Automatic Test Research Articles

EFL Students’ Achievement in Reading Comprehension through Gamification Kahoot

This study attempts to examine EFL students’ achievement in reading comprehension of recount text through gamification Kahoot, a technological-based learning platform, as well as to explore their perceptions of using gamification Kahoot as an assessment tool in the reading test. The data were obtained from both tests and questionnaires, and the assessment criteria used as proposed by Kartika (2001), and uses descriptive analysis. Meanwhile, the students’ perceptions are based on the indicator theory of perceptions proposed by Hamka (2002). 36 students (21 females, and 15 male students) in the tenth grade of the State Vocational High School 1 Godean in the academic year 2022/2023 participated as the respondents. The findings revealed that the highest score on the test was 100 and the lowest score was 53. Meanwhile, the average or mean of the student's score is 74 with a total score of 2664. Furthermore, the achievement of students in reading comprehension of recount text using gamification Kahoot is in the "Good" category (74). Meanwhile, the students’ perceptions showed that gamification Kahoot as an assessment tool can motivate students, and create fun classroom conditions.

Improving the efficiency of symbolic execution by clustering the input data based on the complexity of test generation

The object of research is means to increase computational effectiveness for automatic unit test generation process. It provides arguments for developing new method to achieve wider use of symbolic execution in commercial software development. The main task of the research is to create adaptive code clustering method that considers test generation complexity for structural source code elements and available computational resources that will increase effectiveness of computations. It is achieved by estimating test generation complexity and balancing the it for produced clusters during clusterization. As a result, proposed clustering method is adaptive to hardware and source code variability. It is shown that developed approach provides up to 30 % increase in computation effectiveness compared to clustering based on code structural properties alone for selected samples and up to 250 % in separate cases. This is caused by balanced estimated test generation complexity within generated clusters. It limits path explosion to expected levels that match computational resources for every cluster. Estimate of test generation complexity makes it possible to stop the computation when the spent time exceeds the corresponding complexity limit. Consequently, it makes it possible to prevent performing unnecessary computations. Proposed method makes it possible to use symbolic execution in commercial software development due to higher adaptability for source code and hardware variations. It will allow to reduce expenses on early-stage software testing and provide means for determining feasibility of symbolic execution for commercial projects

Automated Segmentation of the Mouse Body Language to Study Stimulus-Evoked Emotional Behaviors.

Understanding the neural basis of emotions is a critical step to uncover the biological substrates of neuropsychiatric disorders. To study this aspect in freely behaving mice, neuroscientists have relied on the observation of ethologically relevant bodily cues to infer the affective content of the subject, both in neutral conditions or in response to a stimulus. The best example of that is the widespread assessment of freezing in experiments testing both conditioned and unconditioned fear responses. While robust and powerful, these approaches come at a cost: they are usually confined within selected time windows, accounting for only a limited portion of the complexity of emotional fluctuation. Moreover, they often rely on visual inspection and subjective judgment, resulting in inconsistency across experiments and questionable result interpretations. To overcome these limitations, novel tools are arising, fostering a new avenue in the study of the mouse naturalistic behavior. In this work we developed a computational tool [stimulus-evoked behavioral tracking in 3D for rodents (SEB3R)] to automate and standardize an ethologically driven observation of freely moving mice. Using a combination of machine learning-based behavioral tracking and unsupervised cluster analysis, we identified statistically meaningful postures that could be used for empirical inference on a subsecond scale. We validated the efficacy of this tool in a stimulus-driven test, the whisker nuisance (WN) task, where mice are challenged with a prolonged and invasive whisker stimulation, showing that identified postures can be reliably used as a proxy for stimulus-driven fearful and explorative behaviors.

User and Usability Testing of a Web-Based Genetics Education Tool for Parkinson Disease: Mixed Methods Study.

