Tobacco dependence poses a substantial public health challenge in our country. Moreover, there is a well-established association between tobacco use and other substances. Thus, there is a need to enable patients who wish to quit tobacco use by linking them to treatment services and guiding them through the treatment course. This study proposes using artificial intelligence (AI) methods to increase the reach and ease of treatment for tobacco cessation. A role-based and cross-platform application named Electronic means of Decreasing Overuse of Substance like Tobacco - a Health promoting Intervention (eDOSTHI) will be developed in English and Bengali to cater to patients. After the development of the software and pilot testing, efficacy testing will be done on a sample of 220 patients (age:18-65 years) each in the intervention and control arm by means of randomized controlled trial followed by assessment at 4 and 24 weeks. This study protocol describes randomised controlled trial to evaluate a language compatible and culturally-adapted mobile application (eDOSTHI) that can help patients with tobacco use to obtain medical advice and achieve abstinence. The study has ethical clearance (Ref No. IEC/AIIMS/Kalyani/Meeting/2023/013) from AIIMS Kalyani Institutional Ethics Committee (IEC). The norms of National Ethical Guideline for Biomedical and Health Research Involving Human Participants (2017) by the Indian Council for Medical Research (ICMR) for data collection and for collection of biological samples and storage will be adhered to. CTRI/2025/09/094053 Dated: 01.09.2025.

In the current landscape of software testing, challenges persist in test case data generation, including variability in data quality and the inherent difficulty of data synthesis. These challenges are further exacerbated in scenarios where data are widely distributed across heterogeneous organizational environments. Privacy regulations and security concerns impose strict constraints on data sharing, preventing centralized data aggregation and highlighting the necessity of a federated environment as a more practical solution. To address the privacy protection and data sharing challenges in federated test case data generation, we propose a Generative Adversarial Network (GAN)-based method specifically designed for federated settings. By leveraging the strong data generation capabilities of GANs, the proposed approach is able to generate high-quality and diverse test case data while preserving data privacy. Specifically, through a protocol grammar-based deep learning framework combined with test case encoder-decoder encoding mechanisms and a GAN-driven sample character generator, the proposed method can predict and generate variant test case samples. In the federated environment, each participant trains the generator and discriminator locally, while model parameters are securely aggregated to achieve global model optimization. Experimental results demonstrate that the generated test case data outperforms traditional methods in terms of coverage and effectiveness, significantly enhancing the efficiency and quality of software testing. Ultimately, the proposed framework provides a scalable solution for identifying latent vulnerabilities in critical infrastructure while strictly adhering to data sovereignty requirements in cross-organizational environments.

The increasing complexity of spacecraft On-Board Software (OBSW) necessitates advanced development and testing methodologies to ensure reliability and robustness. This paper presents a digital twin approach for the development and testing of embedded spacecraft software. The proposed electronic digital twin enables high-fidelity hardware and software simulations of spacecraft subsystems, facilitating a comprehensive validation framework. Through real-time execution, the digital twin supports dynamical simulations with possibility of failure injections, enabling the observation of software behavior under various nominal or fault conditions. This capability allows for thorough debugging and verification of critical software components, including Finite State Machines (FSM), Guidance, Navigation, and Control (GNC) algorithms, and platform and mode management logic. By providing an interactive and iterative environment for software validation in nominal and contingency scenarios, the digital twin reduces the need for extensive Hardware-in-the-Loop (HIL) testing, accelerating the software development life-cycle while improving reliability. The paper discusses the architecture and implementation of the digital twin, along with case studies based on a modular OBSW architecture, demonstrating its effectiveness in identifying and resolving software anomalies. This approach offers a cost-effective and scalable solution for spacecraft software development, enhancing mission safety and performance.

A two-phase TOE framework integrating SEM and ANN for evaluating cloud computing adoption in software testing

Analytical and simulation models were used to investigate the formation mechanism and enhancement pathways of green supply chain resilience (GSCR) in customized home furnishing enterprises. A mixed-methods research approach was employed, incorporating both quantitative and qualitative data collection. For the qualitative component, anchored in resilience theory and the Technology-Organization-Environment (TOE) framework, a resilience indicator system was developed that integrates both capability and risk factors, proposing 21 mechanistic hypotheses. For the quantitative component, 179 targeted questionnaires were collected, and partial least squares structural equation modeling (PLS-SEM) was applied using SmartPLS software for factor analysis and hypothesis testing. This was followed by a fuzzy comprehensive evaluation of the case enterprise’s resilience level. Furthermore, a system dynamics model was constructed to simulate resilience development trends under four distinct scenarios. The results indicate that factors such as environmental compliance monitoring maturity and production disruption risks due to adverse events exert the most significant influence on the GSCR of customized home furnishing enterprises.

