Automatic Test Research Articles

Societal Impact of Test Automation: Reducing Human Error in Critical Systems

This article explores the profound societal impact of test automation across critical sectors such as healthcare, finance, transportation, and energy. It examines how automated testing processes significantly reduce human error, enhance system reliability, and improve service quality. The article presents compelling evidence from various studies and reports, demonstrating substantial improvements in medical diagnostics, financial fraud detection, transportation safety, and energy distribution efficiency. Beyond error reduction, the article discusses broader societal benefits, including enhanced accuracy in data processing, faster emergency response times, improved service quality, and strategic resource allocation. The article underscores the crucial role of test automation in addressing the challenges posed by increasingly complex technological systems and its far-reaching implications for public safety, economic stability, and overall quality of life.

Inverse modeling of electromagnetic emissions using grey wolf optimization

The paper proposes an inverse modeling approach for evaluating radiated electromagnetic compatibility (EMC) in electronic components and circuits. To imitate the electromagnetic behavior of real devices, the suggested methodology generates an equivalent model using elementary dipoles. The Grey Wolf Optimization (GWO) algorithm solves an inverse problem to identify dipole parameters such as locations, intensities, and orientations. This technique improves model accuracy and robustness through a two-phase exploration and exploitation process that allows for effective parameter tuning while reducing simulation time and computational resources. Recent research in near-field measurement techniques illustrates the importance of reliable predictive methodologies in EMC evaluation. This method provides significant information on the impacts of power systems on surrounding electronic circuits, making it a useful tool for EMC testing. It also promotes the development of immunity models for high-frequency applications. Overall, this methodology simplifies the examination procedure, resulting in higher design efficiency for a wide range of electronic systems.

Static and dynamic pupillary features in graves disease without orbitopathy.

Pupillary contraction and dilatation are organized by the autonomic nervous system, which can also be affected by Graves' disease (GD). In this study, it was aimed to investigate the static and dynamic pupillary responses of Graves' patients in the hyperthyroid and euthyroid periods and to compare these results with the values obtained from healthy controls. 48 eyes of 24 newly diagnosed Graves' patients with clinical activity score ≤ 2 and 46 eyes of 23 age- and sex-matched healthy controls were included in the study. Patients with GD were evaluated separately in the hyperthyroid phase and after euthyroidism was achieved. After a detailed ophthalmological examination, patients undergone automatic quantitative testing to obtain static (scotopic, mesopic, low photopic, and high photopeak pupil diameters) and dynamic (pupillary contraction amplitude, latency, velocity and duration, and pupil dilation latency, velocity and duration) pupil measurements. A statistically significant difference was found between the control [4.7mm (3.7-6.9)] and hyperthyroid [5.1mm (3.6-7.8)] groups for mesopic pupil diameter (p = 0.003). Dynamic pupillometry measurements showed that hyperthyroid and euthyroid groups had greater pupil dilation delay and lower pupil dilation rate compared to the control group (p < 0.05 for all). Even in the absence of orbitopathy, patients with GD have autonomic dysregulation of pupillary functions. These changes, which prevail in the hyperthyroid phase, do not reverse when the TSH receptor antibody titer drops and the patient becomes euthyroid. Future studies are needed to elucidate the mechanism of these pupillary changes in GD.

Adaptation of the Five-by-Five Resilience Scale in Indonesian

Resilience is the ability of individuals to be able to adapt and rise from difficult conditions or situations. The Five-by-Five Resilience Scale is a measurement tool developed by DeSimone et al (2016) to measure individual resilience. There are five protective factors of resilience, namely adaptation, emotional regulation, optimism, self-efficacy and social support. This research aims to adapt the Five-by-Five Resilience Scale into Indonesian. This research was conducted because there has been no testing of the Indonesian version of the Five-by-Five Resilience Scale. The subjects involved in the study were 267 subjects with an age range of 18 - 25 years who were domiciled in Indonesia. Based on the results of psychometric testing through Exploratory Factor Analysis (EFA), 4 factors were obtained that build the Five-by-Five Resilience Scale. The results also show that the Five-by-Five Resilience Scale has a correlation with other measuring tools in convergence validity testing. The reliability test results show that the dimensions on the Five-by-Five Resilience Scale as a whole have a reliability &gt; 0.7.

