Modeling of software fault detection and correction processes with fault dependency

Peng, R.; Zhai, Q.

doi:10.17531/ein.2017.3.18

Artykuł - szczegóły

Tytuł artykułu

Modeling of software fault detection and correction processes with fault dependency

Autorzy

Peng R. , Zhai Q.

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.17531/ein.2017.3.18

Warianty tytułu

Modelowanie procesów wykrywania i korekcji błędów oprogramowania z założeniem wzajemnej zależności błędów

Języki publikacji

Abstrakty

Software reliability modeling has undergone a continuous evolution over the past three decades to adapt to various and everchanging software testing environments. In existing models, immediate fault removal and fault independency are two basic and commonly used assumptions. Recently, models combining fault detection process (FDP) and fault correction process (FCP) were proposed to alleviate the immediate fault removal assumption. In this paper, we extend such a methodology by proposing a modeling framework for the FDP and FCP incorporating fault dependency. Faults are classified as leading faults and dependent faults and the FCPs for both types of faults are explicitly modeled. Several paired models considering different assumptions for debugging lags are proposed for the combined FDP and FCP. The applicability of the proposed models are illustrated using real testing data. In addition, the optimal software release policy under this framework is studied.

Modelowanie niezawodności oprogramowania w ciągu ostatnich trzech dekad ulegało ciągłej ewolucji, pozwalającej dostosować je do różnych, stale zmieniających się środowisk testowych. W przypadku istniejących modeli, dwoma podstawowymi i powszechnie stosowanymi założeniami jest natychmiastowe usunięcie błędu oraz brak zależności między błędami. Ostatnio, badacze zaproponowali modele, które łagodzą pierwsze z tych założeń, łącząc proces wykrywania błędów (FDP) z procesem ich korekcji (FCP). W niniejszym artykule, rozszerzono tę metodologię, proponując paradygmat modelowania dla zintegrowanych procesów FDP i FCP uwzględniający zależności między błędami. W paradygmacie tym, błędy klasyfikuje się jako błędy nadrzędne i błędy zależne, a procesy FCP dla obu typów błędów są modelowane oddzielnie. Zaproponowano kilka połączonych w pary modeli rozważających różne założenia dotyczące opóźnień debugowania w procesach łączących detekcję i korekcję błędów. Możliwość zastosowania proponowanych modeli przedstawiono na przykładzie rzeczywistych danych testowych. Dodatkowo badano optymalną politykę aktualizacji oprogramowania, jaką można prowadzić w ramach proponowanego paradygmatu.

Słowa kluczowe

fault dependency non-homogeneous Poisson process (NHPP) software reliability growth model (SRGM) software fault detection and correction processes

zależność błędów niejednorodny proces Poissona model wzrostu niezawodności oprogramowania procesy detekcji i korekcji błędów oprogramowania

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2017

Tom

Vol. 19, no. 3

Strony

467--475

Opis fizyczny

Bibliogr. 50 poz., rys., tab.

Twórcy

autor

Peng R.

pengrui1988@ustb.edu.cn

Donlinks School of Economics & Management University of Science & Technology Beijing, China

autor

Zhai Q.

isezq@nus.edu.sg

Department of Industrial and Systems Engineering National University of Singapore, Singapore