Genetic testing is essential to identify research participants for clinical trials enrolling people with Parkinson disease (PD) carrying a variant in the glucocerebrosidase (GBA) or leucine-rich repeat kinase 2 (LRRK2) genes. The limited availability of professionals trained in neurogenetics or genetic counseling is a major barrier to increased testing. Telehealth solutions to increase access to genetics education can help address issues around counselor availability and offer options to patients and family members. As an alternative to pretest genetic counseling, we developed a web-based genetics education tool focused on GBA and LRRK2 testing for PD called the Interactive Multimedia Approach to Genetic Counseling to Inform and Educate in Parkinson's Disease (IMAGINE-PD) and conducted user testing and usability testing. The objective was to conduct user and usability testing to obtain stakeholder feedback to improve IMAGINE-PD. Genetic counselors and PD and neurogenetics subject matter experts developed content for IMAGINE-PD specifically focused on GBA and LRRK2 genetic testing. Structured interviews were conducted with 11 movement disorder specialists and 13 patients with PD to evaluate the content of IMAGINE-PD in user testing and with 12 patients with PD to evaluate the usability of a high-fidelity prototype according to the US Department of Health and Human Services Research-Based Web Design & Usability Guidelines. Qualitative data analysis informed changes to create a final version of IMAGINE-PD. Qualitative data were reviewed by 3 evaluators. Themes were identified from feedback data of movement disorder specialists and patients with PD in user testing in 3 areas: content such as the topics covered, function such as website navigation, and appearance such as pictures and colors. Similarly, qualitative analysis of usability testing feedback identified additional themes in these 3 areas. Key points of feedback were determined by consensus among reviewers considering the importance of the comment and the frequency of similar comments. Refinements were made to IMAGINE-PD based on consensus recommendations by evaluators within each theme at both user testing and usability testing phases to create a final version of IMAGINE-PD. User testing for content review and usability testing have informed refinements to IMAGINE-PD to develop this focused, genetics education tool for GBA and LRRK2 testing. Comparison of this stakeholder-informed intervention to standard telegenetic counseling approaches is ongoing.

Algorithm for Automatic Rod Feeding and Positioning Error Compensation for Underground Drilling Robots in Coal Mines.

In the pursuit of automating the entire underground drilling process in coal mines, the automatic rod feeding technology of drilling robots plays a crucial role. However, the current lack of positional accuracy in automatic rod feeding leads to frequent accidents. To address this issue, this paper presents an algorithm for compensating positioning errors in automatic rod feeding. The algorithm is based on a theoretical mathematical model and manual teaching methods. To enhance the positioning accuracy, we first calibrate the pull rope sensor to correct its measurement precision. Subsequently, we establish a theoretical mathematical model for rod feeding positions by employing spatial coordinate system transformations. We determine the target rod feeding position using a manual teaching-based approach. Furthermore, we analyze the relationship between the theoretical rod delivery position and the target rod delivery position and propose an anisotropic spatial difference compensation technique that considers both distance and direction. Finally, we validate the feasibility of our proposed algorithm through automatic rod feeding tests conducted on a coal mine underground drilling robot. The results demonstrate that our algorithm significantly improves the accuracy of rod feeding positions for coal mine underground drilling robots.

Smartphones swabs analysis as a promising tool for drug testing? A proof of concept study

Smartphones swabs analysis as a promising tool for drug testing? A proof of concept study

Automatic calibration toolbox for SWMM5

The objective of this paper is to introduce an enhanced automatic calibration toolbox for the Storm Water Management Model (SWMM5), one of the most widely used softwares for simulating urban stormwater systems. Within the process of accurately modelling urban runoff and drainage, proper calibration is the most time-intensive sub-task. Thus, a toolbox is developed that eliminates the need to manually re-read input files for each parameter change made during calibration. The Excel-based toolbox written in Visual Basic for Applications (VBA) makes use of a Dynamic-link library (DLL), which has been extended with several new functions. The functionality of the automatic calibration toolbox has been increased by adding new groundwater-related parameters to the DLL. In this article the authors successfully tested automatic calibration toolbox performance test cases, based on example input files provided by SWMM5 developers. The first test case was chosen to demonstrate the program's ability to calibrate using measurements from the conduits, and the second one to demonstrate the performance of using measurements from nodes.

