Proportional Intensity-Based Software Reliability Modeling with Time-Dependent Metrics

The black-box approach based on stochastic software reliability models is a simple methodology with only software fault data in order to describe the temporal behavior of fault-detection processes, but fails to incorporate some significant development metrics data observed in the development process. In this paper we develop proportional intensity-based software reliability models with time-dependent metrics, and propose a statistical framework to assess the software reliability with the time-dependent covariate as well as the software fault data. The resulting models are similar to the usual proportional hazard model, but possess somewhat different covariate structure from the existing one. We compare these metrics-based software reliability models with some typical non-homogeneous Poisson process models, which are the special cases of our models, and evaluate quantitatively the goodness-of-fit from the viewpoint of information criteria. As an important result, the accuracy on reliability assessment strongly depends on the kind of software metrics used for analysis and can be improved by incorporating the time-dependent metrics data in modeling

[1]  Taghi M. Khoshgoftaar,et al.  Predictive Modeling Techniques of Software Quality from Software Measures , 1992, IEEE Trans. Software Eng..

[2]  James J. Reed.  Data and Analysis Center for Software , 1993 .

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

[4]  Sousuke Amasaki,et al.  A New Challenge for Applying Time Series Metrics Data to Software Quality Estimation , 2005, Software Quality Journal.

[5]  Maurice H. Halstead,et al.  Elements of software science , 1977 .

[6]  Harold Ascher,et al.  The Use of Regression Techniques for Matching Reliability Models to the Real World , 1986 .

[7]  Anas N. Al-Rabadi,et al.  A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .

[8]  Mary Shaw,et al.  Empirical evaluation of defect projection models for widely-deployed production software systems , 2004, SIGSOFT '04/FSE-12.

[9]  William M. Evanco,et al.  A model-based framework for the integration of software metrics , 1994, J. Syst. Softw..

[10]  Lawrence H. Putnam,et al.  A General Empirical Solution to the Macro Software Sizing and Estimating Problem , 1978, IEEE Transactions on Software Engineering.

[11]  Amrit L. Goel,et al.  Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures , 1979, IEEE Transactions on Reliability.

[12]  Norman F. Schneidewind.  Software metrics model for integrating quality control and prediction , 1997, Proceedings The Eighth International Symposium on Software Reliability Engineering.

[13]  John D. Musa,et al.  Software reliability - measurement, prediction, application , 1987, McGraw-Hill series in software engineering and technology.

[14]  Chin-Yu Huang,et al.  Analysis of incorporating logistic testing-effort function into software reliability modeling , 2002, IEEE Trans. Reliab..

[15]  Taghi M. Khoshgoftaar,et al.  Predicting software errors, during development, using nonlinear regression models: a comparative study , 1992 .

[16]  John D. Musa,et al.  Software reliability measurement , 1984, J. Syst. Softw..

[17]  Michael R. Lyu,et al.  Handbook of software reliability engineering , 1996 .

[18]  Taghi M. Khoshgoftaar,et al.  Comparing software fault predictions of pure and zero-inflated Poisson regression models , 2005, Int. J. Syst. Sci..

[19]  Michael R. Lyu,et al.  What is software reliability? , 1994, Proceedings of COMPASS'94 - 1994 IEEE 9th Annual Conference on Computer Assurance.

[20]  Shunji Osaki,et al.  Software-reliability growth with a Weibull test-effort: a model and application , 1993 .

[21]  Tadashi Dohi,et al.  Determination of the optimal software release time based on proportional hazards software reliability growth models , 2003 .

[22]  Abhijit S. Pandya,et al.  Application of neural networks for predicting program faults , 1995, Ann. Softw. Eng..

[23]  D W Wightman,et al.  Proportional hazards modelling of software failure data , 1986 .

[24]  Norman F. Schneidewind,et al.  Measuring and evaluating maintenance process using reliability, risk, and test metrics , 1997, 1997 Proceedings International Conference on Software Maintenance.

[25]  Muneo Takahashi,et al.  An empirical study of a model for program error prediction , 1985, ICSE '85.

[26]  Shigeru Yamada,et al.  Software Reliability Growth Models with Testing-Effort , 1986, IEEE Transactions on Reliability.

[27]  P. Sen,et al.  Time-dependent coefficients in a Cox-type regression model , 1991 .

[28]  Taghi M. Khoshgoftaar,et al.  Predicting Software Development Errors Using Software Complexity Metrics , 1990, IEEE J. Sel. Areas Commun..

[29]  D.,et al.  Regression Models and Life-Tables , 2022 .

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

[31]  Michael R. Lyu,et al.  Framework for modeling software reliability, using various testing-efforts and fault-detection rates , 2001, IEEE Trans. Reliab..

[32]  W. M. Evanco,et al.  Using a proportional hazards model to analyze software reliability , 1999, STEP '99. Proceedings Ninth International Workshop Software Technology and Engineering Practice.

[33]  J. Lawless.  Regression Methods for Poisson Process Data , 1987 .

[34]  Suku Nair,et al.  A Model for Software Development Effort and Cost Estimation , 1997, IEEE Trans. Software Eng..

[35]  Tony Bendell,et al.  The Use of Exploratory Data Analysis Techniques for Software Reliability Assessment and Prediction , 1986 .

[36]  L. J. Wei,et al.  On the Cox Model With Time-Varying Regression Coefficients , 2005 .