Bibliografia

1. Boland P J and Chuív N N. Optimal times for software release when repair is imperfect. Statistics & Probability Letters 2007; 77(12): 11761184, https://doi.org/10.1016/j.spl.2007.03.004.
2. Chang Y-C and Liu C-T. A generalized JM model with applications to imperfect debugging in software reliability. Applied Mathematical Modelling 2009; 33(9): 3578-3588, https://doi.org/10.1016/j.apm.2008.11.018.
3. Febrero F, Calero C, and Ángeles Moraga M. Software reliability modeling based on ISO/IEC SQuaRE. Information and Software Technology 2016; 70: 18-29, https://doi.org/10.1016/j.infsof.2015.09.006.
4. Goel A L and Okumoto K. Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Transactions on Reliability 1979; 28(3): 206-211, https://doi.org/10.1109/TR.1979.5220566.
5. Gokhale S S, Lyu M R, and Trivedi K S. Analysis of software fault removal policies using a non-homogeneous continuous time Markov chain. Software Quality Journal 2004;12(3): 211-230, https://doi.org/10.1023/B:SQJO.0000034709.63615.8b.
6. Goseva-Popstojanova K and Trivedi K S. Failure correlation in software reliability models. IEEE Transactions on Reliability 2000; 49(1): 37-48, https://doi.org/10.1109/24.855535.
7. Gutjahr W J. A reliability model for nonhomogeneous redundant software versions with correlated failures. Computer Systems Science and Engineering 2001;16(6): 361-370.
8. Hu Q, Xie M, Ng S H, and Levitin G. Robust recurrent neural network modeling for software fault detection and correction prediction. Reliability Engineering & System Safety 2007;92(3): 332-340. https://doi.org/10.1016/j.ress.2006.04.007.
9. Huang C. Y and Huang W. C, Software reliability analysis and measurement using finite and infinite server queueing models. IEEE Transactions on Reliability 2008; 57(1): 192-203, https://doi.org/10.1109/TR.2007.909777.
10. Huang C-Y, Kuo S-Y, and Lyu M R. An assessment of testing-effort dependent software reliability growth models. IEEE Transactions on Reliability 2007; 56(2): 198-211, https://doi.org/10.1109/TR.2007.895301.
11. Huang C-Y and Lin C-T. Software reliability analysis by considering fault dependency and debugging time lag. IEEE Transactions on Reliability 2006;55(3): 436-450. https://doi.org/10.1109/TR.2006.879607.
12. Huang C-Y and Lyu M R, Optimal release time for software systems considering cost, testing-effort, and test efficiency. IEEE Transactions on Reliability 2005; 54(4): 583-591, https://doi.org/10.1109/TR.2005.859230.
13. Inoue S and Yamada S. Generalized discrete software reliability modeling with effect of program size. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 2007; 37(2): 170-179, https://doi.org/10.1109/TSMCA.2006.889475.
14. Jain M and Priya K. Software reliability issues under operational and testing constraints. Asia-Pacific Journal of Operational Research 2005; 22(01): 33-49, https://doi.org/10.1142/S021759590500042X.
15. Jha P, Gupta D, Yang B, and Kapur P. Optimal testing resource allocation during module testing considering cost, testing effort and reliability. Computers & Industrial Engineering 2009; 57(3): 1122-1130, https://doi.org/10.1016/j.cie.2009.05.001.
16. Jia L, Yang B, Guo S, and Park D H. Software reliability modeling considering fault correction process. IEICE Transactions on Information and Systems 2010; 93(1): 185-188, https://doi.org/10.1587/transinf.E93.D.185.
17. Kapur P, Goswami D, Bardhan A, and Singh O. Flexible software reliability growth model with testing effort dependent learning process. Applied Mathematical Modelling 2008; 32(7): 1298-1307, https://doi.org/10.1016/j.apm.2007.04.002.
18. Kapur P and Younes S. Software reliability growth model with error dependency. Microelectronics Reliability 1995; 35(2): 273-278, https:// doi.org/10.1016/0026-2714(94)00054-R.
19. Kim H S, Park D H, and Yamada S. Bayesian optimal release time based on inflection S-shaped software reliability growth model. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2009; 92(6): 1485-1493, https://doi.org/10.1587/ transfun.E92.A.1485.
20. Lee C H, Kim Y T, and Park D H. S-shaped software reliability growth models derived from stochastic differential equations. IIE transactions 2004; 36(12): 1193-1199, https://doi.org/10.1080/07408170490507792.
21. Li X, Xie M, and Ng S H. Sensitivity analysis of release time of software reliability models incorporating testing effort with multiple changepoints. Applied Mathematical Modelling 2010; 34(11): 3560-3570, https://doi.org/10.1016/j.apm.2010.03.006.
22. Lin C-T and Huang C-Y., Enhancing and measuring the predictive capabilities of testing-effort dependent software reliability models. Journal of Systems and Software 2008; 81(6): 1025-1038, https://doi.org/10.1016/j.jss.2007.10.002.
23. Lin C-T and Huang C-Y. Staffing level and cost analyses for software debugging activities through rate-based simulation approaches. IEEE Transactions on Reliability 2009; 58(4): 711-724, https://doi.org/10.1109/TR.2009.2019669.
24. Lin C-T and Li Y-F. Rate-based queueing simulation model of open source software debugging activities. IEEE Transactions on Software Engineering 2014; 40(11): 1075-1099, https://doi.org/10.1109/TSE.2014.2354032.
25. Lo J-H and Huang C-Y. An integration of fault detection and correction processes in software reliability analysis. Journal of Systems and Software 2006; 79(9): 1312-1323, https://doi.org/10.1016/j.jss.2005.12.006.
