Automatic Test Research Articles

Subjective measurement of resolution of image intensifier tubes

Limiting resolution is traditionally defined as a spatial frequency of smallest resolvable element of a resolution target that can be distinguish by a human observer. This definition and measurement method have been criticized in a series of literature sources due to its subjectivity and low repeatability/accuracy. Such criticism looks reasonable as it is commonly known that results of measurement of resolution of the same tubes carried out by several test teams can differ significantly. This paper presents a detail analysis of classical subjective measurement method of limiting resolution of image intensifier tubes implemented by real test systems. The findings show that in spite of a common opinion, subjectivity of measurement is not the main reason for differences of test results carried out by different test teams. The main reasons are differences in performance of optics of test systems, use of resolution targets of different types and polarity, and inherent spatial non-uniformity of performance of image intensifier tubes. The paper also shows that due to spatial non-uniformity it is very difficult to design automatic test systems that could produce the same results as human observers using classical subjective measurement method.

Design of Automatic Test System of the T/R Component Based on LABVIEW

Design of Automatic Test System of the T/R Component Based on LABVIEW

Exploring Multiple-Objective Optimization for Efficient and Effective Test Paper Design with Dynamic Programming Guided Genetic Algorithm.

Automatic test paper design is critical in education to reduce workloads for educators and facilitate an efficient teaching process. However, current designs fail to satisfy the realistic teaching requirements of educators, including the consideration of both test quality and efficiency. This is the main reason why teachers still manually construct tests in most teaching environments. In this paper, the quality of tests is quantitatively defined while considering multiple objectives, including a flexible coverage of knowledge points, cognitive levels, and question difficulty. Then, a model based on the technique of linear programming is delicately designed to explore the optimal results for this newly defined problem. However, this technique is not efficient enough, which cannot obtain results in polynomial time. With the consideration of both test quality and generation efficiency, this paper proposes a genetic algorithm (GA) based method, named dynamic programming guided genetic algorithm with adaptive selection (DPGA-AS). In this method, a dynamic programming method is proposed in the population initialization part to improve the efficiency of the genetic algorithm. An adaptive selection method for the GA is designed to avoid prematurely falling into the local optimal for better test quality. The question bank used in our experiments is assembled based on college-level calculus questions from well-known textbooks. The experimental results show that the proposed techniques can construct test papers with both high effectiveness and efficiency. The computation time of the test assembly problem is reduced from 3 hours to 2 seconds for a 5000-size question bank as compared to a linear programming model with similar test quality. The test quality of the proposed method is better than the other baselines.

Research on Artificial Intelligence-Assisted Software Test Automation Methods

Abstract Software testing faces problems such as low automation and difficult reuse of testing methods. The purpose of this paper is to explore software testing automation methods with the aid of artificial intelligence. In this paper, based on the BS algorithm, the RF algorithm is constructed by Bagging integration, the RF algorithm is optimized by reducing the generalization error through the residual function, and the random forest model for software automation detection is constructed. After that, the model is examined and analyzed using automated detection of malicious software samples as a case study. The experimental results show that the accuracy of the Random Forest algorithm after feature selection reaches 98.9%, its prediction time is the least (7 seconds), and the Random Forest algorithm for training is the best. Z software’s optimized RF algorithm model has an accuracy of between 86% and 99.3% when detecting seven malicious types of samples. This paper’s proposed random forest algorithm model based on artificial intelligence assistance is well-suited for automated software testing, and the detection method is feasible.

Graphic analysis of various sulfur applications on safflower fatty acids profile.

In this study, we examined the effects of seven different sulfur treatments on safflower seeds. The treatments included: no sulfur application (S0), 25 kg/ha of pure bulk sulfur (S25), 50 kg/ha of pure bulk sulfur (S50), 25 kg/ha of sulfur phosphate (Sp25), 50 kg/ha of sulfur phosphate (Sp50), 25 kg/ha of zinc sulfate (Zs25), and 50 kg/ha of zinc sulfate (Zs50). Our evaluation covered various seed quality attributes, including ash percentage (ASH), oil percentage (OIL), and protein percentage (PRO). Additionally, we analyzed the fatty acid composition, including palmitic acid 16 : 0 (PAL), stearic acid 18 : 0 (STE), oleic acid 18 : 1 (OLE), linoleic acid 18 : 2 (LINL), arachidic acid 20 : 0 (ARA), and linolenic acid 18 : 3 (LINN). The vector-view of the biplot illustrated positive associations among the fatty acids STE, PAL, and OLE, whereas ASH exhibited negative associations with OIL, LINL, and LINN. The polygon-view graph was divided into four sectors, with the genotype S50 emerging as the top performer for attributes such as OIL, PRO, LINL, ARA, and LINN. Treatment Zs50 occupied the vertex of another sector and displayed the highest values for palmitic acid PAL, STE, and OLE, while treatment S0 was positioned at the vertex of the next sector, characterized by its high ASH content. By utilizing the ideal tester tool of treatment by trait biplot, we identified OIL as the desirable trait that most effectively represented the data. The qualitative properties of safflower oil were notably influenced by sulfur application, with treatment S50 proving to be the most effective in enhancing these properties.

