Accumulative Bi-Damped Oscillation Model for the Sequential Process of Software Defect Discovery
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    Abstract:

    In software testing practice, usually, the software under test (SUT) experiences multiple iterative processes of testing and modification. With the influence of many uncertain factors, such as defect distribution in the software under test, iterative developing and testing processes, and testers’ capabilities of defect detection, the software defect discovery process shows that the sequential characteristics correspond with test cycles, i.e. periodicity, random oscillation, and attenuation. Through a deep analysis of the basic characters and key influencing factors of the controlled software testing process, the paper proposes an Accumulative Bi-Damped Oscillation Model (ABDOM), which describes periodicity, random oscillation, and attenuation for a sequential software defect discovery process. It verifies ABDOM’s validity with defect data gathered from 2 true software test projects, and discusses the application scope of ABDOM and the possible applications in test predication and evaluation.

    Reference
    [1] Myers GJ. The Art of Software Testing. Wiley Interscience, 1979.
    [2] Gaffney JE. Estimating the number of faults in code. IEEE Trans. on Software Engineering, 1984,10(6):459?464. [doi: 10.1109/ TSE.1984.5010260]
    [3] Malaiya YK, Denton J. Estimating the number of residual defects. In: Proc. of the 3rd IEEE Int’l High-Assurance Systems Engineering Symp. (HASE’98). 1998.
    [4] Malaiya YK, Denton J, Li MN. Estimating the number of defects: A simple and intuitive approach. In: Proc. of the 9th Int’l Symp. on Software Reliability Engineering. 1998. 307?315.
    [5] Halstead MH. Elements of Software Science. New York: Elsevier, North-Holland, 1977.
    [6] Lyu MR. A phase-based approach to creating highly reliable software. In: Proc. of the 24th Annual Int’l Computer Software and Applications Conf. (COMPSAC). 2000. 276.
    [7] Carol S, Martin S. Software reliability modeling: An approach to early reliability prediction. IEEE Trans. on Reliability, 1998,47(3): 268?278. [doi: 10.1109/24.740500]
    [8] Briand LC, Emam KE, Freimut BG. A comprehensive evaluation of capture-recapture models for estimating software defect content. IEEE Trans. on Software Engineering, 2000,26(6):518?540. [doi: 10.1109/32.852741]
    [9] Zhu YC, Xu H. Empirical-Based software defect content estimation improvement. Journal of Beijing University of Aeronautics and Astronautics, 2003,29(10):947?950 (in Chinese with English abstract).
    [10] Cai KY. On the neural network approach in software reliability modeling. Journal of Systems and Software, 2001,58:47?62. [doi: 10.1016/S0164-1212(01)00027-9]
    [11] Zhang JH, Sun F, Xie RS, Hao YL. Neural network based prediction of software faults in integrated navigation system. Journal of Harbin Engineering University, 2001,22(1):55?58 (in Chinese with English abstract).
    [12] Bai CG. Estimation of the Number of Remaining software errors based on Bayesian network. Computer Engineering, 2003,29(18): 39?40 (in Chinese with English abstract).
    [13] Bai CG, Yu MH, Hu SX. Software failure predication model based on multiple Markov Bayesian network. Computer Engineering and Applications, 2003,39(10):40?42 (in Chinese with English abstract).
    [14] Bergander T, Luo Y, Hamza AB. Software defects prediction using operating characteristic curves. 1-4244-1500-4/07/, IEEE, 2007.
    [15] Liu C, Jin MZ. Software Test Process Model-POCERM. Journal of Beijing University of Aeronautics and Astronautics, 1997,1:56?58 (in Chinese with English abstract).
    [16] Fenton N, NeiI M. A critique of software defect prediction models. IEEE Trans. on Software Engineering, 1999,25(5):675?689. [doi: 10.1109/32.815326]
    附中文参考文献: [9] 朱永春,徐红.一种基于历史数据的软件缺陷预测方法改进.北京航空航天大学学报,2003,29(10):947?950.
    [11] 张家海,孙枫,谢荣生,郝燕玲.估测组合导航系统软件缺陷的一种神经网络方法.哈尔滨工程大学学报,2001,22(1):55?58.
    [12] 白成刚.基于Bayes网的软件残留错误数度量.计算机工程,2003,29(18):39?40.
    [13] 白成刚.基于多重马尔可夫Bayes网的软件失效预测模型.计算机工程与应用,2003,39(10):40?42.
    [15] 刘超,金茂忠.软件测试过程的基本模型POCERM.北京航空航天大学学报,1997,1:56?58.
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何智涛,晏海华,刘超.软件缺陷发现时序过程的叠加双阻尼振荡模型.软件学报,2010,21(12):2999-3010

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History
  • Received:February 11,2009
  • Revised:July 09,2009
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