(Invited) Translational Applications of Nanostructured Biosensors: Diagnostics at the Point of Care

Development of diagnostic devices with clinically relevant sensitivity and rapidity is highly desirable for decreasing the delay between diagnosis and treatment. Diagnostic inefficiency permeates multiple medical fields including infectious diseases and antimicrobial resistance (both recognized by WHO among paramount threats and research priorities). Molecular detection is also central to cancer, where therapies are often out of step with disease complexity and progression. The respective challenges may be addressed through the application of nanomaterial and high-throughput devices that offer unique advantages. In Mahshid Lab, we develop novel paradigms in point of care diagnosis via synergistically combining innovative nanostructured sensors with fluidic sample delivery systems and biomolecular assay capabilities (Nano/Bio diagnostic devices).From an engineering perspective, the lab seeks to use the remarkable intrinsic properties of novel nanomaterials, to render them capable of sensing the specific biomolecules. Such miniaturized sensors could be integrated with automated lab-chip devices and deployed to diagnose molecular changes in biological systems and in disease such as cancer (by targeting new cancer biomarkers) or to detect infectious agents in biological samples, e.g. in blood, saliva and urine. From a health industry perspective, we target the advancement of the automated and portable tools for in-field testing, remote locations and hospitals in close collaboration with clinicians to validate the devices with clinical samples. In particular and in the context of infectious disease, Mahshid lab has developed SALIVERA analogous to a qPCR, and NFluidEX analogous to a glucometer, that enabled rapid portable automated monitoring of SARS-CoV-2 infection in patient saliva and antibodies in patient blood, respectively. In the context of cancer, Mahshid lab has developed MoSERS, anon-chip approach for molecular profiling of extracellular vesicles (a new cancer biomarker)on-chip approach for molecular profiling of extracellular vesicles (a new cancer biomarker)in plasma and cerebrospinal fluid of glioblastoma patients. The proposed hybrid devices are capable of working with small sample volumes and precise dosing of reagents, enabling the transition to a portable diagnostic tool.

The effect of a prior e-learning tool on genetic counseling outcomes in diverse ethnic couples with abnormal Down syndrome screening tests: A randomized controlled trial.

Genetic counseling (GC) following abnormal Down syndrome (DS) screening tests aims to ensure learning of complex medical concepts and discussion of counselees' personal desires. Pre-GC use of electronic learning tools (e-learning tools) can facilitate GC sessions by allowing more time for dialogue rather than learning medical and genetic concepts, enabling greater focus on the counselee's decisional, psychological, and personal needs. Few studies have investigated such tools for DS screening tests and those who have focused on screening uptake rather than abnormal results and implications. This study evaluated prenatal GC outcomes following implementation of an e-learning tool utilizing an educational animated movie for couples of varied ethnic backgrounds in northern Israel, with abnormal DS screening tests. E-learning tool impact was assessed as knowledge level, informed choices, satisfaction with the intervention and GC process, the state of anxiety and duration of the GC meeting. The 321 study participants were randomized to three groups: animation movie, booklet, and control. All participants had been asked to complete pre- and post-counseling questionnaires. Outcome scores were compared between the research groups. Results showed increased knowledge level in general among participants in the animation group; among minority participants, the highest knowledge level was in the animation group. Anxiety levels and informed choices were not statistically different among the groups. However, watching the animation, Jewish ethnicity, good level of genetic literacy, and academic degree were significant predictors of informed choice, and those who watched the animation were three times more likely to make an informed choice than the control group. Our findings suggest that this e-learning tool is efficient and acceptable for the general population. Special attention is needed for minorities with lower genetic literacy and education.