Implementasi RESTful API Pada Laravel dan Simulator IoT Wokwi Untuk Pengukuran Suhu dan Kelembaban Menggunakan Metode Waterfall

This research discusses the implementation of RESTful API in the Laravel web development framework, along with the utilization of the IoT Wokwi simulator for monitoring temperature and humidity. The application of the waterfall development method is employed as an approach to organize the development process with clear steps in creating the temperature and humidity measurement program. The aim of this research is to create an efficient and well-structured application program interface using Laravel, while simultaneously leveraging the IoT Wokwi simulator as a tool for testing and validating the functionality of temperature and humidity measurement. The research methodology involves the use of the software development life cycle (SDLC) model, specifically the waterfall model, as an approach to the software development life cycle. The anticipated outcome of this research is to provide practical guidance for developers in creating an effectively integrated temperature and humidity measurement system using web technology and IoT.

Comprehensive Exploration of Web Application Security Testing with Burp Suite Tools

This research paper investigates the pivotal role of Burp Suite tools in web application security testing, aligned with the foundational principles of secure coding practices outlined by the Open Web Application Security Project (OWASP). The paper provides an in-depth overview of Burp Suite tools, emphasizing their features and the significance of web application vulnerability scanning in enforcing robust security practices. The exploration includes a detailed examination of Burp Repeater and Burp Intruder, with a practical demonstration of a Burp Suite cluster bomb attack. This research contributes to advancing the understanding and application of Burp Suite tools for effective web application security testing within the framework of OWASP secure coding practices.

Performance analysis of React v. 18.1.0 and Angular v. 11.0.2 development frameworks

This paper presents the results of the comparative analysis of the two JavaScript frameworks. The research was carried out using test applications with the same functionalities, implemented in both environments. The number of bytes occupied by both projects, the number of lines of code, the average RAM consumption and the time efficiency were used as criteria for comparison. Automatic load tests and technical analysis of both environments were performed. The results showed that React is able to better handle a large amount of data (around 100 000 records), while for small data sets oscillating around 1000 records, no significant differences in both application performance were demonstrated. For a medium number of records (10000), React proved to be more efficient.

Magnetic particle inspection: Status, advances, and challenges — Demands for automatic non-destructive testing

Magnetic particle inspection (MPI) is a highly sensitive and user-friendly nondestructive technique that remains essential for detecting surface and near-surface defects in ferromagnetic materials. Extensive research and industry applications have been conducted to verify its effectiveness. However, it is crucial to discuss the status of magnetic particle inspection, especially in light of advancements in image processing technology and the emergence of the concept of non-destructive testing 4.0. This review aims to explore automated magnetic particle inspection, focusing specifically on two key aspects: the factors influencing the formation of magnetic particle indications and the automatic recognition of these indications. The paper provides a comprehensive overview of current research, encompassing important aspects such as magnetic flux leakage, magnetic particles, image processing techniques, inspection devices and equipments. The challenges in implementing automatic magnetic particle inspection systems are also highlighted, such as the need for signal interpretation and noise reduction, reliable defect detection algorithms, system integration and real-time recognition. The paper concludes by summarizing the key findings and identifying future research directions in automatic non-destructive testing for magnetic particle inspection.

