The widespread integration of big data technologies into daily life has heightened the demand for reliable software systems, yet research on open source software reliability lags behind that of closed source software, whose theoretical framework is already mature. Conventional software reliability growth models fail to address the unique attributes of open source big data systems, creating an urgent need for a specialized reliability assessment tool. To fill this gap, this paper proposes an imperfect debugging reliability model that considers big data technology characteristics and captures the complex dynamics of fault removal efficiency using a Weibull distribution. Rigorous experimental validation demonstrates the model’s exceptional fitting accuracy and predictive power. By enabling effective and precise reliability evaluation of open source big data system software, this work not only advances the theoretical frontier of software reliability but also provides actionable insights for optimizing software development and testing processes.
TamuraYMiyaokaKYamadaS. Reliability analysis based on three-dimensional stochastic differential equation for big data on cloud computing. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, December, 2014, pp. 863–867. IEEE.
2.
TamaruYYamadaS. Reliability analysis based on AHP and software reliability models for big data on cloud computing. Int J Stat Appl2014; 1(1): 43–49.
3.
CaoRGaoJ. Research on reliability evaluation of big data system. In 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), April, 2018, pp. 261–265. IEEE.
4.
GovindasamyPDillibabuR. Development of software reliability models using a hybrid approach and validation of the proposed models using big data. J Supercomput2020; 76(4): 2252–2265.
5.
KumarRKumarSTiwariSK. A study of software reliability on big data open source software. Int J Syst Assur Eng Manag2019; 10(2): 242–250.
6.
ZhangXTengXPhamH. Considering fault removal efficiency in software reliability assessment. IEEE Trans Syst Man Cybern A Syst Hum2003; 33(1): 114–120.
7.
ChatterjeeSShuklaA. An ideal software release policy for an improved software reliability growth model incorporating imperfect debugging with fault removal efficiency and change point. Asia Pac J Oper Res2017; 34(03): 1740017.
8.
HaqueMAAhmadN. A software reliability model using fault removal efficiency. J Reliab Stat Stud2022; 15(02): 459–472.
9.
VermaVAnandSKapurPK, et al. Unified framework to assess software reliability and determine optimal release time in presence of fault reduction factor, error generation and fault removal efficiency. Int J Syst Assur Eng Manag2022; 13(5): 2429–2441.
10.
SamalUKumarA. An enhanced software reliability growth model considering dynamic fault removal efficiency and residual error change rate. J Inf Sci Eng2024; 40(6): 1321.
11.
PradhanVKumarADharJ. Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time. Proc Inst Mech Eng O J Risk Reliab2022; 236(1): 18–36.
12.
ShrivastavaAKSharmaRPhamH. Software reliability and cost models with warranty and life cycle. Proc Inst Mech Eng O J Risk Reliab2023; 237(1): 166–179.
13.
AggarwalAKumarSGuptaR. Testing coverage based NHPP software reliability growth modeling with testing effort and change-point. Int J Syst Assur Eng Manag2024; 15(11): 5157–5166.
14.
NatarajanNMahapatraAMahapatraGS. Imperfect debugging-based modelling of fault detection incorporating change point for software reliability analysis. Proc Inst Mech Eng O J Risk Reliab2025; 239(5): 1153–1163.
15.
BeheraAAgarwalP. Modeling software reliability with power law testing effort function under operational uncertain environment. J Softw Evol Process2025; 37(7): e70037.
16.
HussainAOraibiYMashikhinZ, et al. New software reliability growth model: piratical swarm optimization-based parameter estimation in environments with uncertainty and dependent failures. Stat Optim Inf Comput2025; 13(1): 209–221.
17.
SamalU. Software reliability growth model considering imperfect debugging and fault removal efficiency. Qual Reliab Eng Int2025; 41(4): 1268–1278.
18.
ShafiqASindhuTNLoneSA, et al. An updated software reliability model using the Shanker model and failure data. Qual Reliab Eng Int2024; 40(4): 2078–2095.
19.
GargMKumarVChaudharyK, et al. Uncertain differential equation based software belief reliability growth model (SBRGM) considering software patching. Int J Syst Assur Eng Manag. Epub ahead of print 2024. DOI: 10.1007/s13198-023-02225-3.
20.
YangJZhaoMChenJ. ELS algorithm for estimating open source software reliability with masked data considering both fault detection and correction processes. Commun Stat Theo Meth2022; 51(19): 6792–6817.
21.
SarafIIqbalJShrivastavaAK, et al. Modelling reliability growth for multi-version open source software considering varied testing and debugging factors. Qual Reliab Eng Int2022; 38(4): 1814–1825.
22.
NguyenHHuynhQ. New non-homogeneous Poisson process software reliability model based on a 3-parameter S-shaped function. IET Softw2022; 16(2): 214–232.
23.
YangJLiuYXieM, et al. Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes. J Syst Softw2016; 115: 102–110.
24.
SamalUKumarA. Incorporating human dynamics into software reliability analysis: learning, fatigue, and efficiency considerations. Int J Syst Assur Eng Manag. Epub ahead of print 2024. DOI: 10.1007/s13198-024-02368-x 1–10.
25.
FangCCMaLKuoW. Integrating human learning factors and Bayesian analysis into software reliability growth models for optimal release strategies. IEEE Access2025; 13: 11846–11862.
26.
YadavAKSrivastavaSPantM. A generalized software reliability prediction model for module based software incorporating testing effort with cost model. Life Cycle Reliab Safety Eng2024; 13(4): 419–436.
27.
YamadaSOhbaMOsakiS. S-shaped reliability growth modeling for software error detection. IEEE Trans Reliab1983; R-32: 475–484.
28.
EhrlichWKEmersonTJ. Modeling software failures and reliability growth during system testing. In: Proceedings of the 9th International Conference on Software Engineering (ICSE’87), Monterey, CA, March 30–April, 2, 1987, pp. 72–82.
29.
ChiuKCHuangYSLeeTZ. A study of software reliability growth from the perspective of learning effects. Reliab Eng Syst Saf2008; 93: 1410–1421.
30.
LiPLHerbslebJShawM. Forecasting field defect rates using a combined time-based and metrics-based approach: a case study of OpenBSD. In: Proceedings of the 16th IEEE International Symposium on Software. Reliability Engineering, Chicago, IL, 2005, pp. 193–202.
31.
PhamH. A new software reliability model with Vtub-shaped fault-detection rate and the uncertainty of operating environments. Optimization2014; 63(10): 1481–1490.
32.
OhbaM. Inflection S-shaped software reliability growth model, stochastic models in reliability theory. Springer, 1984, pp. 144–162.
33.
LiXLiYFXieM, et al. Reliability analysis and optimal version-updating for open source software. Inf Softw Tech2011; 53(9): 929–936.
34.
SamalUKumarA. A software reliability model incorporating fault removal efficiency and it’s release policy. Comput Stat2024; 39(6): 3137–3155.