The Application of PID Control in DC Motor Control Systems

Abstract. The application of Proportional-Integral-Derivative (PID) control in direct current (DC) motor control systems is essential for achieving high levels of precision and reliability in both industrial and robotic applications. As DC motors are widely utilized in environments requiring accurate speed and torque control, this study seeks to address common challenges such as overshoot, instability, and sluggish response times that can compromise performance. The research is centered on the development and implementation of a PID-based control strategy using MATLAB/Simulink as the primary tool for simulation and testing. The study involves detailed simulation models and experimental validation, using data collected from controlled lab environments to evaluate the effectiveness of various tuning methods. These methods include adjusting the proportional, integral, and derivative gains to optimize motor responsiveness and system stability. The results indicate that with appropriate tuning, PID controllers can significantly enhance DC motor performance, leading to reduced overshoot, faster response times, and greater overall system reliability. The research concludes that adaptive PID control strategies, which dynamically adjust parameters in response to changing operating conditions, provide a robust solution for improving DC motor control in complex, dynamic, and nonlinear environments. This study lays the groundwork for future innovations in automation and precision-driven industries, offering insights that could be applied across a wide range of applications.

Quantile analysis for financial bubble detection and surveillance

Understanding and monitoring financial bubbles is critical, as they can lead to market instability, asset price crashes, and economic downturns with widespread consequences. This article explores the usefulness of quantile regression (QR) technique in detecting and surveilling financial bubbles, encompassing both global testing and real‐time monitoring. We demonstrate that the QR‐based quantile unit root test, coupled with an optimal quantile selection technique, serves as an effective tool for a global bubble test without necessitating additional recursive techniques. Moreover, we propose two QR‐based bubble monitoring techniques. We show that the monitoring statistics follow a random variate under the null hypothesis of no bubbles but diverge to positive infinity in the presence of a mildly explosive bubble, and hence consistently date the origination of a bubble. Monte Carlo simulations suggest that compared with their LS counterparts, in the presence of skewed distributions, the QR‐based global test delivers substantially greater power, while the QR‐based monitoring procedures offer higher bubble detection rate and more accurate dating of the bubble origination. As an illustration, we conduct a pseudo real‐time monitoring exercise with the S&amp;P 500 composite index.

Robotized Mobile Platform for Non-Destructive Inspection of Aircraft Structures

The robotization of the non-destructive inspection of aircraft is essential for improving the accuracy and duration of performed inspections, being an integral part of inspection and data management systems within the currently developed NDT 4.0 concept. In this paper, the authors presented the design and testing of a universal mobile platform with interchangeable sensing systems for the non-destructive inspection of aircraft structures with various angles of inclination. As a result of the performed studies, a low-cost approach of automation of existing measurement devices used for inspection was proposed. The constructed prototype of the mobile platform was equipped with eddy current testing probe and successfully passed both laboratory and environmental tests, demonstrating its performance in various conditions. The presented approach confirms the effectiveness of the automation of the inspection process using climbing robots and defining the directions of possible development of automation in non-destructive testing in aviation.

Impaired cognitive flexibility in schizophrenia: A systematic review of behavioral and neurobiological findings

Background and hypothesisImpaired cognitive flexibility in schizophrenia (SZ) is well documented and correlation with worse functional outcome indicates clinical relevance. Paradigms that assess cognitive flexibility include the Wisconsin Card Sorting Test (WCST) and the Cambridge Neuropsychological Test Automated Battery’s (CANTAB) Intra-Extra Dimensional Set Shift (IED). This systematic review provides an overview of the current state of research on cognitive flexibility in schizophrenia and points out relevant areas of non-consensus. MethodsTwo electronic databases (Embase and PubMed) were searched for records published from 1993 to 2024 on adult SZ patients that were assessed for cognitive flexibility/set-shifting ability using WCST and/or IED. Results38 studies were included in the review, most of which reported significantly worse performance of SZ patients in WCST and/or IED compared to healthy controls (HC). Most publications focused on the specific profile of cognitive inflexibility. Other aspects included progression of cognitive inflexibility over the course of the illness, neurobiological correlates, IQ as a possible confounder and whether cognitive inflexibility is a heritable trait. ConclusionIncluded studies show that cognitive inflexibility rather reflects a stable trait than a state, indicating a lasting prefrontal impairment in SZ. Further longitudinal studies are needed to clarify how these deficits evolve during progression of the disorder. Neither antipsychotic medication nor intelligence seem to explain impaired cognitive flexibility. However, a disease-specific cognitive phenotype has not yet been established and additional research on neuro-computational mechanisms is thus needed to identify possible targets for interventional studies.

