Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
DE
EN
Books
Journals
Events
Topic Page
Marketing
Log in
Learn more about individual access
Request company access
Heavy-Duty Engines 2025 | 18 + 19 November 2025

Commercial Vehicles | Agricultural and Construction Machinery | Marine

Take part as a speaker in the 20th International MTZ Conference for the exchange of experience on industrial and commercial vehicle engines. Submit a subject proposal online!
EXTENDED SEARCH
Log in
Top
Journal of Electronic Testing
Published in:

28-09-2021

Neuro-Fuzzy Evaluation of the Software Reliability Models by Adaptive Neuro Fuzzy Inference System

Authors: Milos Milovancevic, Aleksandar Dimov, Kamen Boyanov Spasov, Ljubomir Vračar, Miroslav Planić

Published in: Journal of Electronic Testing | Issue 4/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Software quality has become a key aspect of any electronic system. In this respect, software reliability is an important quality characteristic and there are many models that aim to estimate the reliability from different perspectives. However, there are no industry established reliability models. There is need to estimate which reliability model has the best performance. In this study several reliability models are analyzed by a soft computing approach, called adaptive neuro-fuzzy inference system (neuro-fuzzy), in order to estimate the models’ capability based on root mean square errors (RMSE). Various aspects of accuracy of some of the well-known software reliability models have been used in this work. According to the results Non-Homogeneous Poisson Process Model (NHPP) is the best software reliability model. A combination of Linear Littlewood-Verall (LV) and NHPP is the optimal combination of two software reliability models. In other words, the best results could be achieved if one combines the LV and NHPP models.
previous article Review of Manufacturing Process Defects and Their Effects on Memristive Devices
next article Clock-Less DFT and BIST for Dual-Rail Asynchronous Circuits