Artificial Intelligence-Based Tools in Software Development Processes: Application of ChatGPT

Software development processes are continuously evolving and rapidly transforming alongside the rapid changes in technology. Recently, innovations in the field of Artificial Intelligence (AI) have led to significant changes in software development practices. AI tools can greatly enhance traditional software development processes by offering developers the ability to create projects more intelligently, swiftly, and effectively. These tools can be employed in various tasks, such as code generation, test automation, error analysis, and performance improvements. Particularly, ChatGPT, an AI-based language model that has had a profound impact on almost every domain, can assist software developers in writing code faster and in a more natural language manner. In this research article, essential information about the usage of ChatGPT in the software development process is presented. To evaluate some capabilities of ChatGPT in the software development context, applications were performed on a software project. For this purpose, a software development process was constructed based on the responses provided by ChatGPT. Various questions related to software development processes were formulated, and the responses generated by GPT were evaluated. The obtained results indicated that ChatGPT exhibited excellent performance in the software development process. Based on these findings, it was observed that AI-based models like ChatGPT could be effectively utilized as assisting tools in software development processes, accelerating traditional workflows. Furthermore, AI-based tools can automate testing processes, enhancing software quality while saving time and effort.

An improved sand 3D printing method for better reproduction of high-strength and high-brittleness rock mechanical properties is proposed

3D printing is an emerging technology which can provide valuable insights into the laboratory characterization and testing of rock mechanics. The binder jetting technology (BJT) process in 3D printing is one of the most promising tools for laboratory testing of rocks but still cannot reproduce high-strength and high-brittleness rocks. In this study, quartz sand and furan resin are chosen as the printing materials, and sand 3D printed rock-like specimens are used as the study objects. Based on this, the specimens are post treated, including vacuum infiltration with phenolic resin and high-temperature baking, and then these specimens are further subjected to uniaxial compression tests and Brazilian splitting tests to study their mechanical properties. A micromechanical explanation is given for the mechanical property enhancement using scanning electron microscopy (SEM) analysis and box-counting fractal dimension. The results show that the improved sand 3D printed rock-like specimens proposed in this study can reproduce the mechanical properties of high-strength and high-brittleness rocks in terms of the stress‒strain relationship, uniaxial compressive strength, elastic modulus, tensile strength and brittleness index. From the microstructure perspective, the mechanical properties are enhanced because of the improved micro-pore structure and increased bond strength between the particles. The micro-pore structure morphology of sand 3D printed specimens shows fractal characteristics, and the micro-pore pore area and the fractal dimension are positively correlated. The findings can support, to a certain extent, the future systematic investigation of the mechanical behavior of high-strength and high-brittleness rocks in underground engineering.

Maximizing Efficiency in Automation Testing: Leveraging BrowserStack’s Advanced Features for Web and Mobile Platforms

Maximizing Efficiency in Automation Testing: Leveraging BrowserStack’s Advanced Features for Web and Mobile Platforms

3D unsupervised anomaly detection through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography

Computer-aided diagnosis for low-dose computed tomography (CT) based on deep learning has recently attracted attention as a first-line automatic testing tool because of its high accuracy and low radiation exposure. We propose a method based on a deep neural network for computer-aided diagnosis called virtual multi-view projection and reconstruction for unsupervised anomaly detection (VMPR-UAD) in low-dose chest CT. Presumably, this is the novel method that only requires data from healthy patients for training to identify three-dimensional (3D) regions containing any anomalies. The method has three key components. Unlike existing computer-aided diagnosis tools that use conventional CT slices as the network input, our method (1) improves the recognition of 3D lung structures by virtually projecting an extracted 3D lung region to obtain two-dimensional (2D) images from diverse views to serve as network inputs, (2) accommodates the input diversity gain for accurate anomaly detection, and (3) achieves 3D anomaly/disease localization through a novel 3D map restoration method using multiple 2D anomaly maps. The proposed method based on unsupervised learning showed a high performance in pneumonia, tuberculosis, and both diseases with patient-level anomaly detection performance of 0.965 area under the curve (AUC) (95% confidence interval (CI); (0.955, 0.972)), 0.948 AUC (95% CI; (0.928, 0.966)), and 0.963 AUC (95% CI; (0.955, 0.970)), respectively. Additionally, our technology visualizes anomalous regions in the 3D perspective. This achieved 93% accuracy in visualizing the location of lung cancer lesions through external validation. These results highlight the potential of a new AI methodology without utilizing disease data learning; this can secure AI model prediction stability by reducing the false negative rate that occurs in various patterns of diseases.

