Skip to main content
SpringerLink
Log in

Hidden Markov Model Approach for Software Reliability Estimation with Logic Error

  • Research Article
  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

To ensure the safe operation of any software controlled critical systems, quality factors like reliability and safety are given utmost importance. In this paper, we have chosen to analyze the impact of logic error that is one of the contributors to the above factors. In view of this, we propose a novel framework based on a data driven approach known as software failure estimation with logic error (SFELE). Here, the probabilistic nature of software error is explored by observing the operation of a safety critical system by injecting logic fault. The occurrence of error, its propagations and transformations are analyzed from its inception to end of its execution cycle through the hidden Markov model (HMM) technique. We found that the proposed framework SFELE supports in labeling and quantifying the behavioral properties of selected errors in a safety critical system while traversing across its system components in addition to reliability estimation of the system. Our attempt at the design level can help the design engineers to improve their system quality in a cost-effective manner.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. G. Carrozza, R. Pietrantuono, S. Russo. A software quality framework for large-scale mission-critical systems engineering. Information and Software Technology, vol. 102, pp. 100–116, 2018. DOI: https://doi.org/10.1016/j.infsof.2018.05.009.

    Google Scholar 

  2. E. Kovacs. NISTtool finds errors in complex safety-critical software, [Online], Available: https://www.security-week.com/nist-tool-finds-errors-complex-safety-critical-software, April 26, 2019.

  3. M. Grottke, K. S. Trivedi. Fightingbugs: Remove, retry, replicate, and Rejuvenate. Computer, vol. 40, no. 2, pp. 107–109, 2007. DOI: https://doi.org/10.1109/MC.2007.55.

    Google Scholar 

  4. H. Altinger, Y. Dajsuren, S. Siegl, J. J. Vinju, F. Wotawa. Onerror-class distribution in automotive model-based software. In Proceedings of the 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering, IEEE, Suita, Japan, pp. 688–692, 2016. DOI: https://doi.org/10.1109/saner.2016.81.

    Google Scholar 

  5. W. Mostowski. AUTO-CAAS: Model-Based Fault Prediction and Diagnosis of Automotive Software, Technical Report, Halmstad University, Halmstad, Sweden, 2016.

    Google Scholar 

  6. R. S. Pressman. Software Engineering: A Practitioner’s Approach, 7th ed., New York, USA: McGraw Hill, 2010.

    MATH  Google Scholar 

  7. J. L. Boulanger, V. Q. Dao. Requirementsengineering in a model-based methodology for embedded automotive soft-ware. In Proceedings of IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies, IEEE, Ho Chi Minh City, Vietnam, pp. 263–268, 2008. DOI: https://doi.org/10.1109//RIVF.2008.4586365.

    Google Scholar 

  8. M. L. Shooman. Bohrbugs, mandelbugs, exhaustive testing and unintended automobile acceleration. In Proceedings of IEEE 23rd International Symposium on Software Reliability Engineering Workshops, IEEE, Dallas, USA, pp. 5–6, 2012. DOI: https://doi.org/10.1109/ISSREW.2012.25.

    Google Scholar 

  9. R. L. Glass. Persistentsoftware errors. IEEE Transactions on Software Engineering, vol.SE-7, no. 2, pp. 162–168, 1981. DOI: https://doi.org/10.1109/TSE.1981.230831.

    Google Scholar 

  10. R. L. Glass. Twomistakes and error-free software: A confession. IEEE Software, vol.25, no.4, Article number 96, 2008. DOI: https://doi.org/10.1109/MS.2008.102.

    Google Scholar 

  11. J. B. Bowen. Standarderror classification to support software reliability assessment. In Proceedings of National Computer Conference, ACM, Anaheim, California, USA, pp. 697–705, 1980. DOI: https://doi.org/10.1145/1500518.1500638.

    Google Scholar 

  12. B. J. Czerny, J. G. D’Ambrosio, B. T. Murray, P. Sundaram. EffectiveApplication of Software Safety Techniques for Automotive Embedded Control Systems, Technical Report 2005-01-0785, SAE International, Detroit, USA, 2005. DOI: https://doi.org/10.4271/2005-01-0785.

    Google Scholar 

  13. P. H. Feiler, J. B. Goodenough, A. Gurfinkel, C. B. Weinstock, L. Wrage. Reliability Validation and Improvement Framework, Technical Report CMU/SEI-2012-SR-013, Pittsburgh Pa Software Engineering Institute, Carnegie-Mellon University, Pittsburgh, USA, 2012.

