Research of testing method based on UML statecharts

Unified modeling language （UML） is a powerful graphical modeling language with intuitional meaning. It provides various diagrams to depict system characteristics and complex environment from different viewpoints and different application layers. UML-based software development and modeling environments have been widely accepted in industry, including areas in which safety is an important issue such as spaceflight, defense, automobile, etc. To ensure and improve software quality becomes a main concern in the field. As one of the key techniques for software quality, software testing can effectively detect system faults. UML based software testing based is an important research direction in software engineering. The key to software testing is the generation of test cases. This dissertation studies an approach to generating test cases from UML statecharts.

Block-based test data adequacy measurement criteria and test complexity metrics

On the basis of software testing tools we developed for programming languages, we firstly present a new control flowgraph model based on block. In view of the notion of block, we extend the traditional program\|based software test data adequacy measurement criteria, and empirically analyze the subsume relation between these measurement criteria. Then, we define four test complexity metrics based on block. They are J\|complexity 0; J\|complexity 1; J\|complexity \{1+\}; J\|complexity 2. Finally, we show the Kiviat diagram that makes software quality visible.

New artificial neural networks for true triaxial stress state analysis and demonstration of intermediate principal stress effects on intact rock strength

Simulations are conducted using five new artificial neural networks developed herein to demonstrate and investigate the behavior of rock material under polyaxial loading. The effects of the intermediate principal stress on the intact rock strength are investigated and compared with laboratory results from the literature. To normalize differences in laboratory testing conditions, the stress state is used as the objective parameter in the artificial neural network model predictions. The variations of major principal stress of rock material with intermediate principal stress, minor principal stress and stress state are investigated. The artificial neural network simulations show that for the rock types examined, none were independent of intermediate principal stress effects. In addition, the results of the artificial neural network models, in general agreement with observations made by others, show （a） a general trend of strength increasing and reaching a peak at some intermediate stress state factor, followed by a decline in strength for most rock types; （b） a post-peak strength behavior dependent on the minor principal stress, with respect to rock type; （c） sensitivity to the stress state, and to the interaction between the stress state and uniaxial compressive strength of the test data by the artificial neural networks models （two-way analysis of variance; 95% confidence interval）. Artificial neural network modeling, a self-learning approach to polyaxial stress simulation, can thus complement the commonly observed difficult task of conducting true triaxial laboratory tests, and/or other methods that attempt to improve two-dimensional （2D） failure criteria by incorporating intermediate principal stress effects.

A method for threshold selection in load extrapolation based on multiple criteria decision-making technology

In order to establish an accurate peak over threshold(POT)model for reasonable load extrapolation,a new threshold selection method based on multiple criteria decision making(MCDM)technology is proposed.The fitting test criterion is taken into consideration in the method.For each candidate threshold,the fitting values of several fitting test criteria are integrated into a comprehensive evaluation value through entropy method and MCDM technology.The threshold corresponding to the minimum comprehensive evaluation value is assumed as the optimal threshold.A random simulation study is carried out to evaluate the performance of the method and to compare them with other literature methods.Genuine load data are applied the proposed method.Both the results shown that the proposed method could be seen as an additional method that complements existing threshold selection methods.