<p><strong>Context and relevance.</strong><strong> </strong>Modern web application development requires continuous testing, but maintaining automated tests is becoming increasingly labor-intensive due to locator instability and growing interface complexity. The emergence of Large Language Models (LLM) opens new opportunities for test creation automation, but their practical application faces challenges in processing large HTML documents and the need to create maintainable code. <strong>Objective. </strong>To develop and evaluate the effectiveness of a method for automatic generation of maintainable web application tests using LLM based on HTML structure analysis and the Page Object Model(POM) pattern. <strong>Hypotheses. </strong>Primary hypothesis: combining LLM with a two-stage generation approach and the POM pattern will enable the creation of maintainable tests, reducing development time by at least one-third (to 67% or less) while preserving code readability. Secondary hypothesis: the success rate of automatic generation will be inversely proportional to the complexity of interface components. <strong>Methods and materials. </strong>The study employed an approach based on Playwright, LLM, and a two-stage generation procedure with intermediate validation. Testing was conducted on four components of an SPA application for virtual infrastructure management. Validation of results was performed by a team of three testers who assessed the correctness and readability of generated tests. <strong>Results. </strong>The proposed method achieved high success rates in automatic test generation and substantial reduction in time costs for test creation. The two-stage procedure with intermediate validation enabled localization of a significant portion of errors at the early stage of Page Object creation. Automatically generated tests provided coverage of most required functionality while maintaining code readability. An inverse relationship between generation success and interface component complexity was confirmed: standardized interfaces demonstrated significantly higher success rates. <strong>Conclusions.</strong> The proposed method provides substantial time savings in creating a baseline test suite while maintaining quality and maintainability. The approach is recommended for early stages of feature development with expert control retained for validating critical scenarios. The method is particularly effective for projects with frequent interface changes, large volumes of regression testing, and components with standardized interfaces.</p>

This study employs a two-part research design to explore the impact of ChatGPT on programming education for engineering undergraduates. Study 1 involved 56 third-year students who completed a questionnaire examining the frequency and purposes of ChatGPT use in text-based programming. While no statistically significant association was found between ChatGPT usage frequency and final grades, high-achieving students tended to use the tool less frequently, whereas lower-performing students relied on it more for support. Study 2 employed a counterbalanced repeated-measures design with nine first-year students divided into two groups, who developed desktop applications with and without ChatGPT. Project assessments and focus-group interviews were used to examine the effects of ChatGPT on creativity, confidence, effectiveness, and teamwork in visual programming. The results indicate that ChatGPT use was associated with reduced task completion time and increased coding efficiency; however, it was also linked to decreased creativity, greater reliance on ready-made solutions, and diminished code readability and collaborative engagement. These results highlight the need for teaching strategies that balance AI integration in programming education. The study recommends incorporating tasks based on the Reverse Bloom’s Taxonomy and activities that allow students to work with and without ChatGPT to encourage critical reflection and responsible AI use.

Code-reading ability has traditionally been under-emphasized in assessments as it is difficult to assess at scale. Prior research has shown that code-reading and code-writing are closely related skills; thus being able to assess and train code reading skills may be necessary for student learning. One way to assess code-reading ability is using Explain in Plain English (EiPE) questions, which ask students to describe what a piece of code does with natural language. Previous research deployed a binary (correct/incorrect) autograder using bigram models that performed comparably with human teaching assistants on student responses. With a dataset of 3,064 student responses from 17 EiPE questions, we investigated multiple autograders for EiPE questions. We evaluated methods as simple as logistic regression trained on bigram features, to more complicated Support Vector Machines (SVMs) trained on embeddings from Large Language Models (LLMs) to GPT-4. We found multiple useful autograders, most with accuracies in the \(86\!\!-\!\!88\%\) range, with different advantages. SVMs trained on LLM embeddings had the highest accuracy; few-shot chat completion with GPT-4 required minimal human effort; pipelines with multiple autograders for specific dimensions (what we call 3D autograders) can provide fine-grained feedback; and code generation with GPT-4 to leverage automatic code testing as a grading mechanism in exchange for slightly more lenient grading standards. While piloting these autograders in a non-major introductory Python course, students had largely similar views of all autograders, although they more often found the GPT-based grader and code-generation graders more helpful and liked the code-generation grader the most.

