Automatic Test Research Articles

Luminescent Properties of (Ca7ZrAl6O18-Ca3Al2O6-CaZrO3):Eu3+ Composite Ceramics and Tracing in the Hydration Process

In this work, solid-state reaction sintering was used to fabricate Ca7ZrAl6O18-Ca3Al2O6-CaZrO3:Eu3+ ternary composite ceramics and cements. The structural, microstructural, and spectroscopic properties of the ceramics with different Eu2O3 content were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS), and spectrofluorimetry, respectively. The XRD patterns analyzed with Rietveld refinement confirm the presence of the orthorhombic phase of Ca7ZrAl6O18 and the cubic phase of Ca3Al2O6 in all the samples, indicating that doping of Eu3+ slightly changes the crystalline structure of both aluminate phosphors. EDS analysis revealed that the Eu doping element was strongly concentrated to the two phases, i.e., Ca7ZrAl6O18 and Ca3Al2O6, with the Eu concentrations of 8.45 wt.% and 8.26 wt.%, respectively. The luminescent properties of the ceramics doped with different Eu3+ ion concentrations were investigated by excitation and emission spectroscopy at room temperature. These results were compiled using a laser with an optical parametric oscillator (OPO) system. The obtained spectra indicated changes in the luminescence intensity and shape occurring with phase composition and Eu2O3 concentration. The emission spectra of the ceramics exhibit a strong dependence on the excitation wavelength in the range from 210 to 300 nm, and invariably, five peaks were assigned to the 5D0 → 7FJ (J = 0, 1, 2, 3, 4) transitions of Eu3+. The luminescence spectroscopy was then used to trace the early and long-term hydration behavior of cements. Thus, luminescence spectroscopy may provide a new tool for non-destructive testing of cement-based structures.

Targeted sequencing of high-density SNPs provides an enhanced tool for forensic applications and genetic landscape exploration in Chinese Korean ethnic group

BackgroundIn this study, we present a NGS-based panel designed for sequencing 1993 SNP loci for forensic DNA investigation. This panel addresses unique challenges encountered in forensic practice and allows for a comprehensive population genetic study of the Chinese Korean ethnic group. To achieve this, we combine our results with datasets from the 1000 Genomes Project and the Human Genome Diversity Panel.ResultsWe demonstrate that this panel is a reliable tool for individual identification and parentage testing, even when dealing with degraded DNA samples featuring exceedingly low SNP detection rates. The performance of this panel for complex kinship determinations, such as half-sibling and grandparent-grandchild scenarios, is also validated by various kinship simulations. Population genetic studies indicate that this panel can uncover population substructures on both global and regional scales. Notably, the Han population can be distinguished from the ethnic minorities in the northern and southern regions of East Asia, suggesting its potential for regional ancestry inference. Furthermore, we highlight that the Chinese Korean ethnic group, along with various Han populations from different regional areas and certain northern ethnic minorities (Daur, Tujia, Japanese, Mongolian, Xibo), exhibit a higher degree of genetic affinities when examined from a genomic perspective.ConclusionThis study provides convincing evidence that the NGS-based panel can serve as a reliable tool for various forensic applications. Moreover, it has helped to enhance our knowledge about the genetic landscape of the Chinese Korean ethnic group.

Quantitative x ray phase contrast imaging of oblique shock wave–interface interactions

Oblique shock wave–interface interactions of gases and liquids have been extensively studied in shock tubes using optical methods to measure equation-of-state (EOS) parameters. However, this is difficult with solids due to their opaqueness to visible light. X ray phase contrast imaging (XPCI) has the penetrative strength to probe solids while still being sensitive to mass density and enhancing the visibility of material boundaries. We investigate the accuracy and repeatability of measuring the mean value of the average mass density (areal density divided by thickness) over region S (BS) and flow deflection angle (θ) from XPCI images of a sample. To that end, a Hough transform-based method for measuring θ is developed. To measure BS, the XPCI image intensity probability density function (PDF) is modeled accounting for the spatial distribution of x ray energy, scintillator response, and pulse-to-pulse variation in the x ray intensity. In addition, a Monte Carlo-based algorithm for computing the BS PDF is developed. Both methods are validated on an impact-generated oblique shock wave interacting at a solid polymer-to-polymer interface. This is accomplished through a comparison to hydrodynamic simulations using well-established EOS. Under the modeling framework for the XPCI image intensity, BS is computed with an accuracy of <0.1% and precision of 3%–5%, while θ has an uncertainty of 0.2°, respectively. This shows that the XPCI-based model that is developed here could be an invaluable tool for high-fidelity testing of hydrodynamic models in shock polar configurations.