Towards Reliable ECG Analysis: Addressing Validation Gaps in the Electrocardiographic R-Peak Detection

Electrocardiographic (ECG) R-peak detection is essential for every sensor-based cardiovascular health monitoring system. To validate R-peak detectors, comparing the predicted results with reference annotations is crucial. This comparison is typically performed using tools provided by the waveform database (WFDB) or custom methods. However, many studies fail to provide detailed information on the validation process. The literature also highlights inconsistencies in reporting window size, a crucial parameter used to compare predictions with expert annotations to distinguish false peaks from the true R-peak. Additionally, there is also a need for uniformity in reporting the total number of beats for individual or collective records of the widely used MIT-BIH arrhythmia database. Thus, we aim to review validation methods of various R-peak detection methodologies before their implementation in real time. This review discusses the impact of non-beat annotations when using a custom validation method, allowable window tolerance, the effects of window size deviations, and implications of varying numbers of beats and skipping segments on ECG testing, providing a comprehensive guide for researchers. Addressing these validation gaps is critical as they can significantly affect validatory outcomes. Finally, the conclusion section proposes a structured concept as a future approach, a guide to integrate WFDB R-peak validation tools for testing any QRS annotated ECG database. Overall, this review underscores the importance of complete transparency in reporting testing procedures, which prevents misleading assessments of R-peak detection algorithms and enables fair methodological comparison.

Correlation of factor XIII subunit A with factor XIII activity in a population of parturient women.

The role of factor XIII in acute bleeding situations is gaining more and more importance. It has previously been shown that prepartum factor XIII activity has a significant impact on postpartum blood loss. Whether factor XIII antigen behaves in a similar manner is unknown. As postpartum hemorrhage is one of the leading causes of maternal morbidity and mortality worldwide and factor XIII antigen determination might be available more readily in some centers as compared to factor XIII activity, this is an important question to answer, especially in the emergency situation of a postpartum hemorrhage. To assess the correlation of prepartum factor XIII antigen with prepartum factor XIII activity and to evaluate the correlation between prepartum factor XIII antigen on measured postpartum blood loss. This is a secondary analysis of a prospective cohort study which analyzed the impact of prepartum blood coagulation factor XIII activity on postpartum blood loss in 1300 women at the University Hospital Zurich, Switzerland between October 2015 and November 2016 ("PPH-1300 study"). Blood loss was quantified using a previously validated technique. The association of factor XIII activity and factor XIII antigen was assessed by means of a Spearman rank correlation and differences were displayed using Bland-Altman plot and Passing-Bablok regression. The effect of the prepartum factor XIII antigen on blood loss was estimated by continuous outcome logistic regression. Prepartum factor XIII activity significantly correlated with prepartum factor XIII antigen (Spearman rank correlation coefficient for prepartum values 0.89, p < 0.001 and postpartum values 0.902, p < 0.001). Elevated values of prepartum factor XIII antigen showed a trend toward lower measured postpartum blood loss. The correlation of factor XIII activity with factor XIII antigen (subunit A) in a large real-world sample as well as an association of prepartum factor XIII antigen and postpartum blood loss is observed. Factor XIII antigen determination, a highly automatable test, could be useful in emergency situations such as a PPH (as well as other bleeding situations) if the determination of factor XIII activity is not possible. To evaluate whether FXIII replenishment reduces blood loss is the focus of ongoing studies.

