Analysis of the Debugging Model Based on Probabilistic State Transition

In general, software reliability test is an important task in software developing process. Meanwhile, it called perfect debugging which assumed that detecting a fault and fixing it,  no introducing new faults, in turn, introducing new faults, called imperfect debugging. We all hope the testing process is perfect debugging and no new faults will be introduced. Therefore, it can shorten the testing time and decrease the testing costs. However, in practice, the software test is a complex, intractable process and can be divided into two categories, which are perfect debugging and imperfect debugging. Furthermore, most of testing processes are imperfect debugging. Then, what is relation between perfect debugging and imperfect debugging and what is impact to imperfect debugging transiting to perfect debugging? In this paper, we propose two state models based on probabilistic state transition to analyze the relation between perfect debugging and imperfect debugging and express debugging time and costs during imperfect debugging transiting to perfect debugging process in terms of the parameters in that model. Threshold conditions for perfect debugging process to be beneficial are also derived. Finally, using the numerical examples prove our assumptions and inference.

[1]  Norman F. Schneidewind,et al.  Modelling the fault correction process , 2001, Proceedings 12th International Symposium on Software Reliability Engineering.

[2]  Q. P. Hu,et al.  A study of the modeling and analysis of software fault‐detection and fault‐correction processes , 2007, Qual. Reliab. Eng. Int..

[3]  Yan Gao,et al.  Comparison of GARCH Models based on Different Distributions , 2012, J. Comput..

[4]  Chin-Yu Huang,et al.  An integration of fault detection and correction processes in software reliability analysis , 2006, J. Syst. Softw..

[5]  Dong Wang,et al.  Using Markov Process for Passenger Structure Prediction within Comprehensive Transportation Channel , 2013, J. Comput..

[6]  Ming Zhao,et al.  The Schneidewind software reliability model revisited , 1992, [1992] Proceedings Third International Symposium on Software Reliability Engineering.

[7]  Zili Zhang,et al.  Prediction Algorithm for State Prediction Model , 2012, J. Comput..

[8]  P. K. Kapur,et al.  A software reliability growth model for an error-removal phenomenon , 1992, Softw. Eng. J..

[9]  Carol S. Smidts,et al.  A stochastic model of fault introduction and removal during software development , 2001, IEEE Trans. Reliab..

[10]  Xiaolin Teng,et al.  Considering fault removal efficiency in software reliability assessment , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[11]  Shigeru Yamada,et al.  S-Shaped Reliability Growth Modeling for Software Error Detection , 1983, IEEE Transactions on Reliability.

[12]  Michael R. Lyu,et al.  Estimation and Analysis of Some Generalized Multiple Change-Point Software Reliability Models , 2011, IEEE Transactions on Reliability.

[13]  P. K. Kapur,et al.  A Unified Approach for Developing Software Reliability Growth Models in the Presence of Imperfect Debugging and Error Generation , 2011, IEEE Transactions on Reliability.

[14]  Hui Zeng Efficient Graduate Employment Serving System based on Queuing Theory , 2012, J. Comput..

[15]  P. K. Kapur,et al.  Optimal sofware release policies for software reliability growth models under imperfect debugging , 1990 .

[16]  Norman F. Schneidewind,et al.  Analysis of error processes in computer software , 1975, Reliable Software.

[17]  Mitsuru Ohba,et al.  Does imperfect debugging affect software reliability growth? , 1989, ICSE '89.

[18]  Hoang Pham,et al.  A software cost model with imperfect debugging, random life cycle and penalty cost , 1996, Int. J. Syst. Sci..

[19]  Amrit L. Goel,et al.  Software Reliability Models: Assumptions, Limitations, and Applicability , 1985, IEEE Transactions on Software Engineering.

[20]  K Okumoto,et al.  TIME-DEPENDENT ERROR-DETECTION RATE MODEL FOR SOFTWARE AND OTHER PERFORMANCE MEASURES , 1979 .

[21]  Hoang Pham,et al.  A general imperfect-software-debugging model with S-shaped fault-detection rate , 1999 .

[22]  Yennun Huang,et al.  Software rejuvenation: analysis, module and applications , 1995, Twenty-Fifth International Symposium on Fault-Tolerant Computing. Digest of Papers.

[23]  Swapna S. Gokhale,et al.  Analysis of Software Fault Removal Policies Using a Non-Homogeneous Continuous Time Markov Chain , 2004, Software Quality Journal.