Real time detection of pathogenic bacteria in veterinary microbiology using isothermal microcalorimetry – A different approach

With today’s challenges regarding antibiotic resistance and the importance of the implementation of prudent use of antibiotics, fast and reliable diagnostic tools for bacterial infections and subsequent antimicrobial susceptibility testing are of utmost relevance. Isothermal microcalorimetry (IMC) is a broadly applicable method, with which metabolic heat flow in reproducing bacteria can be measured in real time. To the best of the authors' knowledge, this is the first report on examination of 124 urine samples from feline and canine urinary tract infection with an IMC-based prototype instrument. A concentration-dependent time of peak heat flow by dilution series with Escherichia coli and Enterococcus faecalis reference strains demonstrated the general good performance of the prototype for detection of these bacteria. With diagnostic culture being set as a gold standard, the diagnostic sensitivity of IMC compared to bacteriological culture was 80 %, the diagnostic specificity was 97 %. With a Cohens' kappa value (κ) of 0.80, the two methods show good concordance. The results from our study demonstrate that the IMC technology is suitable to allow reliable, but much faster detection of bacteria than conventional culture, especially for Escherichia coli. Thus, implementing IMC technology could markedly speed up the bacteriological diagnostic process in veterinary medicine.

Modulation of Kynurenic Acid Production by N-acetylcysteine Prevents Cognitive Impairment in Adulthood Induced by Lead Exposure during Lactation in Mice.

Lead (Pb2+) exposure during early life induces cognitive impairment, which was recently associated with an increase in brain kynurenic acid (KYNA), an antagonist of NMDA and alpha-7 nicotinic receptors. It has been described that N-acetylcysteine (NAC) favors an antioxidant environment and inhibits kynurenine aminotransferase II activity (KAT II, the main enzyme of KYNA production), leading to brain KYNA levels decrease and cognitive improvement. This study aimed to investigate whether the NAC modulation of the brain KYNA levels in mice ameliorated Pb2+-induced cognitive impairment. The dams were divided into four groups: Control, Pb2+, NAC, and Pb2++NAC, which were given drinking water or 500 ppm lead acetate in the drinking water ad libitum, from 0 to 23 postnatal days (PNDs). The NAC and Pb2++NAC groups were simultaneously fed NAC (350 mg/day) in their chow from 0 to 23 PNDs. At PND 60, the effect of the treatment with Pb2+ and in combination with NAC on learning and memory performance was evaluated. Immediately after behavioral evaluation, brain tissues were collected to assess the redox environment; KYNA and glutamate levels; and KAT II activity. The NAC treatment prevented the long-term memory deficit exhibited in the Pb2+ group. As expected, Pb2+ group showed redox environment alterations, fluctuations in glutamate levels, and an increase in KYNA levels, which were partially avoided by NAC co-administration. These results confirmed that the excessive KYNA levels induced by Pb2+ were involved in the onset of cognitive impairment and could be successfully prevented by NAC treatment. NAC could be a tool for testing in scenarios in which KYNA levels are associated with the induction of cognitive impairment.

Flakiness goes live: Insights from an In Vivo testing simulation study

Context:Test flakiness is a topmost concern in software test automation. While conducting pre-deployment testing, those tests that are flagged as flaky are put aside for being either repaired or discarded. Objective:We hypothesise that some flaky tests could provide useful insights if run in the field, i.e., they could help identify failures that manifest themselves sporadically during In House testing, but are later experienced in operation. Method:We present the first simulation study to investigate the behaviour of flaky tests when moved to the field. The work compares the behaviour of known flaky tests from an open-source library when executed in the development environment vs. when executed in a simulation of the field. Results:Our experimentation over 52 test methods labelled as flaky provides a first confirmation that moving from the development environment to the field, the behaviour of tests changes. In particular, the failure frequency of intermittently failing tests can increase, and we could also identify few cases of field failures that would have been hardly detected during In House testing due to the numerous combinations of inputs and states. In most cases, such flakiness was rooted in the design of the test method itself, however we could also identify an actual bug. Conclusion:The results of our study suggest that the identification of an intermittently failing behaviour could be a valuable hint for a test engineer, and hence flaky tests should not be dismissed right away.

