A More Effective and Faster Genetic Algorithm for Parameter Estimation of Software Reliability Growth Models

• Main Authors: Tsan-Yuan Chen, 陳贊元
• Other Authors: Chin-Yu Huang
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/43923145479242261243

碩士 === 國立清華大學 === 資訊工程學系 === 96 === In order to assure software quality and to assess software reliability, one of the current methods is to apply a Software Reliability Growth Model (SRGM). SRGMs can be used to describe software failures as a random process, which is characterized by either times of failures or by the number of failures at fixed times. The fault reduction factor is the ratio of the net fault reduction. That is the removed fault subtracting from the introduced fault per failure. Debugging may introduce a new fault which phenomenon is called imperfect debugging. And this phenomenon may influence the fault reduction factor. Not a lot of SRGMs use this factor. The parameters of SRGMs are unknown and have to be estimated based on collected real software failure data. Several estimation methods have been proposed, like Least Square Estimation (LSE) and Maximum Likelihood Estimation (MLE), but most of them have restrictions such as the existence of derivatives on evaluation functions. On the other hand, Genetic Algorithms (GA) provide us with robust optimization methods in many fields. In this thesis, we propose adding a modified Genetic Algorithm to the parameter estimation of SRGMs and to the SRGMs considering fault reduction factors. Besides, the parameters of SRGMs are usually estimated by LSE and MLE. But we use the modified Genetic Algorithm for parameter estimation. Experimental results show that the proposed Genetic Algorithm is faster and more effective than other traditional Genetic Algorithms and our proposed models can predict the Software reliability more accurately.