    Google Scholar 

  14. H. Pham. Softwarereliability and fault-tolerant systems: An overview and perspectives. Handbook of Performability Engineering, K. B. Misra, Ed., London, UK: Springer, pp. 1193–1208, 2008. DOI: https://doi.org/10.1007/978-1-84800-131-272.

    Google Scholar 

  15. J. J. Hudak, P. H. Feiler. DevelopingAADL models for control systems: A practitioner’s guide, [Online], Available: http://www.sei.cmu.edu/reports/07tr014.pdf, 2007.

    Google Scholar 

  16. A. Hosseinzadeh-Mokarram, A. Isazadeh, H. Izadkhah. Earlyreliability assessment of component-based software system using colored petri net. Turkish Journal of Electrical Engineering & Computer Sciences, vol.27, pp. 2681–2696, 2019. DOI: https://doi.org/10.3906/elk-1805-82.

    Google Scholar 

  17. F. Salfner. Predictingfailures with hidden Markov models. In Proceedings of the 5th Europe Dependable Computer Conference, pp.41–46 2005, [Online], Available: http://www.rok.informatik.hu-berlin.de/Members/Members/ salfner/publications/salfner05predicting.pdf, 2005.

    Google Scholar 

  18. L. Rabiner. Atutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, vol.77, no. 2, pp. 257–286, 1989. DOI: https://doi.org/10.1109/5.18626.

    Google Scholar 

  19. R. Bharathi, R. Selvarani. Softwarereliability assessment of safety critical system using computational intelligence. International Journal of Software Science and Computational Intelligence, vol.11, no. 3, pp. 1–25, 2019. DOI: https://doi.org/10.4018/ijssci.2019070101.

    Google Scholar 

  20. A. Sundararajan, R. Selvarani. Casestudy of failure analysis techniques for safety critical systems. In Proceedings of the Second International Conference on Computer Science, Engineering and Applications, Springer, New Delhi, India, pp. 367–377, 2012. DOI: https://doi.org/10.1007/978-3-642-30157-5_36.

    Google Scholar 

  21. I. Turner, C. Smidts. Integrateddesign-stage failure analysis of software-driven hardware systems. IEEE Transactions on Computers, vol.60, no.8, pp. 1072–1084, 2011. DOI: https://doi.org/10.1109/TC.2010.245.

    MathSciNet  MATH  Google Scholar 

  22. A. Avizienis, J. C. Laprie, B. Randell, C. Landwehr. Basicconcepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, vol.1, no. 1, pp. 11–33, 2004. DOI: https://doi.org/10.1109/TDSC.2004.2.

    Google Scholar 

  23. J. K. Horner, J. Symons. Understandingerror rates in software engineering: Conceptual, empirical, and experimental approaches. Philosophy & Technology, vol.32, no.2, pp. 363–378, 2019. DOI: https://doi.org/10.1007/s13347-019-00342-1.

    Google Scholar 

  24. M. Hamill, K. Goseva-Popstojanova. Exploring fault types, detection activities, and failure severity in an evolving safety-critical software system. Software Quality Journal, vol.23, no. 2, pp. 229–265, 2015. DOI: https://doi.org/10.1007/S11219-014-9235-5.

    Google Scholar 

  25. J. A. Duraes, H. S. Madeira. Emulationof software faults: A field data study and a practical approach. IEEE Transactions on Software Engineering, vol.32, no. 11, pp.849–867, 2006. DOI: https://doi.org/10.1109/TSE.2006.113.

    Google Scholar 

  26. J. Alonso, M. Grottke, A. P. Nikora, K. S. Trivedi. Anempirical investigation of fault repairs and mitigations in space mission system software. In Proceedings of 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, IEEE, Budapest, Hungary, pp. 1–8, 2013. DOI: https://doi.org/10.1109/DSN.2013.6575355.

    Google Scholar 

  27. J. Alonso, M. Grottke, A. P. Nikora, K. S. Trivedi. Thenature of the times to flight software failure during space missions. In Proceedings of IEEE 23rd International Symposium on Software Reliability Engineering, IEEE, Dallas, USA, pp. 331–340, 2012. DOI: https://doi.org/10.1109/ISSRE.2012.32.

    Google Scholar 

  28. F. Zhang, X. S. Zhou, Y. W. Dong, J. W. Chen. Considerof fault propagation in architecture-based software reliability analysis. In Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, IEEE, Rabat, Morocco, pp. 783–786, 2009. DOI: https://doi.org/10.1109/AICCSA.2009.5069416.