Quantifying Success: Measuring ROI in Test Automation

Purpose: The purpose of this paper to provide strategic decision to implement test automation with measurable ROI is a favoured approach for organizations seeking to improve the quality of their software, expedite delivery, and mitigate testing expenses.
 Methodology: The present paper, titled "Quantifying Success: Measuring ROI in Test Automation," delves deeply into the fundamental aspects of ROI evaluation within the realm of test automation. It outlines the methodology behind ROI calculation, explores the cost factors and advantages associated with automation, and sheds light on the optimal strategies for achieving a favourable ROI.
 Findings: By presenting real-world case studies and examples, this paper effectively demonstrates the practical application of ROI measurement in test automation. The formula for Return on Investment (ROI) typically falls under the category of being "basic". While costs are relatively simple to measure, assessing the worth of potential advantages proves to be considerably more challenging.
 Unique Contribution to Theory, Policy, and Practice: Approaches to calculating ROI often oversimplify various aspects of test automation, subsequently resulting in inaccurate outcomes. These inaccuracies, in turn, foster impractical expectations among management in relation to test automation, ultimately culminating in failure at individual, team, and occasionally project levels, thereby offering valuable insights to organizations seeking to optimize the efficacy of their testing endeavours.

RoboWorld: Verification of Robotic Systems with Environment in the Loop

A robot affects and is affected by its environment, so that typically its behaviour depends on properties of that environment. For verification, we need to formalise those properties. Modelling the environment is very challenging, if not impossible, but we can capture assumptions. Here, we present RoboWorld, a domain-specific controlled natural language with a process algebraic semantics that can be used to define (a) operational requirements, and (b) environment interactions of a robot. RoboWorld is part of the RoboStar framework for verification of robotic systems. In this article, we define RoboWorld’s syntax and hybrid semantics, and illustrate its use for capturing operational requirements, for automatic test generation, and for proof. We also present a tool that supports the writing of RoboWorld documents. Since RoboWorld is a controlled natural language, it complements the other RoboStar notations in being accessible to roboticists, while at the same time benefitting from a formal semantics to support rigorous verification (via testing and proof).

A Study on an Intelligent Algorithm for Automatic Test Paper Generation and Scoring in University English Exams

This paper mainly studied the automatic test paper generation and scoring problems in university English exams. Firstly, an automatic test paper generation model was established. Then, an improved genetic algorithm (IGA) was designed for intelligent test paper generation, and it was also used to automatically score answers to Chinese-to-English translation questions in terms of syntax and semantics. It was found that compared with the traditional GA and particle swarm optimization algorithm, the IGA method was faster in generating test papers, with an average generation time of 25 s, and had a higher success rate (94%), suggesting higher validity, and the difficulty and differentiation degrees of the test papers were closer to the preset values. The results of automatic scoring also had a correlation of more than 0.8 with the manual scoring results. The results prove the effectiveness of the automatic test paper generation and scoring method. It can be promoted and applied in practice to enhance the security and fairness of large-scale English exams, as well as achieve objectivity and consistency in scoring.

Fuzzy modelling and inference for physics-aware road vehicle driver behaviour model calibration

Traffic simulation is a crucial tool for testing and evaluating hypotheses in both the design of autonomous driving systems and the optimization of urban transportation, where modelling and calibrating driver behaviour is key. In this article, we introduce a novel approach to calibrating driver models by combining fuzzy logic and inference to produce a plausible parametrization that, simultaneously, reproduces drivers’ behaviour peculiarities and road traffic characteristics. The calibration system uses solely vehicle trajectories that describe cars’ movements through the road network, with a resolution of time and space enough to allow for determining the lane of the road used, speed, and acceleration at every second of the survey period. This data is processed by a lightweight modelling and inference system capable of interpreting the input data through the lens of domain knowledge and physics laws in order to learn plausible driver model parameters using fuzzy computation. The outcome is a set of parameters for mathematical models which emulate the social and physical features of the behaviour of a driver-vehicle unit. Albeit this study limits itself to determining parameters for car-following and lane-change models, the systematic approach for calibration can be seen as a framework where also other driver model components can be calibrated (e.g. gap-acceptance). We evaluate our system on both synthetic and real data while comparing it with other approaches for calibration. We summarize our findings using a thorough analysis where we point out the most relevant aspects to consider when calibrating driver models.

