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研究生：	邱桂珍 Chiu, Kuei-Chen
論文名稱：	學習效果觀點之軟體可靠度成長模型 A Study of Software Reliability Growth from the Perspective of Learning Effects
指導教授：	黃宇翔 Huang, Yeu-Shiang
學位類別：	博士 Doctor
系所名稱：	管理學院 - 工業與資訊管理學系 Department of Industrial and Information Management
論文出版年：	2009
畢業學年度：	97
語文別：	英文
論文頁數：	88
中文關鍵詞：	軟體成長、非齊次蒲瓦松程序、貝氏決策方法、軟體可靠度
外文關鍵詞：	Nonhomogeneous Poisson Process, Bayesian approach, Software Reliability, Reliability Growth
相關次數：	點閱：162 下載：1
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在軟體測試與除錯工作階段中，準確地預測軟體可靠度的成長情形非常重要。在過去二十年已發展的軟體可靠度成長模型(Software Reliability Growth Model, SRGM)中，以非齊次蒲瓦松程序(Non-homogeneous Poisson Process ,NHPP)為基礎的模型最為受到重視。過去的相關研究，由於未考量軟體除錯過程的學習效果，通常僅能單獨地解釋S型或指數型的軟體失效資料型態，而無法對不同軟體除錯情況提出統一與合理的解釋，這將會限制模式的應用範圍。基於此，本研究提出以學習效果觀點的軟體可靠度成長模型，此模式可以同時適合S型與指數型軟體失效資料，並對不同軟體除錯情況提出統一與合理的解釋，同時說明了軟體除錯過程中，除錯人員的學習效果之優劣，提供管理者規劃適當的人員配置方案。同時，本研究進一步運用貝氏機率方法，在缺乏歷史資料，模式之未知參數無法使用統計推估方法估計時，以專家估計方式推估相關參數的事前機率統計特徵，並以事後機率修正之，使得本文模型可適用於軟體除錯歷史資料充足與不充足的各種多元情境，以提供管理者決定最佳軟體上線(發行)時機之參考。

For the last two decades, reliability growth has been studied to predict software reliability in the testing/debugging phase. Most of the models developed were based on the Non-Homogeneous Poisson Process (NHPP), and S-shaped type or exponential-shaped type of behavior is usually assumed. Unfortunately, such models may be suitable only for particular software failure data, thus narrowing the scope of applications. Therefore, from the perspective of learning effects that can influence the process of software reliability growth, this study considered that efficiency in testing/debugging concerned not only the ability of the testing staff, but also the learning effect that comes from inspecting the testing/debugging codes. The proposed model can reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and the results in the experiment show good fit. Moreover, a Bayesian approach was also employed in the study under the cases with insufficient historical data and with different testing environments. Besides, an optimal software release policy is suggested, and the numerical examples are given to verify the effectiveness of the proposed approach, and the sensitive analyses are performed in light of the numerical examples.

Abstract…………………………………………………………………………….	i
Table of Contents…………………………………………………………………..	iii
List of Figures……………………………………………………………………...	v
List of Tables……………………………………………………………………….	vii
Chapter 1 Introduction…………………………………………………………….	1
    1.1 Motivation……………………………………………………...…………	1
    1.2 Objectives…………………………………………………………………	3
    1.3 Overview………………….………………………………………………	3
Chapter 2 Literatures Review……………………………………………………..	5
    2.1 Software Reliability Growth……………………………………...………	5
       2.1.1 Non-homogeneous Poisson Process……………………………..…	5
       2.1.2 SRGMs with Change-point…………….…………….……………	7
    2.2 Learning Effect……………………………………….………...………	8
    2.3 SRGMs for Definite Condition…………………….……………………	10
       2.3.1 Confidence Interval Estimation of SRGMs…………………..……	10
       2.3.2 SRGMs with Stochastic Differential Equation……………………	11
    2.4 SRGMs for Indefinite Condition…………………….……………………	12
       2.4.1 SRGMs with Soft Computing……….……………………………	12
       2.4.2 SRGMs with Bayesian Approach…………………………………	13
Chapter 3 SRGM with Learning Effects………………………………………….	15
    3.1 Model Development………………………………………………………	17
    3.2 Parameters Estimation…………………………………………………….	24
3.3 Data Analysis and Model Comparisons……………..………..…..………	27
       3.3.1 The Criteria of Model Validation and Comparison…………………	27
       3.3.2 Model Validation and Comparisons with Real Data Sets…………..	31
    3.4 The Optimal Software Release Policy……………………………………	34
Chapter 4 Bayesian Updating for SRGM with Learning Effects…………………	42
    4.1 Promoted Model Development…………...………………………………	42
       4.1.1 Bayesian Decision Process……………………...……………..……	43
       4.1.2 Joint Prior Distribution and Joint Posterior Distribution……………	46
    4.2 The Promoted Optimal Software Release Policy…………….…………	50
Chapter 5 Numerical Examples and Discussion…………………………..………	54
    5.1 An Application under Sufficient Historical Failure Data…………………	54
    5.2 An Application under Insufficient Historical Failure Data………………..	58
Chapter 6 Conclusion…………………………………...…………………………	70
    6.1 Contribution……………………………………………………………….	70
    6.2 Limitations………………………………………………………………...	71
    6.3 Future Research…………………………………………………………...	72
Reference……………………………………………………………………….…	73
Appendix…………………………………………………………………..………	79
                                    