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Literature
1.
Wang J, Zhu P, He B, Deng G, Zhang C, Huang X (2021) An adaptive neural sliding mode control with ESO for uncertain nonlinear systems. Int J Control Autom Syst 19(2):687–697CrossRef
2.
Jing L, Pan Y, Wang T, Qu R, Cheng PT (2021) Transient analysis and verification of a magnetic gear integrated permanent magnet brushless machine with Halbach arrays. IEEE journal of Emerging and Selected Topics in Power Electronics.
3.
Zhang L, Zheng H, Wan T, Shi D, Lyu L, Cai G (2021) An integrated control algorithm of power distribution for islanded microgrid based on improved virtual synchronous generator. IET Renew Power Gener
4.
Liu C, Deng F, Heng Q, Cai X, Zhu R, Liserre M (2020) Crossing thyristor branches based hybrid modular multilevel converters for DC line faults. IEEE Transactions on Industrial Electronics
5.
Wang B, Jahanshahi H, Volos C, Khan Bekiros S, MA, Agarwal P, Aly AA, (2021) A new RBF neural network-based fault-tolerant active control for fractional time-delayed systems. Electronics 10(12):1501CrossRef
6.
Han X, Wei Z, Zhang B, Li Y, Du T, Chen H (2021) Crop evapotranspiration prediction by considering dynamic change of crop coefficient and the precipitation effect in back-propagation neural network model. J Hydrol 596:126104
7.
Qin C, Jin Y, Tao J, Xiao D, Yu H, Liu C, Liu C (2021) DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection. Measurement 180:109548
8.
Niu M, Lin Y, Zou Q (2021) sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks. Plant Mol Biol 105(4):483–495CrossRef
9.
Çömez N, Çivi C, Durmuş H (2019) Reliability evaluation of hardness test methods of hardfacing coatings with hypoeutectic and hypereutectic microstructures. Int J Miner Metal Mater 26(12):1585–1593CrossRef
10.
Weng L, He Y, Peng J, Zheng J, Li X (2021) Deep cascading network architecture for robust automatic modulation classification. Neurocomputing 455:308–324
11.
Niu Z, Zhang B, Li D, Ji D, Liu Y, Feng Y, Fan Y (2021) A mechanical reliability study of 3-dB waveguide hybrid couplers in submillimeter and terahertz bands. Front Inform Technol Electron Eng 22(8):1104–1113CrossRef
12.
Xie W, Zhang R, Zeng D, Shi K, Zhong S (2020) Strictly dissipative stabilization of multiple-memory Markov jump systems with general transition rates: A novel event-triggered control strategy. Int J Robust Nonlin Control 30(5):1956–1978MathSciNetCrossRef
13.
Luo J, Li M, Liu X, Tian W, Zhong S, Shi K (2020) Stabilization analysis for fuzzy systems with a switched sampled-data control. J Franklin Institute 357(1):39–58MathSciNetCrossRef
14.
Zhao C, Liu X, Zhong S, Shi K, Liao D, Zhong Q (2021) Secure consensus of multi-agent systems with redundant signal and communication interference via distributed dynamic event-triggered control. ISA Transactions 112:89–98
15.
Yan LM, Shen J, Li JP, Li ZB, Yan XD (2010) Deformation behavior and microstructure of an Al-Zn-Mg-Cu-Zr alloy during hot deformation. Int J Min Metal Mater 17(1):46–52CrossRef
16.
Avizienis A, Laprie JC, Randell B, Landwehr C (2004) Basic concepts and taxonomy of dependable and secure computing. IEEE Trans Dependable Secure Comput 1(1):11–33CrossRef
17.
Chang YC, Leu LY (1998) A state space model for software reliability. Ann Inst Stat Math 50(4):789–799CrossRef
18.
Chatterjee S, Nigam S, Singh JB, Upadhyaya LN (2011) Transfer function modelling in software reliability. Computing 92(1):33–48MathSciNetCrossRef
19.
Chatterjee S, Nigam S, Roy A (2017) Software fault prediction using neuro-fuzzy network and evolutionary learning approach. Neural Comput Appl 28(1):1221–1231CrossRef
20.
Crespo AN, Jino M, Pasquini A, Maldonado JC (2008) A binomial software reliability model based on coverage of structural testing criteria. Empir Softw Eng 13(2):185–209CrossRef
21.
22.
Dimov A (2011) Empirical Analysis of Software Reliability Models Predictive Ability. In Third International Conference on Software, Services and Semantic Technologies S3T 2011 (pp. 139–146). Springer, Berlin, Heidelberg
23.
Dimov A, Chandran SK, Punnekkat S (2010). How do we collect data for software reliability estimation?. In Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies (pp. 155–160)
24.
Febrero F, Calero C, Moraga MÁ (2016) Software reliability modeling based on ISO/IEC SQuaRE. Inf Softw Technol 70:18–29CrossRef
25.
Goel AL, Okumoto K (1979) Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Trans Reliab 28(3):206–211CrossRef
26.
Gokhale SS, Trivedi KS (1999) A time/structure based software reliability model. Ann Softw Eng 8(1):85–121CrossRef
27.
Ivanov V, Reznik A, Succi G (2018) Comparing the reliability of software systems: A case study on mobile operating systems. Inf Sci 423:398–411CrossRef
28.
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRef
29.
Jelinski Z, Moranda PB (1972) Software reliability research, In statistical computer performance evaluation, ed. w. Freiberger, New York
30.
Karunanithi N, Whitley D, Malaiya YK (1992) Prediction of software reliability using connectionist models. IEEE Trans Software Eng 18(7):563CrossRef
31.
Kapur PK, Garg RB, Chanda U, Tandon A (2010) Development of software reliability growth model incorporating enhancement of features and related release policy. Int J Syst Assur Eng Manag 1(1):52–58CrossRef
32.
Li Q, Pham H (2017) NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage. Appl Math Model 51:68–85MathSciNetCrossRef