Metformin is the most commonly prescribed oral treatment for type 2 diabetes mellitus (T2DM) in the UK. Long-term therapy has been linked to vitamin B12 deficiency, a concern recognised for decades but not consistently addressed. In June 2022, the UK Medicines and Healthcare products Regulatory Agency (MHRA) classified low vitamin B12 levels as a common adverse effect of metformin and advised clinicians to consider periodic testing in at-risk patients. Translating such regulatory advice into practice can be challenging, and the extent to which the guidance has influenced testing and diagnostic coding for vitamin B12 deficiency remains unclear. This study evaluated trends in vitamin B12 testing and deficiency coding in metformin-treated patients compared with the general population before and after the 2022 MHRA Drug Safety Update. A retrospective quantitative analysis was conducted using Read code data from 148,000 electronic medical records across three Primary Care Networks (PCNs) in the Southwest of England. Vitamin B12 testing and deficiency coding rates were compared in patients prescribed metformin and the general population across two periods: pre-guidance (2017-2021) and post-guidance (2022-2024). Welch's t-tests were used to determine statistical significance, with p < 0.05 considered significant. Among patients prescribed metformin, vitamin B12 testing rates rose from 34.5% (SD = 1.8) pre-guidance to 38.2% (SD = 0.4) post-guidance (p = 0.008). In the general population, testing rates increased from 12.2% to 14.7% (p = 0.009). However, coding for vitamin B12 deficiency remained unchanged at 0.25% in the metformin group and decreased from 0.072% to 0.060% in the general population, with no statistically significant difference. The post-guidance period included only two years of data, which limits the ability to assess longer-term or comparative trends between groups. This study demonstrated that the release of the MHRA Drug Safety Update was associated with a modest but statistically significant increase in vitamin B12 testing among patients prescribed metformin, paralleled by a smaller rise in testing within the general population. However, diagnostic coding practices did not change, suggesting limited translation of safety alerts into structured documentation. Further research is warranted to explore barriers and evaluate interventions to improve monitoring and coding compliance in primary care.

Traceability is essential in modern manufacturing to ensure product quality, safety, and compliance throughout production and operation. In sectors such as automotive and aerospace, reliable component identification supports process control, maintenance, and failure analysis. Conventional marking methods often fail under difficult thermal conditions, creating demand for more robust solutions. This study evaluates the durability and readability of laser-engraved codes on aluminum components under conditions representative of industrial environments. Codes were engraved on surfaces with varying geometries, textures, and temperature, then exposed to elevated temperatures treatments. Their performance was assessed using industrial and mobile scanning devices to determine suitability for traceability systems in demanding manufacturing processes.

PT Puskomedia Indonesia Kreatif has developed the Panda Attendance Application, a digital technology system for village administration with a focus on the smooth flow of employee infor-mation. This application utilizes QR codes and GPS technology for the attendance or absence of village employees at registered coordinates. One of the issues at PT Puskomedia is that village operators face challenges such as needing to contact the PT Puskomedia system operator for manual attendance tracking, leading to a decline in users. Currently, there are various challenges in the use of the Panda Attendance Application by village government institutions. Several com-plaints have been raised by users regarding technical issues and unsatisfactory user experience. The purpose of this study is to evaluate the level of user acceptance of the Panda application. The method used to analyze user satisfaction is the System Usability Scale (SUS) method. The results of this study yielded a SUS score of 46.22, indicating a low category (Grade E), with a percentile rank of 10%, and classified as poor (Grade E). The nature (adjective) of the application falls into the “Poor” category, and the score of 46.22 places the application in the “Not Acceptable” catego-ry according to the level of acceptance. Recommendations for improvement from this analysis in-clude enhancing system stability and technical improvements such as accelerating the QR code reading process. Responding to user feedback and implementing these improvements is expected to enable the Panda attendance application to achieve a higher level of usability and gain better acceptance from users.

