2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS) 2010
DOI: 10.1109/rams.2010.5448017
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Software reliability accelerated testing method based on mixed testing

Abstract: This study is conducted by solving these four key questions: the key points of the whole testing process; the data and information to be collected during the testing process; the model to estimate the software reliability; the verification of the testing method and the estimation model.Software reliability accelerated testing (SRAT) based on mixed testing is proposed. SRAT increases the effectiveness and efficiency of the reliability testing, and it is able to test some operations or program modules with low o… Show more

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Cited by 11 publications
(5 citation statements)
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“…There are several other aspects in other perspectives that can be used as indicators of a good and optimal test implementation. As stated in the earlier section that the essence of testing is finding software defects and evaluating their quality (Pressman, 2010) (Sommerville, 2011) (Gehring et al, 2017) (Wu, 2010. In terms of quality, it is certainly not easy to justify the quality of an application product or not.…”
Section: Resultsmentioning
confidence: 99%
“…There are several other aspects in other perspectives that can be used as indicators of a good and optimal test implementation. As stated in the earlier section that the essence of testing is finding software defects and evaluating their quality (Pressman, 2010) (Sommerville, 2011) (Gehring et al, 2017) (Wu, 2010. In terms of quality, it is certainly not easy to justify the quality of an application product or not.…”
Section: Resultsmentioning
confidence: 99%
“…(5) The evaluation of old parameters: evaluate the input parameters based on the results obtained in (4). If the coverage criterion is met, exit.…”
Section: Genetic Algorithm Processmentioning
confidence: 99%
“…It is mainly about constructing the corresponding chromosome string in accordance with the format of encoding parameter and generating an initial population. Then repeat AG operations (selection, crossover and mutation) on this population in order to guide the evolution of the population to the target value until a solution is eventually found out [4] [5].…”
Section: Materials Sciencementioning
confidence: 99%
“…If the predetermined number of iteration has been accomplished and it doesn't meet the indicator, return to step (6), or modify the genetic strategy and go back to step (6), in the decision of whether the performance of the group meets a certain indicator [6]. Flow chart of genetic algorithm is shown in Fig.…”
Section: Genetic Algorithm Processmentioning
confidence: 99%