33.
Littlewood B (1979) The Littlewood-Verrall model for software reliability compared with some rivals. J Syst Softw 1:251–258CrossRef
34.
Lu M, Brocklehurst S, Littlewood B (1995) Combination of predictions obtained from different software reliability growth models. In Predictably Dependable Computing Systems (pp. 421–439). Springer, Berlin, Heidelberg
35.
Moranda PB (1975) Prediction of software reliability during debugging. In Proc 1975 Annu Reliab Maintainance Symp
36.
Musa JD, Okumoto K (1984) A logarithmic Poisson execution time model for software reliability measurement. In Proceedings of the 7th international conference on Software engineering (pp. 230–238)
37.
Musa JD, Okumoto K (1988) Application of basic and logarithmic Poisson execution time models in software reliability measurement. In Software Reliability Modelling and Identification (pp. 68–100). Springer, Berlin, Heidelberg
38.
Nikora AP (1994) Computer Aided Software Reliability Estimation. User's Guide. COSMIC Program# NPO-19307, Version 2
39.
Sahu K, Srivastava RK (2018) Soft computing approach for prediction of software reliability. ICIC Express Lett 12(12):1213–1222
40.
Saito Y, Dohi T (2016) Predicting software reliability via completely monotone nonparametric estimator with grouped data. J Syst Softw 117:296–306CrossRef
41.
Song KY, Chang IH, Pham H (2017) A three-parameter fault-detection software reliability model with the uncertainty of operating environments. J Syst Sci Syst Eng 26(1):121–132CrossRef
42.
Pham H (2016) A generalized fault-detection software reliability model subject to random operating environments. Vietnam J Comput Sci 3(3):145–150CrossRef
43.
Utkin LV, Coolen FP (2018) A robust weighted SVR-based software reliability growth model. Reliab Eng Syst Saf 176:93–101CrossRef
44.
Wang J, Zhang C (2018) Software reliability prediction using a deep learning model based on the RNN encoder–decoder. Reliab Eng Syst Saf 170:73–82CrossRef
45.
Wang J, Wu Z, Shu Y, Zhang Z (2016) An optimized method for software reliability model based on nonhomogeneous Poisson process. Appl Math Model 40(13–14):6324–6339MathSciNetCrossRef
46.
Wang Q, Lu Y, Xu Z, Han J (2011) Software reliability model for component interaction mode. J Electron 28(4):632–642
47.
Williams DP, Vivekanandan P (2002) Truncated software reliability growth model. J Comput Appl Math 9(2):591–599MathSciNetMATH
48.
Yadav HB, Yadav DK (2017) Early software reliability analysis using reliability relevant software metrics. Int J Syst Assur Eng Manag 8(4):2097–2108CrossRef
49.
Zhang J, Tu JX, Chen ZN, Yan XG (2009) Quasi-Bayesian software reliability model with small samples. J Shanghai Univ (English Edition) 13(4):301–304CrossRef
50.
Yan YF, Lü ZM (2021) Multi-objective quality control method for cold-rolled products oriented to customized requirements. Int J Min Metal Mater 28(8):1332–1342CrossRef
51.
Li B, Wu Y, Song J, Lu R, Li T, Zhao L (2020) DeepFed: federated deep learning for intrusion detection in industrial cyber–physical systems. IEEE Transactions on Industrial Informatics 17(8):5615–5624CrossRef
52.
Li B, Xiao G, Lu R, Deng R, Bao H (2019) On feasibility and limitations of detecting false data injection attacks on power grid state estimation using D-FACTS devices. IEEE Transactions on Industrial Informatics 16(2):854–864CrossRef
53.
Zhao C, Zhong S, Zhang X, Zhong Q, Shi K (2020) Novel results on nonfragile sampled-data exponential synchronization for delayed complex dynamical networks. Int J Robust Nonlin Control 30(10):4022–4042MathSciNetCrossRef
54.
Pazhoohan J, Beiki H, Esfandyari M (2019) Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank. Int J Min Metal Mater 26(5):538–546CrossRef
55.
Jiang L, Zhang B, Han S, Chen H, Wei Z (2021) Upscaling evapotranspiration from the instantaneous to the daily time scale: Assessing six methods including an optimized coefficient based on worldwide eddy covariance flux network. J Hydrol 596, 126135
56.
Shariati M, Shariati A (2021) Hybridization of metaheuristic algorithms with adaptive neuro-fuzzy inference system to predict load-slip behavior of angle shear connectors at elevated temperatures. Compos Struct 114524
57.
Shariati M, Armaghani DJ, Khandelwal M, Zhou J, Khorami M (2021) Assessment of longstanding effects of fly ash and silica fume on the compressive strength of concrete using extreme learning machine and artificial neural network. J Adv Eng Comput 5(1):50–74CrossRef
Metadata
Title
Neuro-Fuzzy Evaluation of the Software Reliability Models by Adaptive Neuro Fuzzy Inference System
Authors
Milos Milovancevic
Aleksandar Dimov
Kamen Boyanov Spasov
Ljubomir Vračar
Miroslav Planić
Publication date
28-09-2021
Publisher
Springer US
Published in
Journal of Electronic Testing / Issue 4/2021
Print ISSN: 0923-8174
Electronic ISSN: 1573-0727
DOI
https://doi.org/10.1007/s10836-021-05964-y

Other articles of this Issue 4/2021

Review of Manufacturing Process Defects and Their Effects on Memristive Devices

  • Open Access

Test Technology Newsletter

Design of Radiation Hardened Latch and Flip-Flop with Cost-Effectiveness for Low-Orbit Aerospace Applications

Fault-Aware Dependability Enhancement Techniques for Phase Change Memory

Neural Network-based Online Fault Diagnosis in Wireless-NoC Systems

Clock-Less DFT and BIST for Dual-Rail Asynchronous Circuits