The rapid development of the automotive industry has significantly increased the number of vehicles, thereby driving a higher demand for vehicle diagnosis and maintenance. However, existing diagnostic devices are still limited by their connection range and high cost. This study aims to develop a novel diagnostic solution that offers more flexible and cost-effective connectivity. The research begins by analyzing and synthesizing technical documents on OBD-II systems, android application development tools, and real-time databases. A diagnostic module using an ESP32 microcontroller is then developed to read and process CAN signals from the vehicle's OBD-II system. The module is designed to be compact, cost-effective, and non-intrusive to the vehicle's hardware. Additionally, an android application was developed with functionalities such as data reading, Diagnostic Trouble Code (DTC) reading, clearing all DTC, and component activation testing. This application enables users to interact bidirectionally with the remote diagnostic module via a real-time database without being restricted by distance. The application functions similarly to a multi-functional diagnostic tool, offering effectively unlimited connection range and fast response times (0.4–0.6 seconds). This research successfully delivers a flexible diagnostic solution for vehicles, addressing common limitations of existing diagnostic devices.

Optical rotary pseudorandom encoders are an excellent solution for reliable and precise angular position measurement in various industrial motion systems. The paper describes an improved optical rotary pseudorandom encoder solution that has a reduced angular position measurement time. The presented encoder uses two heads to read the pseudorandom code and to form the main and control code words. The encoder algorithm has been improved so that code conversion is not performed after each new code word. In presented solution, after each new code word, a code reading error checking is performed, and the position is determined based on the previous measured value if no code reading error is detected. The functionality of the proposed encoder solution in the LabVIEW environment is tested.

Metabolic syndrome (MetS) in inflammatory arthritis (IA) directly impacts its management and associated morbidity and mortality. MetS is a well-recognised comorbidity in PsA, but the epidemiology across IA is unclear. This study aimed to characterise the prevalence of MetS across rheumatoid arthritis (RA), psoriatic arthritis (PsA) and axial spondyloarthritis (axSpA) compared to controls. We performed a cross-sectional analysis of half a million individuals from the UK Biobank, aged 40 to 69 years, who were collected between 2006 and 2010. Participants with RA, PsA, and axSpA were identified using ICD-10 codes and/or read codes. MetS was defined according to adapted National Cholesterol Education Program Adult Treatment Panel III criteria. Statistical analysis included ANOVA and chi-squared test for between-group difference and logistic regression for odds of MetS, adjusted for age, sex, CRP and smoking status. The prevalence of MetS was highest in RA (43.4%), followed by PsA (42.3%), axSpA (37.1%) and controls (31.8%). Hypertension was prevalent across all IAs (~ 80%), as was hypertriglyceridaemia. Elevated waist circumference and dysglycaemia were more prevalent in RA and PsA compared to axSpA. The adjusted odds of comorbid MetS were elevated in RA (OR 1.15; 95% CI 1.07, 1.24; p < 0.001) and PsA (OR 1.31; 95% CI 1.13, 1.52; p < 0.001) compared to controls, but decreased in axSpA (OR 0.82; 95% CI 0.70, 0.96; p = 0.012). RA and PsA, but not axSpA, are associated with an increased odds of MetS. Holistic management strategies that address both IA and MetS are essential for improving mortality and morbidity.

Bridging Quality, Interoperability, and Terminology Through the Updated Malnutrition Care Score.

In this research work, we present a transformer-based method for generating function-level summaries of Python code using synthetically generated data. The primary objective is to automate the creation of docstrings, which are essential for code readability, reuse, and maintainability. Traditional datasets for code summarization are either scarce or noisy, which limits the performance and generalizability of data-driven models. To address this challenge, we designed a pipeline that synthetically generates a dataset containing Python functions and their corresponding human-readable summaries, mimicking real-world documentation patterns. We employ the CodeT5-small transformer model in a sequence-to-sequence (seq2seq) learning framework to perform the summarization task. The dataset is preprocessed to remove noise, normalize formatting, and tokenize inputs suitable for the model. Training is conducted over multiple epochs, with the model progressively improving its understanding of the mapping between code and natural language descriptions. The evaluation phase uses both automated metrics—such as BLEU, ROUGE-1, ROUGE-2, ROUGE-L, and Exact Match—and manual inspection through human evaluation scores to assess the quality and coherence of generated summaries. The results demonstrate consistent improvements in accuracy, with occasional fluctuations resembling realistic model behavior. To enhance accessibility and usability, a lightweight Streamlit web application is developed that allows users to input custom Python code and receive automatically generated docstrings. Keywords: Python Code Summarization, CodeT5, Natural Language Processing, Transformer, Synthetic Dataset, Docstring Generation, Streamlit, Software Documentation, Code Analysis, ROUGE Score, BLEU Score, Fine-tuning, Sequence-to-sequence, Human Evaluation.