Real-Time ITO Layer Thickness for Solar Cells Using Deep Learning and Optical Interference Phenomena

The thickness of the indium tin oxide (ITO) layer is a critical parameter affecting the performance of solar cells. Traditional measurement methods require sample collection, leading to manufacturing interruptions and potential quality issues. In this paper, we propose a real-time, non-contact approach using deep learning and optical interference phenomena to estimate the thickness of ITO layers in solar cells. We develop a convolutional neural network (CNN) model that processes microscopic images of solar cells and predicts the ITO layer thickness. In addition, mean absolute error (MAE) and mean squared error (MSE) loss functions are combined to train the model. Experimental results demonstrate the effectiveness of our approach in accurately estimating the ITO layer thickness. The integration of computer vision and deep learning techniques provides a valuable tool for non-destructive testing and quality control in the manufacturing of solar cells. The loss of the model after training is reduced to 0.83, and the slope of the test value in the scatter plot with the true value of the ellipsometer is approximately equal to 1, indicating the high reliability of the model.

Novel and highly reliable leak check tests for drop- and external pressure-type cell-free and concentrated ascites reinfusion therapy.

For safe management of cell-free and concentrated ascites reinfusion therapy (CART), a highly reliable leak test for detecting ascites filter damage is essential. However, such a test has not been established for drop-type CART. We devised two novel leak tests for drop- and external pressure-type CART, manual or pump pressurization methods, using high-pressure loading and pressure monitoring, and investigated their reliability. Both methods could easily load and maintain sufficiently high pressure (>400 Torr) on the hollow fibers for 2 min. No result deviation was noted between different operators. The pressure drops in both methods were identical and significantly lower than those in the leak test using a special CART machine, the e-CART. The reliability of our revised leak test is equivalent to that of the automatic leak test of e-CART. These highly reliable leak tests may contribute to safety in patients undergoing drop- and external pressure-type CART.

Detecting abnormal behavior of automatic test equipment using autoencoder with event log data

The utilization of automatic test equipment (ATE) in a wafer test is crucial because it helps maintain optimal temperatures and simultaneously provides electrical power for multiple chips. Ensuring the proper functioning of ATE is essential to avoid productivity losses, such as extended wafer testing times and potential malfunctions. Although previous research has investigated the detection of ATE malfunctions using yields, probe cards, and electrical characteristic data, these approaches exhibit limitations in data characteristics and are not suitable for continuous monitoring. This study introduces a novel approach for detecting abnormal behavior within ATE by using event log data and an autoencoder. Autoencoders have demonstrated efficacy in identifying abnormal behavior, making them apt for detecting such behavior within ATE using event log data. To develop this approach, data analysts and wafer test engineers collaborated to establish a practical methodology that encapsulates the operational characteristics of ATE. This methodology comprises four distinct steps. The proposed approach was assessed using simulated event log data and was proven effective in detecting abnormal behavior within ATE. Furthermore, the approach was applied to real-world ATE data from a semiconductor company, identifying abnormal temperature control and monitoring behavior. This detection can lead to reduced wafer test times and contribute to environmental protection efforts.

A high-throughput plate method for nucleic acid extraction from beet leafhopper (Hemiptera: Cicadellidae) and potato psyllid (Hemiptera: Triozidae) for pathogen detection.

