GA-based multiple paths test data generator

Developers have learned over time that software testing costs a considerable amount of a software project budget. Hence, software quality managers have been looking for solutions to reduce testing costs and time. Considering path coverage as the test adequacy criterion, we propose using genetic algorithms (GA) for automating the generation of test data for white-box testing. There are evidences that GA has been already successful in generating test data. However, existing GA-based test data generators suffer from some problems. This paper presents our approach to overcome one of these problems; that is the inefficiency in covering multiple target paths. We have designed a GA-based test data generator that is, in one run, able to synthesize multiple test data to cover multiple target paths. Moreover, we have implemented a set of variations of the generator. Experimental results show that our test data generator is more efficient and more effective than others.     

Effect of Applied Load in the Nanoindentation of Gold Ball Bonds

We have analyzed the effects of nanoindentation at applied loads of 10 mN and 20 mN on the micromechanical properties of gold (Au) ball bonds with and without cracks. The depth profile and the plastic zone size for each indentation were determined to identify the substrate effect and its relationship with the observed micromechanical properties. The substrate effect occurred for indentations at 20 mN applied load, but did not occur near cracks for either 10 mN or 20 mN loads. Because of the substrate effect and the presence of cracks, the average hardness or yield strength decreased for indentations on Au ball bonds. Therefore, to minimize the substrate effect, an applied load of 10 mN is best for characterizing Au ball bonds.     

Neural network modelling of cold-cathode gauge parameters

This article describes modelling of the operating characteristics of a cold-cathode ionisation gauge (CCG). The gauge characteristics were measured on a gauge comparison UHV calibration system with a test chamber, an extractor gauge, a spinning rotor gauge, and a gas manifold with a precise leak valve. Discharge intensity was measured vs. anode voltage at different pressures selected in the range from 1×10⁻⁹ to 1×10⁻⁵ mbar, and vs. pressure at different operating voltages ranging from 1.2 to 9 kV. In all cases the magnetic flux density was the same and amounted to about 0.13 T. The CCG exhibits an extremely low thermal outgassing rate and a low measurement limit. Therefore, it is suitable for pressure measurements in the ultrahigh vacuum range; however, it has a significant disadvantage. The discharge current vs. the pressure characteristic is non-linear and, in some cases, even discontinuous.The measured CCG characteristics were used as an input for the artificial neural network, which was used to generate a non-linear CCG input-output function used for linearisation purposes. It is generally known and strictly proven that neural networks are capable of learning and building any kind of real and non-polynomial input-output function. Furthermore, it was also mathematically proven that the single hidden neural layer system can learn any function. Other authors have reported that the learned function characteristics are not always continuous.In our experimental work, no mapping discontinuities in the formed model were detected. Despite the fact that learning of the input-output characteristics can be obtained by the neural networks with only one hidden layer, we have used the multilayer neural networks that exhibit a faster convergent and smoother learning process. The neural networks were trained to perform the transfer function between the input gauge parameters and the pressure. The neural networks are a suitable solution for CCG characteristics modelling and thus offer the possibility to overcome the disadvantages of the CCG.