Explain-in-Plain-English (EiPE) questions are used by some researchers and educators to assess code reading skills. EiPE questions require students to briefly explain (in plain English) the purpose of a given piece of code, without restating what the code does line-by-line. The premise is that novices who can explain the purpose of a piece of code have higher code reading skills than those who can trace the code but cannot see its high-level purpose. However, using natural language in EiPE questions poses challenges. Students (especially those whose first language is not English) may struggle to convey their understanding of the code unambiguously. Also, grading responses written in natural language is time-consuming, requires the design of a rubric, and is difficult to automate. We propose a new code reading question type that addresses these issues with EiPE questions. Given a piece of code involving repetition (in the form of iteration or recursion), the student is asked to provide the output for a set of inputs, where the output for some of these inputs cannot be determined using code tracing alone and requires higher-level code comprehension. In empirical evaluations, using CS1 exams, think-aloud interviews with students, and interviews with instructors, we found that assessments of code reading skills using the new question type are highly consistent with the assessments using EiPE questions, yet are more reliable. These results put forward the proposed question type as another way to assess high-level code reading skills without the issues associated with expressing in natural language or grading responses expressed in natural language.

Refactoring is a critical process in software development, aiming at improving the internal structure of code while preserving its external behavior. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Like traditional software systems, refactoring engines can generate incorrect refactored programs, resulting in unexpected behaviors. In this paper, we present the first systematic study of refactoring engine bugs by analyzing bugs arising in three popular refactoring engines (i.e., Eclipse , IntelliJ IDEA, and Netbeans ). We analyzed these bugs according to their refactoring types, symptoms, root causes, and triggering conditions. We obtained 12 findings and provided a series of valuable guidelines for future work on refactoring bug detection and debugging. Furthermore, our transferability study revealed 134 new bugs in the latest version of those refactoring engines. Among the 22 bugs we submitted, 11 bugs are confirmed by their developers, and seven of them have already been fixed.

Accurate method naming is crucial for code readability and maintainability. However, manually creating concise and meaningful names remains a significant challenge. To this end, in this paper, we propose an approach based on Large Language Model (LLMs) to suggest method names according to function descriptions. The key of the approach is ContextCraft , an automated algorithm for generating context-rich prompts for LLM that suggests the expected method names according to the prompts. For a given query (functional description), it retrieves a few best examples whose functional descriptions have the greatest similarity with the query. From the examples, it identifies tokens that are likely to appear in the final method name as well as their likely positions, picks up pivot words that are semantically related to tokens in the according method names, and specifies the evaluation results of the LLM on the selected examples. All such outputs (tokens with probabilities and position information, pivot words accompanied by associated name tokens and similarity scores, and evaluation results) together with the query and the selected examples are then filled in a predefined prompt template, resulting in a context-rich prompt. This context-rich prompt reduces the randomness of LLMs by focusing the LLM’s attention on relevant contexts, constraining the solution space, and anchoring results to meaningful semantic relationships. Consequently, the LLM leverages this prompt to generate the expected method name, producing a more accurate and relevant suggestion. We evaluated the proposed approach with 43k real-world Java and Python methods accompanied by functional descriptions. Our evaluation results suggested that it significantly outperforms the state-of-the-art approach RNN-att-Copy , improving the chance of exact match by 52% and decreasing the edit distance between generated and expected method names by 32%. Our evaluation results also suggested that the proposed approach worked well for various LLMs, including ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, Gemini-1.5, and Llama-3.

