
Rani, P., Mahapatra, G.S.: A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter. Heliyon 5(7) (2019)
Lin, C., Huang, C.: Enhancing and measuring the predictive capabilities of testing-effort dependent software reliability models. J. Syst. Softw. 81(6), 1025–1038 (2008)
Li, Q., Pham, H.: A testing-coverage software reliability model considering fault removal efficiency and error generation. PLOS ONE 12(7), e0181524 (2017)
Huang, C., Kuo, S., Lyu, M.R.: An assessment of testing-effort dependent software reliability growth models. IEEE Trans. Reliab. 56(2), 198–211 (2007)
Jing, Z., Hongwei, L., Gang, C., Xiaozong, Y.: A software reliability growth model considering differences between testing and operation. J. Comput. Res. Dev. 43(3), 503 (2006)
Misra, P.N.: Software reliability analysis. IBM Syst. J. 22(3), 262–270 (1983)
Hui, Z., Liu, X.: Research on software reliability growth model based on gaussian new distribution. Procedia Comput. Sci. 166, 73–77 (2020)
Kapur, P.K., Anand, S., Yamada, S., Yadavalli, V.S.: Stochastic differential equation-based flexible software reliability growth model. Math. Probl. Eng. 2009, 1–15 (2009)
Xie, J., Jinxia, A.N., Zhu, J.: NHPP software reliability growth model considering imperfect debugging. J. Softw. 21(5), 942–949 (2010)
Chatterjee, S., Chaudhuri, B., Bhar, C.: Optimal release time determination using FMOCCP involving randomized cost budget for FSDE-based software reliability growth model. Int. J. Reliab. Qual. Saf. Eng. 27(1), 257–279 (2020)
Yamada, S., Ohba, M., Osaki, S.: S-shaped reliability growth modeling for software error detection. IEEE Trans. Reliab. 32(5), 475–484 (1983)
Laura, P.: Software fault tolerance techniques and implementation. Artech (2001)
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Sun, X., Li, J. (2021). Simulation of Software Reliability Growth Model Based on Fault Severity and Imperfect Debugging. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_12
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DOI: https://doi.org/10.1007/978-3-030-72795-6_12
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