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Selection of a Predictive Coverage Growth Function
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 Title & Authors
Selection of a Predictive Coverage Growth Function
Park, Joong-Yang; Lee, Gye-Min;
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 Abstract
A trend in software reliability engineering is to take into account the coverage growth behavior during testing. A coverage growth function that represents the coverage growth behavior is an essential factor in software reliability models. When multiple competitive coverage growth functions are available, there is a need for a criterion to select the best coverage growth functions. This paper proposes a selection criterion based on the prediction error. The conditional coverage growth function is introduced for predicting future coverage growth. Then the sum of the squares of the prediction error is defined and used for selecting the best coverage growth function.
 Keywords
Construct;coverage;coverage growth function;prediction error;software testing;software reliability;
 Language
English
 Cited by
1.
Virtual Coverage: A New Approach to Coverage-Based Software Reliability Engineering,;;

Communications for Statistical Applications and Methods, 2013. vol.20. 6, pp.467-474 crossref(new window)
1.
Virtual Coverage: A New Approach to Coverage-Based Software Reliability Engineering, Communications for Statistical Applications and Methods, 2013, 20, 6, 467  crossref(new windwow)
 References
1.
Crespo, A. N., Pasquini, A., Jino, M. and Maldonado, J. C. (2008). A binomial software reliability model based on coverage of structural testing criteria, Empirical Software Engineering, 13, 185–209.

2.
Crespo, A. N., Pasquini, A., Jino, M. and Maldonado, J. C. (2009). Applying code coverage approach to an infinite failure software reliability model, In Proceedings of 23rd Brazilian Symposium on Software Reliability Engineering, 216–226.

3.
Gokhale, S. S., Philip, T., Marinos, P. N. and Trivedi, K. S. (1996). Unification of finite failure non-homogeneous Poisson process models through test coverage, In Proceedings of 7th IEEE International Symposium on Software Reliability Engineering, 299–307.

4.
Lyu, M. R. (1996). Handbook of Software Reliability Engineering, McGraw-Hill, New York.

5.
Malaiya, Y. K., Li, M. N., Bieman, J. M. and Karcich, R. (2002). Software reliability growth and test coverage, IEEE Transactions on Reliability, 51, 420–426.

6.
Musa, J. D. (1999). Software Reliability Engineering: More Reliable Faster Development and Testing, McGraw-Hill, New York.

7.
Musa, J. D., Iannino, A. and Okumoto, K. (1987). Software Reliability: Measurement, Prediction, Application, McGraw-Hill, New York.

8.
Park, J. Y. and Fujiwara, T. (2006). Coverage growth functions for software reliability modeling, Proceedings of 2nd Asian International Workshop on Advanced Reliability Modeling, 435–442.

9.
Park, J. Y., Lee, G. and Park, J. H. (2007). A class of discrete time coverage growth functions for software reliability engineering, Communications of the Korean Statistical Society, 14, 497–506.

10.
Park, J. Y., Lee, G. and Park, J. H. (2008a). A class of coverage growth functions and its practical application, Journal of the Korean Statistical Society, 37, 241–247. crossref(new window)

11.
Park, J. Y., Lee, G. and Park, J. H. (2008b). A general coverage-based NHPP SRGM framework, Communications of the Korean Statistical Society, 15, 875–881. crossref(new window)

12.
Pham, H. and Zhang, X. (2003). NHPP software reliability and cost models with testing coverage, European Journal of Operational Research, 145, 443–454.