NDLI: A Comparative Evaluation of Using Genetic Programming for Predicting Fault Count Data

Content Provider	IEEE Xplore Digital Library
Author	Afzal, W. Torkar, R.
Copyright Year	2008
Description	Author affiliation: Blekinge Inst. of Technol., Ronneby (Afzal, W.; Torkar, R.)
Abstract	There have 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 data.
Starting Page	407
Ending Page	414
File Size	355807
Page Count	8
File Format	PDF
ISBN	9781424432189
DOI	10.1109/ICSEA.2008.9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-26
Publisher Place	Malta
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic programming Software reliability Predictive models Artificial neural networks Software quality Mathematical model Testing Software engineering Computer industry Software performance prediction software reliability growth modeling
Content Type	Text
Resource Type	Article

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