Reference
Achcar, J.A., Dey, D.K., and Niverthi, M. (1997) “A Bayesian approach using nonhomogeneous Poisson Process for software reliability models”, In Frontiers in Reliability, Basu et al. (Eds).
Adam Smiarowski , Jr. , Hoda S. Abdel-Aty-Zohdy , Mostafa Hashem Sherif , Hemal Shah (2006) “Wavelet Based RDNN for Software Reliability Estimation”, 11th IEEE Symposium on Computers and Communications (ISCC'06), iscc,pp.312-317.
Bai, C.G. (2005) “Bayesian network based software reliability prediction with an operational profile”, The Journal of Systems and Software, 77: 103-112.
Bai, C.G., Hu, Q.P., Xie, M., and Ng, S.H. (2005) “Software failure prediction based on a Markov Bayesian network model”, The Journal of Systems and Software, 74: 275-282.
Bunea, C., Charitosb, T., Cooke, R. M., and Beckerd, G. (2005) “Two-stage Bayesian models—application to ZEDB project”, Reliability Engineering and System Safety, 90: 123-130.
Chin-Yu Huang, Sy-Yen Kuo, Michael R. Lyu, (2000) "Effort-Index-Based Software Reliability Growth Models and Performance Assessment," Computer Software and Applications Conference, Annual International, The Twenty-Fourth Annual International Computer Software and Applications Conference, 0:454.
Cid, J.E.R. and Achcar, J.A. (1999) “Bayesian inference for nonhomogeneous Poisson processes in software reliability models assuming nonmonotonic intensity functions”, Computational Statistics & Data Analysis, 32: 147-159.
Dietmar Pfahl (2001) “An Integrated Approach to Simulation-Based Learning in Support of Strategic and Project Management in Software Organisations” , PhD Theses in Experimental Software Engineering, Fraunhofer-Institut fr Experimentelles Software Engineering, 8:27-40.
Goel, A.L. and Okumoto, K. (1979) “Time-dependent fault detection rate model for software and other performance measures”, IEEE Transactions on Reliability, 28: 206-211.
Gokhale, S. S. and Trivedi, K. S. (1999) “A time/structure based software reliability model”, Annals of Software Engineering, 8: 85-121.
Hossain, S.A. and Dahiya, R.C. (1993) “Estimating the Parameters of a Non-homogeneous Poisson-Process Model for Software Reliability”, IEEE Transactions on Reliability, 42: 604-612.
Hu, Q.P., Xie, M., Ng, S.H. and Levitin, G. (2007) “Robust recurrent neural network modeling for software fault detection and correction prediction”, Reliability Engineering & System Safety, 92: 332-340.
Huang, C.-Y. (2005) “Performance analysis of software reliability growth models with testing-effort and change-point”, Journal of Systems and Software, 76: 181-194.
Jeske, D.R. and Zhang, X. (2005) “Some successful approaches to software reliability modeling in industry”, The Journal of Systems and Software, 74: 85-99.
Kapur, P.K. and Bhalla, V.K. (1992) “Optimal release policies for a flexible software reliability growth model”, Reliability Engineering & System Safety, 35: 49-54.
Karatsas, I., Shreve, S. (1997) Brownian Motion and Stochastic Calculus, 2nd ed. Springer-Verlag: New York.
Katrina Maxwell, Luk Van Wassenhove, and Soumitra Dutta (1999) “Performance Evaluation of General and Company Specific Models in Software Development Effort Estimation”, Management Science , 45: 787-803.
Kimura, M., Toyota, T. and Yamada, S. (1999) “Economic analysis of software release problems with warranty cost and reliability requirement”, Reliability Engineering & System Safety, 66: 49-55.
Kuo, L., Lee, J.C., Choi, K., and Yang, T.Y. (1997) “Bayes inference for S-shaped software reliability growth models”, IEEE Transactions on Reliability, 46: 76-80.
Kuo, L. and Yang, T.Y. (1996) “Bayesian computation for nonhomogeneous Poisson processes in software reliability”, Journal of the American Statistical Association, 91: 763-773.
Lee, C.H., Kim, Y.T., Park, D.H. (2004) “S-shaped software reliability growth models derived from stochastic differential equations”, IIE Transactions, 36: 1193-1199.
