Notice of Violation of IEEE Publication Principles: Evaluation of GP Model for Software Reliability

Notice of Violation of IEEE Publication Principles

"Evaluation of GP Model for Software Reliability,"
by S. Paramasivam, and M. Kumaran,
in the 2009 International Conference on Signal Processing Systems, May 2009

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"A Comparative Evaluation of Using Genetic Programming for Predicting Fault Count Data,"
by W. Afzal, R. Torkar,
in the Third International Conference on Software Engineering Advances, 2008. ICSEA '08, pp.407-414, October 2008


There has been a number of software reliability growth models (SRGMs) proposed in literature. Due to several reasons, such as violation of models’ assumptions and complexity of models, the practitioners face difficulties in knowing which models to apply in practice. This paper presents a comparative evaluation of traditional models and use of genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The motivation of using a GP approach is its ability to evolve a model based entirely on prior data without the need of making underlying assumptions. The results show the strengths of using GP for predicting fault count.