한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

  • 제15권9호
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  • Pages.5652-5659
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  • 2014
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  • 1975-4701(pISSN)
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  • 2288-4688(eISSN)
한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
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모듈의존성을 갖는 불완전수리 다항모듈 소프트웨어의 성능평가에 관한 연구

Performance Evaluation of Multi-Module Software System with Imperfect Debugging and Module Dependency

  • 투고 : 2014.06.05
  • 심사 : 2014.09.11
  • 발행 : 2014.09.30
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초록

소프트웨어를 구성하는 모듈들은 각 모듈에 주어지는 업무들이 동시에 처리될 수 있도록 멀티태스킹이 가능하도록 개발되며, 또한 처리중인 업무들은 완전처리된 업무들과 처리중 모듈고장으로 완전처리 되지 않는 불완전 처리업무로 세분화한다. 이러한 경우 여러 모듈에 동시에 업무가 주어졌을 때, Farlie [11]의 결합확률분포를 기반으로 모듈간의 의존성을 고려하여 업무의 완전처리확률을 평가할 수 있는 모형을 제안하며, 이를 통하여 모듈의존성 모수 값이 커질수록 소프트웨어에 주어진 업무의 완전처리확률은 점점 커짐을 보이고자 한다.

The purpose of this study was to introduce a software task processing evaluation model that considers the following situations: i) a software system is integratedly composed of several number of modules, ii) each modules has its corresponding module task, iii) all module tasks are tested simultaneously, and iv) the processing times of the module tasks are mutually dependent. The software task completion probability with the module dependency was derived using the joint distribution function of Farlie [11]. The results showed that the task completion probability of software increases with increasing module dependency parameter.

키워드

참고문헌

  1. M.L. Shooman and A.K. Trivedi, "A many-state Markov model for computer software performance parameters", IEEE Transactions on reliability, R-25, pp. 66-68, 1976. DOI: http://dx.doi.org/10.1109/TR.1976.5214978
  2. K. Tokuno and S. Yamada, "Markovian software availability measurement based on the number of restoration actions", IEICE Transactions on Fundamentals, E83-A, pp. 835-841, 2000.
  3. C.H. Lee and D.H. Park, "Markovian imperfect software debugging model and its performance", Stochastic Analysis and Applications, 21(4), pp. 849-864, 2003. DOI: http://dx.doi.org/10.1081/SAP-120022866
  4. K. Tokuno and S. Yamada, "Stochastic performance evaluation for multi-task processing system with software availability model", Journal of Quality in Maintenance Engineering, 12, pp. 412-424, 2006. DOI: http://dx.doi.org/10.1108/13552510610705964
  5. S. Gokhale and M.R. Lyu, "A simulation approach to structure-based software reliability analysis", IEEE Transactions on Software Engineering, 31(8), pp. 643-656, 2005. DOI: http://dx.doi.org/10.1109/TSE.2005.86
  6. L. Yu, K. Chen and S. Ramaswamy, "Multiple- parameter coupling metrics for layered component based software", Software Quality Journal, 17, pp. 5-24, 2009. DOI: http://dx.doi.org/10.1007/s11219-008-9052-9
  7. A. Melo, E. Tavares, M. Marinho, E. Sousa, B. Nogueira and P. Maciel, "Development Risk Assessment in Software Projects Using Dependability Models", IEEE 16th International Conference on Computational Science and Engineering, pp. 260-267, 2013. DOI: http://dx.doi.org/10.1109/CSE.2013.49
  8. T. Pitakrat, A.V. Hoorn and L. Grunske, "Increasing Dependability of Component-Based Software Systems by Online Failure Prediction", 2014 European Dependable Computing Conference, pp. 66-69, 2014.
  9. C.H. Lee, Y.H. Kim and D.H. Park, "Evaluation of multi-tasking software system performance with consideration of module dependency", Journal of Software Maintenance and Evolution: Research and Practice, 23(5), pp. 361-374, 2011. DOI: http://dx.doi.org/10.1002/smr.514
  10. P.B. Moranda, "Event-altered rate models for general reliability analysis", IEEE Transactions on Reliability, R-28(5), pp. 376-381, 1979. DOI: http://dx.doi.org/10.1109/TR.1979.5220648
  11. D.J.G. Farlie, "The performance of some correlation coefficients for a general bivariate distribution", Biometrika, 47, pp. 307-323, 1960. DOI: http://dx.doi.org/10.2307/2333302
  12. S.M. Ross, Introduction to probability models(11th Edition), San Diego: Academic press, 2014.