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Software Defect Prediction in Automotive and Telecom Domain: A Life-Cycle Approach

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 555))

Abstract

Embedded software is playing an ever increasing role in providing functionality and user experience. At the same time, size and complexity of this software is also increasing which bring new challenges for ensuring quality and dependability. For developing high quality software with superior dependability characteristics requires an effective software development process with greater control. Methods of software defect predictions can help optimize the software verification and validation activities by providing useful information for test resource allocation and release planning decisions. We review the software development and testing process for two large companies from the automotive and telecom domain and map different defect prediction methods and their applicability to their lifecycle phases. Based on the overview and current trends we also identify possible directions for software defect prediction techniques and application in these domains.

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References

  1. Jones, E.L.: Integrating testing into the curriculum—arsenic in small doses. ACM SIGCSE Bull. 33, 337–341 (2001)

    Article  Google Scholar 

  2. Rana, R., Staron, M., Hansson, J., Nilsson, M.: Defect prediction over software life cycle in automotive domain: state of the art and road map for future. Presented at the 9th International Joint Conference on Software Technologies - ICSOFT-EA, Vienna, Austria (2014)

    Google Scholar 

  3. Staron, M., Meding, W.: Predicting weekly defect inflow in large software projects based on project planning and test status. Inf. Softw. Technol. 50(7), 782–796 (2008)

    Article  Google Scholar 

  4. Almering, V., van Genuchten, M., Cloudt, G., Sonnemans, P.J.: Using software reliability growth models in practice. IEEE Softw. 24(6), 82–88 (2007)

    Article  Google Scholar 

  5. Rana, R., Staron, M., Mellegård, N., Berger, C., Hansson, J., Nilsson, M., Törner, F.: Evaluation of standard reliability growth models in the context of automotive software systems. In: Oivo, M., Jedlitschka, A., Baldassarre, M.T., Heidrich, J. (eds.) PROFES 2013. LNCS, vol. 7983, pp. 324–329. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  6. Rana, R., Staron, M., Berger, C., Hansson, J., Nilsson, M., Törner, F.: Evaluating long-term predictive power of standard reliability growth models on automotive systems. Presented at the 24th Annual International Symposium on Software Reliability Engineering (ISSRE 2013), Pasadena, CA, USA (2013)

    Google Scholar 

  7. Wood, A.: Predicting software reliability. Computer 29(11), 69–77 (1996)

    Article  Google Scholar 

  8. Pham, H.: Software reliability and cost models: perspectives, comparison, and practice. Eur. J. Oper. Res. 149(3), 475–489 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  9. Khoshgoftaar, T.M., Allen, E.B.: Logistic regression modeling of software quality. Int. J. Reliab. Qual. Saf. Eng. 6(04), 303–317 (1999)

    Article  Google Scholar 

  10. Menzies, T., Greenwald, J., Frank, A.: Data mining static code attributes to learn defect predictors. IEEE Trans. Software Eng. 33(1), 2–13 (2007)

    Article  Google Scholar 

  11. Gondra, I.: Applying machine learning to software fault-proneness prediction. J. Syst. Softw. 81(2), 186–195 (2008)

    Article  Google Scholar 

  12. Ceylan, E., Kutlubay, F.O., Bener, A.B.: Software defect identification using machine learning techniques. In: 32nd EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2006, pp. 240–247 (2006)

    Google Scholar 

  13. Fenton, N.E., Neil, M.: A critique of software defect prediction models. IEEE Trans. Software Eng. 25(5), 675–689 (1999)

    Article  Google Scholar 

  14. Fenton, N., Neil, M., Marsh, W., Hearty, P., Radliński, Ł., Krause, P.: On the effectiveness of early life cycle defect prediction with Bayesian Nets. Empir. Softw. Eng. 13(5), 499–537 (2008)

    Article  Google Scholar 

  15. Boehm, B.W.: A spiral model of software development and enhancement. Computer 21(5), 61–72 (1988)

    Article  Google Scholar 

  16. Dieterle, W.: Mechatronic systems: Automotive applications and modern design methodologies. Annu. Rev. Control 29(2), 273–277 (2005)

    Article  Google Scholar 

  17. ISO: International Standard-ISO 26262-Road vehicles-Functional safety. International Organization for Standardization (2011)

    Google Scholar 

  18. Tomaszewski, P., Berander, P., Damm, L.-O.: From traditional to streamline development—opportunities and challenges. Softw. Process Improv. Pract. 13(2), 195–212 (2008)

    Article  Google Scholar 

  19. Chillarege, R., Bhandari, I.S., Chaar, J.K., Halliday, M.J., Moebus, D.S., Ray, B.K., Wong, M.-Y.: Orthogonal defect classification-a concept for in-process measurements. IEEE Trans. Software Eng. 18(11), 943–956 (1992)

    Article  Google Scholar 

  20. Jørgensen, M.: A review of studies on expert estimation of software development effort. J. Syst. Softw. 70(1–2), 37–60 (2004)

    Article  Google Scholar 

Download references

Acknowledgements

The research presented here is done under the VISEE project which is funded by Vinnova and Volvo Cars jointly under the FFI programme (VISEE, Project No: DIARIENR: 2011-04438). We are also thankful to companies involved (Volvo Car Group and Ericsson) for their participation in this study.

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Correspondence to Rakesh Rana .

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Rana, R., Staron, M., Hansson, J., Nilsson, M., Meding, W. (2015). Software Defect Prediction in Automotive and Telecom Domain: A Life-Cycle Approach. In: Holzinger, A., Cardoso, J., Cordeiro, J., Libourel, T., Maciaszek, L., van Sinderen, M. (eds) Software Technologies. ICSOFT 2014. Communications in Computer and Information Science, vol 555. Springer, Cham. https://doi.org/10.1007/978-3-319-25579-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-25579-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25578-1

  • Online ISBN: 978-3-319-25579-8

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