    Google Scholar 

  29. S. G. Shu, Y. C. Wang, Y. K. Wang. Aresearch of architecture-based reliability with fault propagation for software-intensive systems. In Proceedings of Annual Reliability and Maintainability Symposium, IEEE, Tucson, USA, pp. 1–6, 2016. DOI: https://doi.org/10.1109/RAMS.2016.7447984.

    Google Scholar 

  30. V. Cortellessa, V. Grassi. Amodeling approach to analyze the impact of error propagation on reliability of component-based systems. In Proceedings of the 10th International Symposium on Component-Based Software Engineering, Springer, Medford, USA, pp. 140–156, 2007. DOI: https://doi.org/10.1007/978-3-540-73551-910.

    Google Scholar 

  31. M. Hiller, A. Jhumka, N. Suri. EPIC: Profiling the propagation and effect of data errors in software. IEEE Transactions on Computer, vol.53, no. 5, pp. 512–530, 2004. DOI: https://doi.org/10.1109/TC.2004.1275294.

    Google Scholar 

  32. A. Jhumka, M. Leeke. Theearly identification of detector locations in dependable software. In Proceedings of the 22nd IEEE International Symposium on Software Reliability Engineering, IEEE, Hiroshima, Japan, pp. 40–49, 2011. DOI: https://doi.org/10.1109/ISSRE.2011.34.

    Google Scholar 

  33. L. Fiondella, S. S. Gokhale. Architecture-based software reliability with error propagation and recovery. In Proceedings of International Symposium on Performance Evaluation of Computer and Telecommunication Systems, IEEE, Toronto, Canada, pp. 38–45, 2013.

    Google Scholar 

  34. S. Sinha, N. Kumar Goyal, R. Mall. Earlyprediction of reliability and availability of combined hardware-software systems based on functional failures. Journal of Systems Architecture, vol. 91, pp. 23–38, 2019. DOI: https://doi.org/10.1016/j.sysarc.2018.10.007.

    Google Scholar 

  35. X. W. Wu, C. Li, X. Wang, H. J. Yang. Acreative approach to reducing ambiguity in scenario-based software architecture analysis. International Journal of Automation and Computing, vol. 16, no. 2, pp. 248–260, 2019. DOI: https://doi.org/10.1007/s11633-017-1102-y.

    Google Scholar 

  36. NASA Software Safety Guidebook, NASA-GB-8719.13, 2004.

  37. R. C. Cheung. Auser-oriented software reliability model. IEEE Transactions on Software Engineering, vol.SE-6, no. 2, pp. 118–125, 1980.

    MATH  Google Scholar 

  38. R. Baldoni, L. Montanari, M. Rizzuto. On-line failure prediction in safety-critical systems. Future Generation Computer Systems, vol.45, pp. 123–132, 2015. DOI: https://doi.org/10.1016/j.future.2014.11.015.

    Google Scholar 

  39. A. A. Markov. Anexample of statistical investigation of the text Eugene onegin concerning the connection of samples in chains. Science in Context, vol.19, no. 4, pp. 591–600, 2006. DOI: https://doi.org/10.1017/S0269889706001074.

    MATH  Google Scholar 

  40. K. Wang, X. X. Long, R. F. Li, L. J. Zhao. Adiscriminative algorithm for indoor place recognition based on clustering of features and images. International Journal of Automation and Computing, vol.14, no. 4, pp. 407–419, 2017. DOI: https://doi.org/10.1007/s11633-017-1081-z.

    Google Scholar 

  41. S. Honamore, S. K. Rath. Aweb service reliability prediction using HMM and fuzzy logic models. Procedia Computer Science, vol. 93, pp. 886–892, 2016. DOI: https://doi.org/10.1016/j.procs.2016.07.273.

    Google Scholar 

  42. G. I. F. Neyens, D. Zampunieris. Usinghidden markov models and rule-based sensor mediation on wearable eHealth devices. In Procedings of the 11th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, IARIA, Barcelona, Spain, pp. 19–24, 2017.

    Google Scholar 

  43. E. Dorj, C. C. Chen, M. Pecht. Abayesian hidden markov model-based approach for anomaly detection in electronic systems. In Proceedings of IEEE Aerospace Conference, IEEE, Big Sky, USA, pp. 1–10, 2013. DOI: https://doi.org/10.1109/AERO.2013.6497204.