Intelligent chemiluminescent system for wash-free and ultrasensitive assay of aflatoxin B1 in coix seed and decoction pieces

Aflatoxin B1 (AFB1) is one of the most harmful mycotoxins. Convenient and reliable determination of trace AFB1 is highly desired. Herein, dual hemin labeled DNA hairpin was designed as a chemiluminescence (CL) switch and combined with aptamer of AFB1 and its partial complementary strand for homogenous assay of AFB1. Due to the judicious design of three DNA sequences and the help of nicking enzyme, the AFB1 recognition, signal transduction and recycling amplification can be implemented during one-step reaction. The whole protocol only involved the simple mixing of samples with homogeneous solution, avoiding the preparation of sophisticated solid-phase sensor and eliminating the multi-step washing and resulting errors, which provided a unique tool for on-site rapid testing. Importantly, when the operation process was simplified, the detection sensitivity can be enhanced facilely due to the enzyme recycling. Under optimal conditions, the intelligent CL system for sensing AFB1 exhibited a wide dynamic range (1.0 × 10-2 ∼ 100 ng mL−1) with a low detection limit (down to 1.5 pg mL−1), suitable for detecting trace AFB1 in complex matrix. The methodology was validated in coix seed and decoction pieces. The superiority including free of washing and separation, high sensitivity, acceptable accuracy, and greenness, showed good applicability of the proposed CL system in safety evaluation of food and medicine.

Optimizing the sodium iodate model: Effects of dose, gender, and age

Sodium iodate (NaIO3) is a commonly used model for age-related macular degeneration (AMD), but its rapid and severe induction of retinal pigment epithelial (RPE) and photoreceptor degeneration can lead to the premature dismissal of potentially effective therapeutics. Additionally, little is known about how sex and age affect the retinal response to NaIO3. This study aims to establish a less severe yet reproducible regimen by testing low doses of NaIO3 while considering age- and sex-related effects, enabling a broader range of therapeutic evaluations. In this study, young (3–5 months) and old (18–24 months) male and female C57Bl/6J mice were given an intraperitoneal (IP) injection of 15, 20, or 25 mg/kg NaIO3. Damage assessment one week post-injection included in vivo imaging, histological examination, and qRT-PCR analysis. The results revealed that young mice showed no damage at 15 mg/kg IP NaIO3, with varying degrees of damage observed at 20 mg/kg. At 25 mg/kg, most young mice displayed widespread retinal damage, with females exhibiting less retinal thinning than males. In contrast, older mice at 20 and 25 mg/kg displayed a more patchy degeneration pattern, outer retinal undulations, and greater variability in degeneration than the young mice. The most effective model for minimizing damage while maintaining consistency utilizes young female mice injected with 25 mg/kg NaIO3. The observed sex- and age-related differences underscore the importance of considering these variables in research, aligning with the National Institutes of Health's guidance. While the model does not fully replicate the complexity of AMD, these findings enhance its utility as a valuable tool for testing RPE/photoreceptor protective or replacement therapies.

МАТЕМАТИЧЕСКАЯ МОДЕЛЬ ТЕХНОЛОГИЧЕСКОГО УЧАСТКА МАГИСТРАЛЬНОГО ТРУБОПРОВОДА С РЕГУЛИРОВАНИЕМ ДРОССЕЛИРОВАНИЕМ

The aim of the work is to develop a mathematical model of the technological section of the main pipeline, which reflects the main hydraulic processes in the pipeline, namely Q-H characteristics of pressure and main pumping units, their connection diagrams, characteristics of the electric drive part, characteristics of the linear part (length, diameter, thickness of the pipeline ), shut-off linear valves, throttling valves, including a frequency converter, an electric motor with a gearbox and a hydraulic part (flow characteristic). The task is to develop a mathematical model of the main pipeline technological section for further use as part of a simulation stand for an automatic pressure control system (APCS). Research methods are used such as the system analysis method, technical hydraulics, mathematical modelling of operating the main oil pipeline section, considering the switch on and off of the main pumping units and changes in the opening degree of the throttling unit. The research results state that the developed mathematical model of the main pipeline technological section takes into account the basic hydraulic processes and can be used in the simulator software for automatic pressure control systems. The novelty of the work consists in building a mathematical model of the main pipeline technological section with throttling control, adapted for real-time operation for the simulator programmable logic controller. The paper findings state that the proposed mathematical model of the main pipeline technological section with throttling control has some assumptions necessary for the software to operate in real time and reflects the main hydraulic processes occurring in the oil pipeline. Switching the main pumping units and control valves is successfully modelled. The simulation results can be used in the computer program for the software and hardware complex (SHC) of the automation testing simulator and the SHC for setting up the APCS.

