A MODEL FOR PROGRAM ERROR PREDICTION BASED ON TESTING CHARACTERISTICS AND ITS EVALUATION
Kazuhiro Esaki, Muneo Takahashi · International Journal of Reliability Quality and Safety Engineering · 1999
There are two types of models for predicting software reliability at the end of testing. One is the software reliability growth model (dynamic model) based on a given set of time series data. The other is the software complexity model (static model) based on the development environmental factors which have an influence on the software reliability. As the dynamic model depends on the time factor and the test method used, its prediction accuracy does not necessarily correspond to the data of practical projects. On the other hand, the static model needs the many significant parameters to accurately predict the software reliability. However, it is very difficult to select the main factors that determine the significant parameters out of a great number of factors which affect software reliability. In order to resolve these problems, this paper proposes a model to predict the number of embedded errors in a program at the end of testing phase. This model is based on the testing characteristics such as error detection rate and test case density. The result of an experiment shows that the proposed model is more reliable than the conventional models.