An Automated Penetration Testing Framework Based on Hierarchical Reinforcement Learning

Given the large action space and state space involved in penetration testing, reinforcement learning is widely applied to enhance testing efficiency. This paper proposes an automatic penetration testing scheme based on hierarchical reinforcement learning to reduce both action space and state space. The scheme includes a network intelligence responsible for specifying the penetration host and a host intelligence designated to perform penetration testing on the selected host. Specifically, within the network intelligence, an action-masking mechanism is adopted to shield unenabled actions, thereby reducing the explorable action space and improving the penetration testing efficiency. Additionally, the host intelligence employs an invalid discrimination mechanism, terminating testing after actions that do not alter system states, thereby preventing sudden increases in the number of neural network training steps for an action. An optimistic estimation mechanism is also introduced to select penetration strategies suited to various hosts, preventing training crashes due to value confusion between different hosts. Ablation experiments demonstrate that the host intelligence can learn different penetration strategies for varying penetration depths without significant fluctuations in training steps, and the network intelligence can coordinate with the host intelligence to perform network penetration steadily. This hierarchical reinforcement learning framework can detect network vulnerabilities more quickly and accurately, significantly reducing the cost of security policy updates.

Implementing and Evaluating the Timed Up and Go Test Automation Using Smartphones and Smartwatches.

Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.

High-Sensitivity Detection of Copper Ions in Water via Cellulose Nanomaterial Nano-Antennas and DFT studies

The widespread occurrence of copper in water presents serious health hazards, requiring improvement of a method to monitor and remove copper ions in the field. In this study, a fresh approach is introduced that incorporates Cellulose Nanomaterials (CNMs) with a unique rod-like structure, offering a large surface area that is perfect for adsorbing cupral, a Cu(II) ion probe. By using this arrangement, toxic copper can be rapidly and visibly detected. Without any special tools, it's easy to see the color change from off-white to brown. The copper nano-antenna (CNA) has an impressively high sensitivity, detecting as low as 2.5 × 10-7 M through image analysis and 4.3 × 10-8 M with spectrophotometric methods. The values fall significantly below the WHO drinking water guidelines of 2 ppm. The CNA's ability to detect at low thresholds and be easily regenerated makes it an effective tool for initial water testing. According to the findings, the CNA could be a promising solution for enhancing safety in drinking water by allowing real-time visualization of copper ions, with only 5 mg required for measurements. Computational analysis has been conducted to study the structural properties of copper and its copper complex (Cu(CNA)2). There is a significant-close agreement between the theoretical and experimental results. The developed CNA material was tested in various environmental and real samples for detecting Cu2+ ions, especially in water and whitening cream samples showing the material application for real-world applications.

Characterization of Human Melanoma Skin Cancer Models: A step towards Model-Based Melanoma Research.

Advancing 3D in vitro human tissue models is crucial for biomedical research and drug development to address the ethical and biological limitations of animal testing. Recently, 3D skin models have proven to be effective for studying serious skin conditions, such as melanoma. For these advanced models to be applicable in preclinical studies, thorough characterization is essential to understand their applicability and limitations. In this study, we used bioimaging and RNA sequencing to assess the architecture and transcriptomic profiles of skin models, including models with melanoma. Our results indicated that these models closely mimicked skin morphology and gene expression patterns. The full-thickness (FT) model shows a superior resemblance to the human skin, particularly in basement membrane formation and cellular interactions. The integrity of the skin-like properties and gene expression signatures of both skin and melanoma cells were preserved upon the integration of melanoma cells, establishing these models as robust platforms for cancer research. The responsiveness of the FT melanoma models to vemurafenib treatment was successfully monitored, demonstrating their validity as a reliable, reproducible, and humane tool for pharmacological testing and drug development. Furthermore, the transcriptomic data showed that skin models with cancer spheroids had upregulated genes linked to aggressive and resilient cancer behavior compared to spheroids alone. This emphasizes the importance of the microenvironment in cancer progression and suggests that 3D skin models can serve to uncover mechanisms and therapeutic targets that are not detectable in simpler systems. STATEMENT OF SIGNIFICANCE: This study introduces advanced, ethically sound skin and melanoma models as alternatives to animal testing in drug discovery. By thoroughly characterizing these models using bioimaging and RNA sequencing, we demonstrate their close resemblance to human skin, particularly in full-thickness models. These models not only replicate the complex cellular interactions and gene expression patterns of human tissue but also maintain robustness after melanoma integration. Our findings highlight the potential of these models in revealing cancer mechanisms and therapeutic targets, offering a significant impact on melanoma research and preclinical testing.