Littlewood, B. (2006) “Comments on ‘Evolutionary neural network modeling for software cumulative failure time prediction”, Reliability Engineering & System Safety, 91: 485-486.
Melo, A.C.V. and Sanchez, A.J. (2008) “Software maintenance project delays prediction using Bayesian networks”, Expert Systems with Applications, 34: 908-919.
Moran, P.A.P. (1969) “Statistical inference with bivariate gamma distribution”, Biometrika, 56: 627-634.
Nalina Suresh, A.N.V. Rao, A.J.G. Babu (1996) “A software reliability growth model”, International Journal of Quality & Reliability Management, 13:84-94.
Ohba, M. (1984a) “Inflexion S-shaped software reliability growth models”, in Stochastic Models in Reliability Theory, Osaki, S. and Hatoyama, Y., Eds. Berlin, Germany: Springer-Verlag, 144-162.
Ohba, M. (1984b) “Software reliability analysis models”, IBM Journal of Research and Development, 28: 428-443.
zekici, S. and Soyer, R. (2003) “Reliability of software with an operational profile”, European Journal of Operational Research, 149: 459-474.
P.K. Kapur, D.N. Goswami, and A. Bardhan (2007) “A General Software Reliability Growth Model with Testing Effort Dependent Learning Process”, International Journal of Modeling and Simulation, 205:4401
Pham, H. and Zhang, X. (1999) “A software cost model with warranty and risk costs”, IEEE Transactions on Computers, 48: 71-75.
Pham, H. and Zhang, X. (2003) “NHPP software reliability and cost models with testing coverage”, European Journal of Operational Research, 145: 445-454.
Pham, H. (2003) “Software reliability and cost models - Perspectives, comparison, and practice”, European Journal of Operational Research, 149: 475-489.
Shyur, H.-J. (2003) “A stochastic software reliability model with imperfect debugging and change-point”, The Journal of Systems and Software, 66: 135-141.
T. P. Wright. (1936) “Factors Affecting the Cost of Airplanes”, Journal of the Aeronautical Sciences, February:3
Tamura, Y., Yamada, S. (2006) “A flexible stochastic differential equation model in distributed development environment”, European Journal of Operational Research, 168: 143-152.
Tian, L. and Noore, A. (2005) “Evolutionary neural network modeling for software cumulative failure time prediction”, Reliability Engineering & System Safety, 87: 45-51.
William J. Stevenson. (1999) “Production Operations Management”, Irwin/McGraw-Hill, 349-358.
Yamada, M. Kimura, H. Tanaka and S. Osaki. (1994) “Software reliability measurement and assessment with stochastic differential equations”, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E77-A: 109-116.
Yamada, S., Ohba, M., and Osaki, S. (1983) “S-Shaped software reliability modeling for software error detection”, IEEE Transactions on Reliability, 32: 475-484.
Yamada, S., Osaki. S. (1985) “Software reliability growth modeling: Models and applications”, IEEE Transactions on Software Engineering, 11: 1431-1437.
Yamada, S., Tokuno, K. and Osaki, S. (1992) “Imperfect debugging models with fault introduction rate for software reliability assessment”, International Journal of Systems Science, 23: 2241-2252.
Yin, L., Trivedi, K.S. (1999) “Confidence Interval Estimation of NHPP-Based Software Reliability Models”, Proceedings of the 10th International Symposium on Software Reliability Engineering, November: 6-11.
Zhang, X. and Pham, H. (1998) “A software cost model with warranty cost, error removal times and risk costs”, IIE Transactions, 30: 1135-1142.
Zhang, X. and Pham, H. (2006) “Software field failure rate prediction before software deployment”, The Journal of Systems and Software, 79: 291-300.
Zhao, M. (1993) “Change-point problems in software and hardware reliability”, Communications in Statistics Theory and Methods, 22: 757-768.

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校外：2008-10-21公開

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