    Google Scholar 

  44. S. Ghassempour, F. Girosi, A. Maeder. Clusteringmultivariate time series using hidden Markov models. International Journal of Environmental Research and Public Health, vol.11, no. 3, pp. 2741–2763, 2014. DOI: https://doi.org/10.3390/ijerphll0302741.

    Google Scholar 

  45. A. Simões, J. M. Viegas, J. T. Farinha, I. Fonseca. Thestate of the art of hidden markov models for predictive maintenance of diesel engines. Quality and Reliability Engineering International, vol.33, no. 8, pp. 2765–2779, 2017. DOI: https://doi.org/10.1002/qre.2130.

    Google Scholar 

  46. J. B. Durand, O. Gaudoin. Softwarereliability modelling and prediction with hidden Markov chains. Statistical Modelling, vol. 5, no. 1, pp. 75–93, 2005.

    MathSciNet  MATH  Google Scholar 

  47. SimulinkDemo. Modeling an anti-lock braking system - Matlab & Simulink - MathWorks India, [Online], Available: https://in.mathworks.com/help/simulink/slref/modeling-an-anti-lock-braking-system.html?s_tid=srch-title, January 5, 2019.

    Google Scholar 

  48. R. Bharathi, R. Selvarani. Amachine learning approach for quantifying the design error propagation in safety critical software system. IETE Journal of Research, to be published. DOI: https://doi.org/10.1080/03772063.2019.1611490.

  49. W. L. Wang, D. Pan, M. H. Chen. Architecture-based software reliability modeling. Journal of Systems and Software, vol.79, no. 1, pp. 132–146, 2006. DOI: https://doi.org/10.1016/j.jss.2005.09.004.

    Google Scholar 

  50. S. R. Devi, P. Arulmozhivarman, C. Venkatesh, P. Agarwal. Performancecomparison of artificial neural network models for daily rainfall prediction. International Journal of Automation and Computing, vol.13, no. 5, pp. 417–427, 2016. DOI: https://doi.org/10.1007/s11633-016-0986-2.

    Google Scholar 

  51. Y. Z. Jin, H. Zhou, H. J. Yang, S. J. Zhang, J. D. Ge. Anapproach to locating delayed activities in software processes. International Journal of Automation and Computing, vol.15, no. 1, pp. 115–124, 2018. DOI: https://doi.org/10.1007/s11633-017-1092-9.

    Google Scholar 

  52. R. Roshandel. Calculating architectural reliability via modeling and analysis. In Proceedings of the 26th International Conference on Software Engineering, IEEE, Edinburgh, UK, pp. 69–71, 2004. DOI: https://doi.org/10.1109/icse.2004.1317426.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, PES University, Bangalore, 560100, India

    R. Bharathi

  2. Research scholar, Visvesvaraya Technological University, Belagavi, 590018, India

    R. Bharathi

  3. Department of Computer Science, Alliance University, Bangalore, 562106, India

    R. Selvarani

Authors
  1. R. Bharathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Selvarani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Bharathi.

Additional information

Recommended by Associate Editor Xun Chen

R. Bharathi received the M.E. degree in computer science and engineering from Bharathiar University, India in 2001. She has a progressive teaching experience of 20 years and currently working as a faculty at PES University, Electronic City Campus, India and research scholar at Vis-veswaraya Technological University, Belagavi, India.

Her research interests include safety critical software systems, machine learning, computational intelligence, and software design quality estimation.

R. Selvarani received the Ph.D. degree from Jawaharlal Nehru Technological University, India in 2009. She is currently working as a professor having a progressive teaching experience of 28 years and program director for doctoral program at Alliance College of Engineering and Design, Alliance University, India. She has a patent in software architecture and design domain. Her publication in Information and Software Technology was selected in terms of having “the best content” in the area of Information Technology for the year 2012 by VERTICAL NEWS, USA. She is carrying out collaborative research with Leeds Metropolitan University, UK and her name is listed in Who’s Who for Science and Technology, USA.

Her research interests include machine learning, Internet of things software design quality estimation, service oriented cloud applications, software safety critical systems, and QoS in distributed networks.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bharathi, R., Selvarani, R. Hidden Markov Model Approach for Software Reliability Estimation with Logic Error. Int. J. Autom. Comput. 17, 305–320 (2020). https://doi.org/10.1007/s11633-019-1214-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-019-1214-7

Keywords

Search

Navigation