Teaching and methodological manual as an effective preparation tool for arabic language testing (level A1-A2)

Учебно-методические пособия, разработанные преподавателями кафедры арабской филологии ИСАА МГУ имени М.В. Ломоносова, являются эффективным инструментом диагностики и систематизации знаний по арабской грамматике в ее базовой части, а также проверки и развития практических навыков говорения и аудирования в пределах определенных тем в объеме, необходимом для тестирования по арабскому языку (базовыйуровень)

The Development and Application of MTPA Calibration Method Based on INCA FLOW and MATLAB

Article The Development and Application of MTPA Calibration Method Based on INCA FLOW and MATLAB Zhijun Liu 1,2, Renyi Huang 1,*, Xiaoyan Zhuo 1, Kailiang Chen 1, and Guanglan Li 1 1 Liuzhou Saike Technology Development Co., Ltd., Liuzhou 545005, China 2 Liuzhou Key Laboratory of Electrified Powertrain Electronic Control, Liuzhou 545005, China * Correspondence: renyi.huang@foxmail.com Received: 20 July 2023 Accepted: 6 November 2023 Published: 28 November 2023 Abstract: In industrial applications, the maximum torque per ampere (MTPA) control algorithm typically employs an offline look-up table (LUT) method instead of an online MTPA control method to reduce the online computational resources. However, the LUT method has some drawbacks, such as heavy testing workload, longer cycles, and cumbersome data processing, significantly diminishing the efficiency of MTPA data acquisition. To address the limitations of the LUT method, this study proposes an automatic MTPA calibration method combining INCA FLOW and MATLAB. The calibration efficiency and data accuracy are improved by utilizing INCA FLOW for automatic MTPA testing and MATLAB for data processing. An experimental test bench was constructed to evaluate the proposed method. The experimental results demonstrate that this method efficiently performs MTPA data testing and visualizes the results. Compared with the traditional manual MTPA calibration method, this method achieves a 320% improvement in calibration efficiency. The main technical contribution of this study is the introduction of the automatic MTPA calibration method that not only optimizes data accuracy but also enhances calibration efficiency.

High-Throughput Monitoring of Pathogenic Fungal Growth Using Whole Slide Imaging for Rapid Antifungal Susceptibility Assessment

Invasive fungal infections are a major health threat with high morbidity and mortality, highlighting the urgent need for rapid diagnostic tools to detect antifungal resistance. Traditional culture-based antifungal susceptibility testing (AFST) methods often fall short due to their lengthy process. In our previous research, we developed a whole-slide imaging (WSI) technique for the high-throughput assessment of bacterial antibiotic resistance. Building on this foundation, this study expands the application of WSI by adapting it for rapid AFST through high-throughput monitoring of the growth of hundreds of individual fungi. Due to the distinct “budding” growth patterns of fungi, we developed a unique approach that utilizes specific cell number change to determine fungi replication, instead of cell area change used for bacteria in our previous study, to accurately determine the growth rates of individual fungal cells. This method not only accelerates the determination of antifungal resistance by directly observing individual fungal cell growth, but also yields accurate results. Employing Candida albicans as a representative model organism, reliable minimum inhibitory concentration (MIC) of fluconazole inhibiting 100% cells of Candida albicans (denoted as MIC100) was obtained within 3h using the developed method, while the modified broth dilution method required 72h for the similar reliable result. In addition, our approach was effectively utilized to test blood culture samples directly, eliminating the need to separate the fungi from whole blood samples spiked with Candida albicans. These features indicate the developed method holds great potential serving as a general tool in rapid antifungal susceptibility testing and MIC determination.

Exposure to immersive virtual environments decreases present bias

In a Cave Automatic Virtual Environment, we conducted an experiment to investigate the influence of future self-continuity on the delay of immediate monetary gratification. By utilizing immersive virtual environments, we aimed to accentuate the role of environmental cues in shaping behavior. Our findings revealed that these environmental stimuli had a significant impact, leading to a higher likelihood of selecting a larger delayed option. This effect was attributed to the enhanced desirability of future gratification and a reduction in the influence of present bias on future rewards. These results underscore the utility of virtual environments as a valuable tool for both testing and elucidating intertemporal choice behavior.

Development of a CNN-based fault detection system for a real water injection centrifugal pump

Large-sized centrifugal pumps play a major role in produced water injection systems in oil and gas production. Monitoring this equipment operation is vital to guarantee its efficiency and to reduce the occurrence of unplanned downtimes. The main goal of this work was to develop a fault detection system based on artificial intelligence (AI) algorithms for a water injection centrifugal pump located at an oil and gas company’s offshore platform. The convolutional neural network (CNN) was the main algorithm investigated in this work. However, other machine learning techniques (i.e., support vector machines, random forest, and multilayer perceptrons) were used to compare against the results achieved by the CNN. A data-driven methodology was proposed for the stages of exploratory data analysis (EDA), data labeling, automatic hyperparameter optimization, training and testing of the chosen models. The results showed that the proposed methodology was effective and applicable. The CNN and the support vector machine (SVM) models presented interesting performances. The CNN model returned 93.7% precision and 59.8% recall, whereas the SVM model returned 84.6% precision and 66.7% recall. The challenges related to the use of a real dataset were also discussed, emphasizing the data labeling step. Applying the k-means clustering technique proved to be useful for labeling the data instances.