Declarative UI frameworks have gained widespread adoption in mobile app development, offering benefits such as improved code readability and easier maintenance. Despite these advantages, the process of translating UI designs into functional code remains challenging and time-consuming. Recent advancements in multimodal large language models (MLLMs) have shown promise in directly generating mobile app code from user interface (UI) designs. However, the direct application of MLLMs to this task is limited by challenges in accurately recognizing UI components and comprehensively capturing interaction logic. To address these challenges, we propose DeclarUI, an automated approach that synergizes computer vision (CV), MLLMs, and iterative compiler-driven optimization to generate and refine declarative UI code from designs. DeclarUI enhances visual fidelity, functional completeness, and code quality through precise component segmentation, Page Transition Graphs (PTGs) for modeling complex inter-page relationships, and iterative optimization. In our evaluation, DeclarUI outperforms baselines on React Native, a widely adopted declarative UI framework, achieving a 96.8% PTG coverage rate and a 98% compilation success rate. Notably, DeclarUI demonstrates significant improvements over state-of-the-art MLLMs, with a 123% increase in PTG coverage rate, up to 55% enhancement in visual similarity scores, and a 29% boost in compilation success rate. We further demonstrate DeclarUI’s generalizability through successful applications to Flutter and ArkUI frameworks. User studies with professional developers confirm that DeclarUI’s generated code meets industrial-grade standards in code availability, modification time, readability, and maintainability. By streamlining app development, improving efficiency, and fostering designer-developer collaboration, DeclarUI offers a practical solution to the persistent challenges in mobile UI development.

Modelling heat and mass transfer processes is essential in designing and optimising technological processes in power engineering, mechanical engineering, metallurgy, chemical industry, and other engineering fields. For the mathematical description of such processes, differential equations of heat conduction and diffusion are used, the solution of which requires the application of efficient numerical methods, especially in the case of complex geometries and diverse boundary conditions. This study presents a unified methodology for the numerical solution of boundary value problems of heat conduction with internal heat sources, based on locally one-dimensional implicit finite difference schemes derived using the integral-interpolation method (balance method) in Cartesian and cylindrical coordinate systems. Special attention is given to discretising boundary conditions of the first, second, and third kinds, focusing on Robin conditions, the most commonly encountered in engineering practice. A quasi-linear approximation scheme and spatial splitting schemes are recommended to increase the efficiency of numerical solutions. This approach enables the application of the unconditionally stable Tridiagonal Matrix Algorithm (TDMA). The introduction of indica-tor coefficients provides flexibility in implementation, allowing the balance equation to be used variably by manipulating the terms responsible for heat fluxes and the location of com-putational nodes. This ensures ease of implementation and improves code readability, facili-tating software development for computational modelling. The results of the numerical simu-lation obtained using the proposed method are compared with known analytical and numerical solutions and demonstrate high accuracy. The proposed methodology opens broader opportu-nities for modelling thermal regimes in complex engineering systems.

The APOLLO-QR (APPlying smartphOne for piLLs intake cOnfirmation by QR code reading) study assessed the congruence between a quick response (QR) code-based digital self-reporting and pill count in measuring medication adherence. The APOLLO-QR pilot, observational study prospectively included patients owning a smartphone accepting to undergo a home-telemonitoring of ticagrelor adherence by sending feedback of each pill intake through an email generated by framing a QR code placed on the medication packaging. Ticagrelor adherence was measured at 1 and 3 months by pill count allowing to calculate accuracy of the digital self-reporting in estimating drug adherence by assessing the correspondence between the number of received feedback emails and the number of pills taken from those prescribed. Among 109 patients, 30-day adherence to ticagrelor was 98.6 ± 2.6% as measured by pill count vs. 88.9 ± 10.4% as assessed by the number of feedback emails sent by the digital self-reporting, which provided an accuracy in estimating drug adherence of 90.1 ± 10.1%. Similar results were achieved at three months among the 95 patients (87.2%) continuing the study. Only nine patients (8.3%) missed sending four consecutive feedback emails of whom three (2.8%) had voluntarily discontinued ticagrelor within 1 month. A high patient satisfaction emerged from responses to a questionnaire showing that tested telemonitoring was consistently perceived as easy, convenient, and useful, although the need for more interactivity was suggested. The QR code-based self-reporting of pill intake showed a high accuracy in estimating medication adherence and yielded a good patient satisfaction, suggesting a potential for its clinical applicability.

