Model-based test case prioritization using selective and even-spread count-based methods with scrutinized ordering criterion.

Regression testing is crucial in ensuring that modifications made did not introduce any adverse effect on the software being modified. However, regression testing suffers from execution cost and time consumption problems. Test case prioritization (TCP) is one of the techniques used to overcome these issues by re-ordering test cases based on their priorities. Model-based TCP (MB-TCP) is an approach in TCP where the software models are manipulated to perform prioritization. The issue with MB-TCP is that most of the existing approaches do not provide satisfactory faults detection capability. Besides, their granularity of test selection criteria is not very good and this can affect prioritization effectiveness. This study proposes an MB-TCP approach that can improve the faults detection performance of regression testing. It combines the implementation of two existing approaches from the literature while incorporating an additional ordering criterion to boost prioritization efficacy. A detailed empirical study is conducted with the aim to evaluate and compare the performance of the proposed approach with the selected existing approaches from the literature using the average of the percentage of faults detected (APFD) metric. Three web applications were used as the objects of study to obtain the required test suites that contained the tests to be prioritized. From the result obtained, the proposed approach yields the highest APFD values over other existing approaches which are 91%, 86% and 91% respectively for the three web applications. These higher APFD values signify that the proposed approach is very effective in revealing faults early during testing. They also show that the proposed approach can improve the faults detection performance of regression testing.

Experts and novices use the same factors--but differently--to evaluate pearl quality.

Well-trained experts in pearl grading have been thought to evaluate pearls according to their glossiness, interference color, and shape. However, the characteristics of their evaluations are not fully understood. Using pearl grading experiments, we investigate the consistency of novice (i.e., without knowledge of pearl grading) and expert participants' pearl grading skill and then compare the novices' grading with that of experts; furthermore, we discuss the relationship between grading, interference color, and glossiness. We found that novices' grading was significantly less concordant with experts average grading than was experts' grading; more than half of novices graded pearls the opposite of how experts graded those same pearls. However, while experts graded pearls more consistently than novices did, novices' consistency was relatively high. We also found differences between the groups in regression analyses that used interference color and glossiness as explanatory variables and were conducted for each trial. Although the regression coefficient was significant in 60% of novices' trials, there were fewer significant trials for the experts (20%). This indicates that novices can also make use of these two factors, but that their usage is simpler than that of the experts. These results suggest that experts and novices share some values about pearls but that the evaluation method is elaborated for experts.

The use of UV-visible reflectance spectroscopy as an objective tool to evaluate pearl quality.

Assessing the quality of pearls involves the use of various tools and methods, which are mainly visual and often quite subjective. Pearls are normally classified by origin and are then graded by luster, nacre thickness, surface quality, size, color and shape. The aim of this study was to investigate the capacity of Artificial Neural Networks (ANNs) to classify and estimate the quality of 27 different pearls from their UV-Visible spectra. Due to the opaque nature of pearls, spectroscopy measurements were performed using the Diffuse Reflectance UV-Visible spectroscopy technique. The spectra were acquired at two different locations on each pearl sample in order to assess surface homogeneity. The spectral data (inputs) were smoothed to reduce the noise, fed into ANNs and correlated to the pearl's quality/grading criteria (outputs). The developed ANNs were successful in predicting pearl type, mollusk growing species, possible luster and color enhancing, donor condition/type, recipient/host color, donor color, pearl luster, pearl color, origin. The results of this study shows that the developed UV-Vis spectroscopy-ANN method could be used as a more objective method of assessing pearl quality (grading) and may become a valuable tool for the pearl grading industry.

Alfons Serrahima orfebre y joyero del deporte / Alfons Serrahima goldsmith and jeweller of sport

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