Persistent visual field deficit after half-fluence photodynamic therapy for chronic central serous chorioretinopathy.

To report a rare case of a persistent paracentral scotoma after half-fluence photodynamic therapy for chronic central serous chorioretinopathy. A 65-year-old man with a history of chronic central serous chorioretinopathy presented with a temporal paracentral visual distortion one week after undergoing repeat off-label treatment with half-fluence verteporfin photodynamic therapy for recurrent intraretinal fluid exudation in the right eye. Fundus photography, fundus autofluorescence, optical coherence tomography, fluorescein angiography, indocyanine green angiography, and automatic visual field testing were obtained. Humphrey visual field 10-2 demonstrated a focal temporal paracentral area of reduced sensitivity, which remained persistent on repeat testing up to 9 months. OCT macula and fluorescein angiography demonstrated subtle corresponding changes in the nasal macula, while indocyanine green angiography demonstrated no visible alteration in choroidal perfusion. Symptomatic scotoma in the location of laser treatment may be a rare complication of verteporfin photodynamic therapy even at reduced fluence, in the setting of repeat treatment in the same location, and may be identified and monitored with automated 10-2 visual field testing.

Integrating AI in testing automation: Enhancing test coverage and predictive analysis for improved software quality

This research explores the integration of Artificial Intelligence (AI) in testing automation, focusing on its ability to enhance test coverage and enable predictive analysis for improved software quality. As software systems become increasingly complex, traditional testing methods often struggle to meet quality demands. This study evaluates various AI techniques, including machine learning and natural language processing, and their applications in generating test cases, optimizing testing processes, and predicting defects. Through empirical case studies from diverse organizations, we demonstrate significant improvements in test coverage and defect detection rates following AI implementation. The findings highlight the efficiency gains and quality enhancements achieved through AI-driven testing, while also addressing challenges such as data dependency, complexity of implementation, and the need for skilled personnel. This research contributes to the understanding of AI's role in software testing and encourages organizations to adopt these technologies for better quality assurance and faster development cycles.

Logic Diagnosis Based on Logic Built-In Self-Test Signatures Collected In-Field from Failing System-on-Chips

Modern embedded nanoelectronic devices, particularly in safety-critical sectors, require high dependability throughout their lifecycle. To address this, designers have started integrating extra circuitry for on-device self-testing, such as the Logic Built-In Self-Test (LBIST). However, while automatic test equipment (ATE) ensures exhaustive testing during manufacturing, in-field testing capabilities are limited. This study introduces a novel methodology for in-field data collection of failure information from LBIST engines and a subsequent logic diagnosis strategy to facilitate failure analysis of field returns. The information is collected from key-on and key-off self-tests, executed by central processing units (CPUs) with a fixed seed and different frequency configurations, primarily addressing transition delay (TRN) faults. The proposed approach capitalizes on the constrained in-field configurability of LBIST and does not require a custom architecture, making it highly practical and readily applicable to real-world devices. The logic diagnosis strategy significantly reduces the number of candidate faults by exploiting the first failing pattern index found during the in-field testing and data collection. Reducing fault candidates could enhance accuracy during failure analysis, especially when field return devices exhibit a “No Trouble Found” (NTF) behavior. The experimental results are reported for ITC’99 benchmarks and an industrial automotive system-on-chip (SoC) produced by STMicroelectronics, with about 20 million gates.

