Abstract
Some of the quality parameters for any successful open source software may be attributed to affordability, availability of source code, re-distributability, and modifiability etc. Quality of software can be further improvised subsequently by either users or associated developers by constantly monitoring some of the reliability aspects. Since multiple users are allowed to modify the code there is a potential threat for security, which might degrade the reliability of software. Bug tracking systems are often considered to monitor various software faults, detected mostly in open source software projects. Various authors have made research in this direction by applying different techniques in order to improve the reliability of open source software projects. In this work, an various machine learning models have been implemented to examine the reliability of the software. An extensive numerical illustration has also been presented for bug data recorded on bug tracking system. The effectiveness of machine learning models for estimating the level of faults associated with the systems has been verified by comparing it with similar approaches as available in the literature.
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References
Lyu, M.R. (ed.): Handbook of Software Reliability Engineering. IEEE Computer Society Press, Los Alamitos (1996)
Yamada, S.: Software Reliability Modeling: Fundamentals and Applications. Springer, Tokyo/Heidelberg (2014). https://doi.org/10.1007/978-4-431-54565-1
Tamura, Y., Matsumoto, M., Yamada, S.: Software reliability model selection based on deep learning. In: Proceedings of the International Conference on Industrial Engineering, Management Science and Application, Korea, 23–26 May 2016, pp. 77–81 (2016)
Behera, R.K., Shukla, S., Rath, S.K., Misra, S.: Software reliability assessment using machine learning technique. In: Gervasi, O., et al. (eds.) ICCSA 2018. LNCS, vol. 10964, pp. 403–411. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95174-4_32
Yamada, S., Tamura, Y.: OSS Reliability Measurement and Assessment. Springer, London (2016). https://doi.org/10.1007/978-3-319-31818-9
Goel, A.L.: Software reliability models: assumption, limitations, and applicability. IEEE Trans. Softw. Eng. 12, 1411–1423 (1985)
Musa, J.D., Iannino, A., Okumoto, K.: Software Reliability, Measurement, Prediction and Application. McGraw-Hill, New York (1987)
Sharma, K., et al.: Selection of optimal software reliability growth models using a distance based method. IEEE Trans. Reliab. 59(2), 266–276 (2010)
Schick, G.H., Wolverton, R.W.: An analysis of competing software reliability models. IEEE Trans. Softw. Eng. 2, 104–120 (1978)
Sukert, A.N.: Empirical validation of three software errors predictions models. IEEE Trans. Reliab. 28, 199–205 (1979)
Brocklehurst, S., Chan, P.Y., Littlewood, B., Snell, J.: Recalibrating software reliability models. IEEE Trans. Softw. Eng. SE–16(4), 458–470 (1990)
Abdel-Ghaly, A.A., Chan, P.Y., Littlewood, B.: Evaluation of competing software reliability predictions. IEEE Trans. Softw. Eng. SE–12(12), 950–967 (1986)
Khoshgoftaar, T.M.: On model selection in software reliability. In: 8th Symposium in Computational Statistics (Compstat 1988), pp. 13–14, August 1988
Lyu, M., Nikora, A.: CASREA-a computer-aided software reliability estimation tool. In: Proceedings of the Fifth International Workshop on Computer-Aided Software Engineering, Montreal, pp. 264–275 (1992)
Shukla, S., Behera, R.K., Misra, S., Rath, S.K.: Software reliability assessment using deep learning technique. In: Chakraverty, S., Goel, A., Misra, S. (eds.) Towards Extensible and Adaptable Methods in Computing, pp. 57–68. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-2348-5_5
Stringfellow, C., et al.: An empirical method for selecting software reliability growth models. Empir. Softw. Eng. J. 7, 319–343 (2002)
The Mozilla Software Foundation: The Mozilla project. https://bugzilla.mozilla.org/
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Behera, R.K., Rath, S.K., Misra, S., Leon, M., Adewumi, A. (2019). Machine Learning Approach for Reliability Assessment of Open Source Software. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_35
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