26. Lyu M R. Handbook of Software Reliability Engineering. New York: McGraw-Hill, Inc., 1996.
27. Musa J D, Iannino A, and Okumono K. Software Reliability, Measurement, Prediction and Application. New York: McGraw-Hill, Inc, 1987.
28. Okamura H and Dohi T. Unification of software reliability models using Markovian arrival process, in Proceedings of 17th Pacific Rim International Symposium on Dependable Computing (PRDC) 2011: 20-27, https://doi.org/10.1109/prdc.2011.12.
29. Okamura H, Dohi T, and Osaki S. Software reliability growth models with normal failure time distributions. Reliability Engineering & System Safety 2013; 116: 135-141, https://doi.org/10.1016/j.ress.2012.02.002.
30. Peng R, Li Y-F, Zhang J-G, and Li X. A risk-reduction approach for optimal software release time determination with the delay incurred cost. International Journal of Systems Science 2015; 46(9): 1628-1637, https://doi.org/10.1080/00207721.2013.827261.
31. Peng R, Li Y-F, Zhang W, and Hu Q. Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction. Reliability Engineering & System Safety 2014;126: 37-43, https://doi.org/10.1016/j.ress.2014.01.004.
32. Pietrantuono R, Russo S, and Trivedi K S. Software reliability and testing time allocation: An architecture-based approach. IEEE Transactions on Software Engineering 2010; 36(3): 323-337, https://doi.org/10.1109/TSE.2010.6.
33. Rana R, Staron M, Berger C, Hansson J, Nilsson M, Törner F, et al., Selecting software reliability growth models and improving their predictive accuracy using historical projects data. Journal of Systems and Software 2014; 98: 59-78, https://doi.org/10.1016/j.jss.2014.08.033.
34. Schneidewind N F. Analysis of error processes in computer software, in Proceedings of 1975 International Conference on Reliable Software 1975: 337-346, https://doi.org/10.1145/800027.808456.
35. Shatnawi O. Discrere time modelling in software reliability engineering A unified approach. Computer Systems Science and Engineering 2009;24(6): 391.
36. Shatnawi O. Measuring commercial software operational reliability: an interdisciplinary modelling approach. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014;16(4): 585-594.
37. Shibata K, Rinsaka K, Dohi T, and Okamura H. Quantifying software maintainability based on a fault-detection/correction model, In Proceedings of 13th Pacific Rim International Symposium on Dependable Computing (PRDC2007) 20 7: 35-42, https://doi.org/10.1109/ PRDC.2007.46.
38. Shinohara Y, Nishio Y, Dohi T, and Osaki S. An optimal software release problem under cost rate criterion: artificial neural network approach. Journal of Quality in Maintenance Engineering 1998; 4(4): 236-247, https://doi.org/10.1108/13552519810233967.
39. Tamura Y and Yamada S. A flexible stochastic differential equation model in distributed development environment. European Journal of Operational Research 2006; 168(1): 143-152, https://doi.org/10.1016/j.ejor.2004.04.034.
40. Ullah N, Morisio M, and Vetro A. A comparative analysis of software reliability growth models using defects data of closed and open source software, in Proceedings of 35th Annual IEEE Software Engineering Workshop 2012: 187-192, https://doi.org/10.1109/sew.2012.26.
41. Wang L, Hu Q, and Liu J. Software reliability growth modeling and analysis with dual fault detection and correction processes. IIE Transactions 2016; 48(4): 359-370, https://doi.org/10.1080/0740817X.2015.1096432.
42. Wu Y, Hu Q, Xie M, and Ng S H. Modeling and analysis of software fault detection and correction process by considering time dependency. IEEE Transactions on Reliability 2007; 56(4): 629-642, https://doi.org/10.1109/TR.2007.909760.
43. Xie M. Software reliability modelling. Singapore: World Scientific, 1991, https://doi.org/10.1142/1390.
44. Xie M, Hu Q, Wu Y, and Ng S H. A study of the modeling and analysis of software fault-detection and fault-correction processes. Quality and Reliability Engineering International 2007; 23(4): 459-470, https://doi.org/10.1002/qre.827.
45. Yamada S, Ohba M, and Osaki S. S-shaped software reliability growth models and their applications. IEEE Transactions on Reliability 1984; 33(4): 289-292, https://doi.org/10.1109/TR.1984.5221826.
46. Yang B, Guo S, Ning N, and Huang H-Z. Parameter estimation for software reliability models considering failure correlation, in Proceedings of Annual Reliability and Maintainability Symposium (RAMS 2008) 2008: 405-410, https://doi.org/10.1109/rams.2008.4925830.
47. Zhang X and Pham H. Comparisons of nonhomogeneous Poisson process software reliability models and its applications. International Journal of Systems Science 2000; 31(9): 1115-1123, https://doi.org/10.1080/002077200418397.
48. Zhang X and Pham H. Predicting operational software availability and its applications to telecommunication systems. International Journal of Systems Science 2002; 33(11): 923-930, https://doi.org/10.1080/0020772021000023022.
49. Zhang X, Teng X, and Pham H. Considering fault removal efficiency in software reliability assessment. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 2003; 33(1): 114-120, https://doi.org/10.1109/TSMCA.2003.812597.
50. Zhao J, Liu H-W, Cui G, and Yang X-Z. Software reliability growth model with change-point and environmental function. Journal of Systems and Software 2006; 79(11): 1578-1587, https://doi.org/10.1016/j.jss.2006.02.030.

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-764321ce-af45-420a-92c5-5e66d0926b64