The subject of this article is the Internet of Things (IoT), particularly in the context of libraries. The author explains the definitions and concepts of IoT, the importance of IoT in libraries, and the potential applications of IoT for libraries. This research is located at the STMIK Bina Bangsa Kendari campus. The goal of this research is to design and simulate a prototype of an IoT-based library system utilizing UHF RFID technology. The research method used in this study is the development of a research and development model. The design of the intelligent library system development is based on two components: system hardware architecture and software development. The development approach uses a prototype development model. The library system can monitor the condition of books in real-time, whether they are available on the shelves, loaned out, or not on the shelves. The library system can provide information in the form of shelf monitoring if a book is misplaced on the shelf. The use of UHF RFID technology allows the application to read tag labels up to a maximum distance of 6 meters, while to support optimal QR Code reading in a room measuring 4 x 4 x3 meters (L x W x H), a minimum of one bulb with a power of 18 watts is required.

ObjectiveEstimate the effect of type 2 diabetes on the development of frozen shoulder and investigate whether the effect is mediated by other metabolic factors.MethodsPrimary care medical record-based cohort study containing 43,977 people newly diagnosed with type 2 diabetes and 43,977 without diabetes. Variables were identified using established Read codes. A weighting approach with Cox regression was used to decompose the total effect into the direct effect and indirect effect, mediated by metabolic health (which was defined as the number of metabolic factors developed during follow-up). Estimates were expressed as hazard ratios (HR). Confounders were identified using a DAG. Sensitivity to unmeasured confounding, extreme weights, and missing data were tested.ResultsThe total effect of type 2 diabetes on the development of frozen shoulder was HR = 4.38 (95% CI: 3.70–5.21), the natural indirect effect (mediated through metabolic health) was HR = 0.98 (95% CI: 0.93–1.03) and the natural direct effect was HR = 4.46 (95% CI: 3.68–5.41). Results were robust to unmeasured confounding, extreme weights, and missing data.ConclusionsThis study suggests that type 2 diabetes may be a cause of frozen shoulder but does not support the hypothesis that the effect is mediated by metabolic health. Clinicians should remain alert that shoulder pain in people with diabetes could be indicative of a frozen shoulder. This study should raise awareness that, despite often being overlooked, musculoskeletal conditions can be complications of diabetes and should be considered during clinical conversations with patients.ISAC protocol registration number19_219R.

ObjectiveTo examine the mental ill health burden associated with allergic and atopic disorders, in a UK primary care cohort.DesignPopulation-based retrospective open cohort study.SettingUnited Kingdom.Participants2 491 086 individuals with primary-care recorded atopic disorder (food allergy, drug allergy, anaphylaxis, urticaria, allergic rhino-conjunctivitis) diagnosis were matched by sex, age (± 2 years), and socio-economic deprivation (Townsend quintile score) at index to 3 120 719 unexposed individuals. The mean age of exposed patients at cohort entry was 39.42 years (SD (SD) 23.65) compared with 35.81 years (SD 22.17) for unexposed patients.Main outcome measuresThe primary outcome was a composite of mental ill health (severe mental illness, anxiety, depression, eating disorders, obsessive-compulsive disorder (OCD), and self-harm), identified using Read codes. Cox regression was used to estimate adjusted hazard ratios with 95% confidence intervals for the composite mental ill health outcome and each of the individual mental health disorders. Covariates adjusted for were age, sex, alcohol use, smoking status, body mass index (BMI), Townsend deprivation quintile score, asthma exposure, and eczema exposure at baseline.ResultsBetween first January 1995 to 31st January 2022, a total of 2 491 086 eligible individuals were identified with a primary care recorded diagnosis of atopic disease and were matched to 3 120 719 unexposed individuals. 229 124 exposed individuals developed a mental ill health outcome during the study period (incidence ratio (IR) 144.13 per 10 000 person-years) compared with 203 450 in the unexposed group (IR 117.82 per 10 000 person-years). This translated to an adjusted hazard ratio (aHR) of 1.16 (95% CI 1.15 to 1.17). Notably, the risk of anxiety was greatest, aHR 1.22 (95% CI 1.21 to 1.23). Our findings were robust to a sensitivity analysis, where individuals were also matched for asthma and eczema.ConclusionThere is an increased risk of mental ill health disorders among patients with diagnosis of an allergic and atopic disorders. There is a need to consider dual delivery of allergy and psychology services to optimise mental well-being among this cohort.