Cost-Benefit Analysis of Open-Source VS. Commercial Test Automation Frameworks in Large-Scale Enterprise Applications

Cost-Benefit Analysis of Open-Source VS. Commercial Test Automation Frameworks in Large-Scale Enterprise Applications

PyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information

Abstract. The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the application of empirical-statistical downscaling (ESD) models to general circulation model (GCM) simulations of future climate. In contrast to dynamical downscaling, the perfect prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM-specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties reproducing existing data products and assessing their credibility, and (3) difficulties reconciling different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework called pyESD. pyESD implements the entire downscaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established machine learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, and hyperparameter optimization in relatively few lines of code. The package is well-documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and effectiveness of the new PP-ESD framework by generating weather-station-based downscaling products for precipitation and temperature in complex mountainous terrain in southwestern Germany. The application example covers all important steps of the downscaling cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results and (2) simplify learning to use the software package.

OBGESS: Automating Original Bender Gestalt Test Based on One Stage Deep Learning

Hand sketch psychological data are mysterious and can be used to detect mental disorders early and prevent them from getting worse and with irreversible consequences. The Original Bender Gestalt Test is a psychology test based on hand-sketched patterns. Mental disorders require an automated scoring system. Unfortunately, there is no automatic scoring system for the Original Bender Gestalt test for adults and children with high accuracy. Automating the Original Bender Gestalt test requires 3 phases: Phase 1, collecting a comprehensive Original Bender Gestalt dataset called OBGET. Phase 2, classifying patterns by a proposed method called MYOLO V5; and Phase 3, scoring classified patterns according to associated rules of psychological standard criteria. This research reviews a comprehensive OBGET dataset that includes 817 samples, labeling samples for mental disorders by a psychologist, statistical analysis, the proposed semi-automatic labeling of patterns, patterns classification applied the proposed modified YOLO V5 called MYOLO V5, and automatic scoring of drawing patterns. MYOLO V5 accuracy is 95% and the accuracy of the proposed method called OBGESS as a mental disorder detection is 90%. In this research, a new automatic computer-aided psychological hand sketch drawing test has been proposed.

Research of the efficiency of computing services management platforms in the organization of fog computing

The work is devoted to studying the effectiveness of computing service management platforms in the organization of Fog Computing. As part of the work, the effectiveness of container orchestration platforms with the Fog computing organization is being studied. During the research, it is necessary to complete the following tasks: 1) select literature, scientific publications and Internet articles necessary for the research; 2) inspect container orchestration platforms; 3) determine the key requirements and criteria for conducting the study; 4) design and implement an automatic testing utility; 5) conduct a study of the effec-tiveness of container orchestration platforms with the organization of fog computing; 6) ana-lyze the results obtained and draw related conclusions. Deployment of Docker containers is organized. Docker Swarm is used to create a clus-ter. The problems of measuring the following parameters are solved: deployment time of one container, deployment time of a group of containers, response time of the horizontal zoom task, transmission delay time. The analysis of the obtained test results is carried out.

Co-worker Relations and Working Environment: Influences on Turnover Intentions in Co-operatives of Surkhet

This study aims to construct a conceptual framework for analyzing and forecasting the factors that influence employee turnover intentions in cooperative organizations in Surkhet, Nepal. Cooperatives play a crucial role in economic growth as they generate jobs, collect and invest funds, and offer a wide range of public services. Cooperatives engage in various activities to achieve their objectives, but they have been struggling with high turnover rates. The study employed a cross-sectional design, with data collected in a single contact with the respondents. The study's demographic consisted of all cooperative employees in Surkhet, and a convenience sampling method was used to gather 167 responses through a structured Likert questionnaire. Data analysis was conducted using SPSS and AMOS 23 software, with structural equation modeling as the statistical tool for hypothesis testing. Hypotheses were formulated using a deductive research approach. The results indicate that co-worker relations and the working environment were significantly associated with employee turnover intentions among cooperative employees. However, promotions showed no significant association with turnover intentions. This study has certain limitations, including the use of cross-sectional data, self-reports, variations in employee education levels, the comprehension of content, and a relatively small sample size. The respondents were a relatively uniform group of employees, so the results may not be generalized to other employees and institutions. Nevertheless, the study's findings suggest that cooperative organizations should place greater emphasis on improving co-worker relations and the working environment to reduce turnover intentions. This research can be particularly useful for managers in cooperative organizations.

NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States.

Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25-0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R2C = 0.979, R2P = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R2C = 0.977, R2P = 0.981, RPD = 7.222) and effective phosphorus (R2C = 0.969, R2P = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.

Test automation for augmented reality applications: a development process model and case study

Abstract Poor software quality results in avoidable costs of trillion dollars annually in the United States alone. Augmented Reality (AR) applications are a relatively new software category. Currently there are no standards to guide the development process and testing is predominantly ad hoc and manual. Consequently, design guidelines and software test automation techniques are intended to remedy the situation. Here, we present a concept for test automation of AR applications. The concept consists of two parts: design guidelines and process model for testing AR applications, and a case study with a prototype application for test automation. The design guidelines and the process model are based on the state-of-the-art. The prototype application presented in this article demonstrates test automation for a multi-platform AR application for Android devices as well as the HoloLens 2. The presented test automation case study is designed to cover a large part of the functions, such as the different interaction variants. This research work shows that by using the proposed process model and test automation techniques, testing of some features of AR applications can be automated. The results of this research can serve as a basis for future research and contribution towards AR application development standardization efforts.

Модельно-управляемое проектирование автоматизированной системы идентификации дефектов листового стекла

An information model of an automated system for identifying point defects of sheet glass is proposed, imple-mented using Harmony technology and the IBM Rhapsody Modeler software environment, which provides automation of modeling, design and testing of software. For automated detection (localization) and determination of the type (identification) of defects in sheet glass based on the class diagram, modules are implemented that are responsible for the implementation of localization algorithms, identification, interaction with sensors and cameras, archiving, data transmission to the network and data generation for display on the monitor of the operator and technologist. The industrial computer software is implemented in C++ in MS Visual Studio. The application of the Harmony software lifecycle management methodology and the IBM Rhapsody Modeler visual modeling environment has made it possible to increase the efficiency of the development of an automated system for identifying point defects of flat glass. The resulting program model of the automated system simplifies its adaptation to new operating requirements and the connection of additional hardware.

Sedimentation Field-Flow Fractionation: A Diagnostic Tool for Rapid Antimicrobial Susceptibility Testing.

Conventional antimicrobial susceptibility testing (AST) methods require 24-48 h to provide results, creating the need for a probabilistic antibiotic therapy that increases the risk of antibiotic resistance emergence. Consequently, the development of rapid AST methods has become a priority. Over the past decades, sedimentation field-flow fractionation (SdFFF) has demonstrated high sensitivity in early monitoring of induced biological events in eukaryotic cell populations. This proof-of-concept study aimed at investigating SdFFF for the rapid assessment of bacterial susceptibility to antibiotics. Three bacterial species were included (Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) with two panels of antibiotics tailored to each bacterial species. The results demonstrate that SdFFF, when used in "Hyperlayer" elution mode, enables monitoring of antibiotic-induced morphological changes. The percentage variation of the retention factor (PΔR) was used to quantify the biological effect of antibiotics on bacteria with the establishment of a threshold value of 16.8% to differentiate susceptible and resistant strains. The results obtained with SdFFF were compared to that of the AST reference method, and a categorical agreement of 100% was observed. Overall, this study demonstrates the potential of SdFFF as a rapid method for the determination of antibiotic susceptibility or resistance since it is able to provide results within a shorter time frame than that needed for conventional methods (3-4 h vs 16-24 h, respectively), enabling earlier targeted antibiotic therapy. Further research and validation are necessary to establish the effectiveness and reliability of SdFFF in clinical settings.

Effectively computing high strength mixed covering arrays with constraints

Covering arrays have become a key piece in Combinatorial Testing. In particular, we focus on the efficient construction of Covering Arrays with Constraints of high strength. SAT solving technology has been proven to be well suited when solving Covering Arrays with Constraints. However, the size of the SAT reformulations rapidly grows up with higher strengths. To this end, we present a new incomplete algorithm that mitigates substantially memory blow-ups and its parallel version that allows reducing run-time consumption. Thanks to these new developments we provide a tool for Combinatorial Testing in practical environments. The experimental results confirm the goodness of the approach, opening avenues for new practical applications.