The usefulness of QT interval measurement as a screening tool for cascade testing in asymptomatic patients at risk of long QT syndrome

Abstract Background Long QT syndrome (LQTS), an inherited arrhythmic syndrome, remains a public health concern with up to 3000 unexpected deaths in the US annually. A simple 12-lead ECG is the most easily accessible tool for initial screening. Purpose To describe the diagnostic utility of the standard QT correction techniques in 12-Lead ECGs, in a screening population of first-degree relatives of persons with LQTS of known genotype. Methods In this single centre study, data were included on consecutive patients undergoing cascade clinical and genetic testing for LQTS, who had a first degree relative with genotype-positive LQTS, from the years 2019 to 2022. Patient demographics, baseline ECG and genetic results were obtained. The QT interval was manually calculated between three independent readers using the tangent method and corrected using the 4 most commonly used formulae: Bazett (BQTc), Fridericia (FreQTc), Framingham (FraQTc) and Hodges (HQTc). The Mean QT was calculated by taking the average of four consecutive QT measurements in lead II or V5. The QT dispersion (QTd), was also calculated by using the formula: QTd = QT max – QT min. Results Data was available on 97 patients from 14 families. The mean age at screening was 44 years, and 51.5% were males. Two-thirds of patients were asymptomatic (n = 74, 76.3%). Fifteen patients had syncopal events. 72.2% (n = 70) of patients had a family history of arrhythmic death. After clinical screening and genotyping, 46.4% (n = 45) patients were gene positive for either KCNQ1 (75.5%) or KCNH2 (24.5%). Patients with prior syncope were more likely to be gene positive (72.7% vs 39.2%, p = 0.005). The 12-Lead ECG derived QTc result is listed in Table 1, with all correction modalities showing a higher corrected QT in the genotype-positive group (p &amp;lt;0.001). The diagnostic performance of BQTc was similar to that of the newer formulae (FreQTc, FraQTc and HQTc) when correcting for heart rate within the range of 60 – 100bpm as listed in Table 2. At a cut-off value of 440ms, BQTc formula had a sensitivity of 51% and a specificity of 84% for predicting genotype pathogenicity as listed in Table 3. The QT dispersion was higher in patients with KCNH2 compared to those with KCNQ1 (122msec, SD 30.5 vs 74.2 msec, SD 30.4, p value &amp;lt;0.001). Patients with KCNH2 were more likely to require an ICD (83.3% vs 16.7%, p &amp;lt;0.001). The mean Schwartz score in gene positive patient was 2.6 compared to 1.9 (p &amp;lt;0.003). Conclusion Formula correction of QT interval to heart rate had good overall diagnostic performance. Both FreQT and FraQT had high AUROC across different heart rates, though there was no statistically significant difference seen between correction formulae. The ECG remains a good preliminary screening tool method for identifying patients with increased risk LQTS in this population. Larger studies are needed to power for comparisons between QT correction methods.

Practice and Application of Intelligent Technology in Software Automation Testing

Practice and Application of Intelligent Technology in Software Automation Testing

Test Case Automation: Transforming Software Testing in the Digital Era

Purpose: This research aims to explore the limitations of manual software testing, the effectiveness of test case automation in addressing these challenges, and the strategies for successful implementation of automation. It also examines the broader impact of adopting test automation on the quality, speed, and efficiency of software delivery. Methodology: The study investigates the challenges of manual testing in agile and DevOps environments and explores the role of automation as a solution. By analyzing various automation tools, frameworks, and best practices, the research provides insights into successful implementation strategies for test case automation. Additionally, it reviews the associated benefits and potential challenges organizations may face when transitioning to automated testing. Findings: Test case automation significantly mitigates the shortcomings of manual testing, particularly in fast-paced development cycles. The findings reveal that automation not only enhances the quality of software by catching defects earlier but also accelerates the testing process, improving overall efficiency. However, the study also identifies challenges, such as the initial investment in tools and skills required for automation, as well as the ongoing need for maintenance of automated test cases. Unique Contribution to Theory, Policy and Practice: This research contributes to the theoretical understanding of software testing (Tom Badgett, Corey Sandler &amp; Glenford J. Myers, 2011) by highlighting the essential role of automation in modern software development methodologies like agile and DevOps. It bridges the gap between traditional manual testing and contemporary automated practices, providing a framework for understanding how automation fits within these rapid development paradigms. In practice, the study encourages organizations to adopt test case automation as a critical policy for improving the quality and speed of software delivery. It offers practical insights into the implementation of automation, including tool selection, training, and process integration, which can guide companies in transitioning to automated testing. Additionally, it highlights the long-term benefits of automation, making it a valuable policy consideration for organizations aiming to optimize their development and testing processes.