Plant pathogens that are transmitted by insect vectors cause considerable damage to crops when pests or pathogens are not detected early in the season and populations are not controlled. Knowledge of pathogen prevalence in insect pest populations can aid growers in their insect pest management decisions but requires the timely dissemination of results. This process requires that specimen capture, identification, nucleic acid extraction, and molecular detection of a pathogen(s) occur alongside a platform for sharing results. The potato psyllid (Bactericera cockerelli, Sulc; Hemiptera: Triozidae) and beet leafhopper (Circulifer tenellus, Baker; Hemiptera: Cicadellidae) transmit pathogens to potato and other vegetable or seed crops each season in the northwestern United States. While the potato psyllid has been tested for pathogen occurrence for the past decade, testing of the beet leafhopper is a new endeavor and substantially increases the specimen number that must be tested by our laboratories each season. To aid in the rapid processing of individual insect specimens, we optimized and validated a new high-throughput 96-well plate nucleic acid extraction method for use in place of a standard 1.5-ml single-tube extraction method. Processing efficiency, in terms of total specimens processed over a 2-day period, improved 2.5-fold, and the cost associated with processing a single sample was nearly cut in half with this newly developed plate nucleic acid extraction method. Overall, this method has proven to be an excellent tool for the rapid testing of large numbers of small, individual insect vectors to enable timely dissemination of data on pathogen prevalence to growers.

An empirical study to compare three web test automation approaches: NLP‐based, programmable, and capture&replay

AbstractA new advancement in test automation is the use of natural language processing (NLP) to generate test cases (or test scripts) from natural language text. NLP is innovative in this context and promises of reducing test cases creation time and simplifying understanding for “non‐developer” software testers as well. Recently, many vendors have launched on the market many proposals of NLP‐based tools and testing frameworks but their superiority has never been empirically validated. This paper investigates the adoption of NLP‐based test automation in the web context with a series of case studies conducted to compare the costs of the NLP testing approach—measured in terms of test cases development and test cases evolution—with respect to more consolidated approaches, that is, programmable (or script‐based) testing and capture&replay testing. The results of our study show that NLP‐based test automation appears to be competitive for small‐ to medium‐sized test suites such as those considered in our empirical study. It minimizes the total cumulative cost (development and evolution) and does not require software testers with programming skills.

Valorization of food waste for biogas production; effect of co-ensiling with maize straw at different C/N ratios

Food waste (FW) contribute to emission of greenhouse gases as well as environmental pollution. One way of reducing the impact of FW on the environment is by using it for biofuel production. The yield of biofuel from FW can be improved if the substrate is pretreated. In this study, ensiling was used as a cost effective method of pretreating FW for biomethane production. Co-ensiling of FW and maize straw (MS) was carried out at carbon to nitrogen ratios (C/N) of 20, 25, 30 and 35 for 210 days at ambient temperature. Thereafter, the biomethane potential (BMP) of the silages was determined using an automatic biomethane potential test system. Results showed that ensiling reduced both structural and non-structural carbohydrate components of the silages. BMPs of 385.58, 497.39, 520.53, 551.37, 542.16 and 517.29mL/gVS from the unensiled FW, ensiled FW without MS, co-ensiled FW at C/N ratio of 20, co-ensiled FW at C/N ratio of 25, co-ensiled FW at C/N ratio of 30 and co-ensiled FW at C/N ratio of 35 respectively were obtained. A kinetic evaluation showed that the dual pool model gave a better prediction of the experimental BMP of all substrates than the first-order model and the modified Gompertz model.

The challenge of making the right choice: patient avatars in the era of cancer immunotherapies.

Immunotherapies are a key therapeutic strategy to fight cancer. Diverse approaches are used to activate tumor-directed immunity and to overcome tumor immune escape. The dynamic interplay between tumor cells and their tumor(immune)microenvironment (T(I)ME) poses a major challenge to create appropriate model systems. However, those model systems are needed to gain novel insights into tumor (immune) biology and a prerequisite to accurately develop and test immunotherapeutic approaches which can be successfully translated into clinical application. Several model systems have been established and advanced into so-called patient avatars to mimic the patient´s tumor biology. All models have their advantages but also disadvantages underscoring the necessity to pay attention in defining the rationale and requirements for which the patient avatar will be used. Here, we briefly outline the current state of tumor model systems used for tumor (immune)biological analysis as well as evaluation of immunotherapeutic agents. Finally, we provide a recommendation for further development to make patient avatars a complementary tool for testing and predicting immunotherapeutic strategies for personalization of tumor therapies.