Nowadays, smartphones play a remarkable role in our lives. Testing mobile applications is significant to guarantee their quality. Automated testing is applied to minimize the cost and the interval of time instead of manual testing. There are different testing levels which are unit testing, integration testing, system testing and acceptance testing. Automated mobile application testing type methodologies are categorized into white-box testing, black-box testing and grey-box testing. Besides, there are several testing types such as functional testing and non-functional testing. Most of the existing studies focus on user interface testing which is type of functional testing. In this paper, testing approaches for user interface testing through different existing studies from 2013 to 2021 have been surveyed. Those approaches are classified into model-based testing, model learning testing, search-based testing, random-based testing, and record & replay testing. Several essential issues related to those approach such as the optimization and redundancy for generation of test suites have been mentioned. Finally, challenges in automated mobile applications user interface testing have been discussed.
Predicting students performance efficiently became one of the most interesting research topics. Efficiently mining the educational data is the cornerstone and the first step to make the appropriate intervention to help at-risk students achieve better performance and enhance the educational outcomes. The objective of this paper is to efficiently predict students' performance by predicting their academic performance level. This is achieved by proposing an enhanced aggregation strategy on a supervised multiclass classification problem to improve the prediction accuracy of students' performance. Two binary classification techniques: Support Vector Machine (SVM) and Perceptron algorithms, have been experimented to use their output as an input to the proposed aggregation strategy to be compared with a previously used aggregation strategy. The proposed strategy improved the prediction performance and achieved an accuracy, recall, and precision of 75.0%, 76.0%, and 75.48% using Perceptron, respectively. Moreover, the proposed strategy outperformed and achieved an accuracy, recall, and precision of 73.96%, 73.93%, and 75.33% using SVM, respectively.
Complexes of the 8-((furan-2-ylmethylene)amino)naphthalene-1-amine, Schiff base ligand with the metal ions, Cr(III), Co(II), Ni(II) and Cu(II) have been prepared and characterized by elemental analyses, IR, 1 H NMR, Uv-visible, magnetic moment, molar conductance and thermal analysis. The complexes are found to have the formulae [CrLCl(H 2 O) 3 ]Cl 2 .3H 2 O, [CoL 2 (H 2 O) 2 ]Cl 2 .3H 2 O, [NiL(H 2 O) 2 ]Cl 2 .4H 2 O and [CuL(H 2 O) 2 ]Cl 2 .2H 2 O. The conductance of complexes is measured and revealed their electrolyticnature. Thermal analysis and thermodynamic parameters of the complexes were investigated and indicated the presence of hydrated water molecules. The antimicrobial activity is assayed in vitro against two fungi and four bacteria species.
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