Improving Coverage and Vulnerability Detection in Smart Contract Testing Using Self-Adaptive Learning GA

In the domain of software testing, the generation of test cases is a critical process for detecting system errors and bugs. However, automated test case generation for smart contracts often encounters challenges related to automation, vulnerability diversity, and coverage. This paper presents a novel method, the self-adaptive learning Genetic Algorithm (self-adaptive learning GA), designed to address these issues. Our research methodology incorporates several construction models, namely the Control Dependence Graph (CDG), Control Flow Graph (CFG), and Application Binary Interface (ABI). Initially, the ABI model provides essential information for generating and executing test cases. The CFG model subsequently visualizes potential execution paths through the functions of smart contracts. Ultimately, the CDG model identifies potential vulnerabilities in smart contracts. Using these models, our method enhances automatic test case generation in smart contracts by improving coverage and reducing execution time. We selected a variety of smart contracts from the Decentralized Finance (DeFi) ecosystem for data collection and comparative analysis. The experimental results show superior performance rates, with an average code coverage rate of 98.1%, a total of 3500 vulnerabilities detected, a vulnerability detection rate of 98.7%, a false positive rate of 1.3%, a recall of 98.2%, precision of 98.8%, a path uniqueness rate of 96.4%, false negative rate of 3.5%, an execution time of 25 s, and test case generation time of 16 s. In conclusion, our proposed approach demonstrates a significant improvement over existing methods for test case generation by providing a promising solution for the robustness of smart contracts and security enhancement in the DeFi ecosystem.

Remote Inclusion of Vulnerable Users in mHealth Intervention Design: Retrospective Case Analysis.

Mobile health (mHealth) interventions that promote healthy behaviors or mindsets are a promising avenue to reach vulnerable or at-risk groups. In designing such mHealth interventions, authentic representation of intended participants is essential. The COVID-19 pandemic served as a catalyst for innovation in remote user-centered research methods. The capability of such research methods to effectively engage with vulnerable participants requires inquiry into practice to determine the suitability and appropriateness of these methods. In this study, we aimed to explore opportunities and considerations that emerged from involving vulnerable user groups remotely when designing mHealth interventions. Implications and recommendations are presented for researchers and practitioners conducting remote user-centered research with vulnerable populations. Remote user-centered research practices from 2 projects involving vulnerable populations in Norway and Australia were examined retrospectively using visual mapping and a reflection-on-action approach. The projects engaged low-income and unemployed groups during the COVID-19 pandemic in user-based evaluation and testing of interactive, web-based mHealth interventions. Opportunities and considerations were identified as (1) reduced barriers to research inclusion; (2) digital literacy transition; (3) contextualized insights: a window into people's lives; (4) seamless enactment of roles; and (5) increased flexibility for researchers and participants. Our findings support the capability and suitability of remote user methods to engage with users from vulnerable groups. Remote methods facilitate recruitment, ease the burden of research participation, level out power imbalances, and provide a rich and relevant environment for user-centered evaluation of mHealth interventions. There is a potential for a much more agile research practice. Future research should consider the privacy impacts of increased access to participants' environment via webcams and screen share and how technology mediates participants' action in terms of privacy. The development of support procedures and tools for remote testing of mHealth apps with user participants will be crucial to capitalize on efficiency gains and better protect participants' privacy.

Test Bench Solution for ASIC Inspection

"Application Specific Integrated Circuits (ASICs) have been, for a long time, the best, and sometimes only, option when there was a need for high performance electronics while operating with tight constraints regarding communication bandwidth, speed and/or latency, low power consumption and other, more specialized requirements, such as radiation hardness. To make sure that all ASICs that are going to be used in electronics parts installed on experimental physics detectors are working properly, they need to be thoroughly tested and their testing results need to be stored and used for manufacturing traceability purposes. A test bench solution which allows identifying and eliminating sources of errors during the integration of ASICs in detector electronics based on machine vision and test automation procedures has been designed for this purpose and is being presented."