Automated software testing has become a core pillar of modern software engineering due to increasing demands for rapid delivery, high reliability, and scalable quality assurance. The integration of Artificial Intelligence (AI) into testing processes has further enhanced automation by enabling intelligent test case generation, predictive defect detection, adaptive prioritization, and proactive fault prevention. However, most AI-driven testing solutions operate as opaque black-box systems, limiting transparency, accountability, and trust. This lack of explainability poses significant challenges for debugging, validation, regulatory compliance, and stakeholder confidence. Explainable Artificial Intelligence (XAI) addresses these challenges by providing human-understandable explanations for AI-driven decisions. This paper investigates the role of XAI in automated software testing, with a particular focus on CI/CD and DevSecOps environments. We analyze key opportunities, including enhanced developer trust, improved debugging and root cause analysis, smarter test optimization, and strengthened compliance support. At the same time, we identify critical challenges such as the trade-off between model performance and interpretability, the absence of standardized metrics for explanation quality, integration complexity within CI/CD pipelines, and potential security risks arising from over-disclosure. Based on a structured review of recent academic literature and industry practices, this study presents a comprehensive perspective on how XAI can transform automated software testing into a more transparent, trustworthy, and responsible discipline. The findings suggest that explainability should be treated as a foundational design principle rather than an optional feature for future AI-driven testing frameworks.

Feedback-based Evolutionary Algorithm for Optimising the Test Suite in Multi-criteria Coverage of Software Testing

Abstract The integration of artificial intelligence (AI) and machine learning (ML) has improved the efficiency and accuracy of testing processes. Deep learning (DL) is increasingly used in software testing to improve accuracy, automation, and efficiency, especially in complex testing tasks. No comprehensive review has examined the use of deep learning in software testing. This study presents a bibliometric analysis to examine has examined the use of deep learning in software testing and to map the current research landscape in this field. This bibliometric review highlights how DL contributes to process innovation in software testing and highlights directions for future research. The search strategy was used to obtain relevant papers from the Scopus database; 737 relevant documents were retrieved based on defined inclusion and exclusion criteria, with the search conducted on July 27, 2024. The analysis focuses on publication trends, prolific authors, co-occurrence of keywords, collaboration networks, and thematic evolution. The results show an increasing trend in publications since 2017, dominated by themes such as fault prediction, code analysis, and automated testing frameworks. Two main research directions emerged: applying DL to support testing activities and testing DL-based systems. However, areas such as model-based testing, exploratory testing, and testing under extreme conditions remain underexplored. This study offers an overview of how DL contributes in software testing. The study also highlights collaboration patterns and thematic developments, offering valuable insights for researchers and practitioners. The findings serve as a roadmap for future research to enhance software quality through deep learning.

Large language models (LLMs) are gaining increasing popularity in software engineering (SE) due to their unprecedented performance across various applications. These models are increasingly being utilized for a range of SE tasks, including requirements engineering and design, code analysis and generation, software maintenance, and quality assurance. As LLMs become more integral to SE, evaluating their effectiveness is crucial for understanding their potential in this field. In recent years, substantial efforts have been made to assess LLM performance in various SE tasks, resulting in the creation of several benchmarks tailored to this purpose. This paper offers a thorough review of 291 benchmarks, addressing three main aspects: what benchmarks are available , how benchmarks are constructed , and the future outlook for these benchmarks . We begin by examining SE tasks such as requirements engineering and design, coding assistant, software testing, AIOps, software maintenance, and quality management. We then analyze the benchmarks and their development processes, highlighting the limitations of existing benchmarks. Additionally, we discuss the successes and failures of LLMs in different software tasks and explore future opportunities and challenges for SE-related benchmarks. We aim to provide a comprehensive overview of benchmark research in SE and offer insights to support the creation of more effective evaluation tools.

Purpose. To conduct a multi-criteria evaluation and analysis of the performance of encryption algorithms that may be potentially resistant to contemporary cyberattacks, including quantum attacks. The evaluation takes into account the ability of the algorithms to be deployed on devices with limited computational resources within the infocommunication networks during the transmission of information messages. Methodology. Software implementation, testing and validation of selected cryptographic algorithms based on Python, considering the impact of limited resources and destabilising factors, such as signal noise components, based on computer experiments were applied. The performance of the studied cryptographic algorithms was analysed using statistical data processing methods and a multi-criteria evaluation approach. Findings. The symmetric algorithms AES-256-GCM and ChaCha20-Poly1305 demonstrated the highest accuracy in signal recovery following encryption and decryption (MSE ranges from 1.95 · 10-6 to 5.12 · 10-5). The time taken to encrypt and decrypt I/Q signals using symmetric algorithms was found to be around 2.5 times faster than that required by the Kyber family. Computer experiments confirmed the existence of a trade-off between processing speed and security level. Symmetric algorithms are optimal for scenarios with critical processing speed requirements. However, Kyber provides greater protection reliability, albeit at the cost of additional resources. The correctness of the proposed computer model, which enables the computational and information-functional characteristics of cryptographic algorithms to be evaluated, has been proven. Originality. Patterns of the destabilising influence of signal-to-noise ratio indicators and signal length on the accuracy of digital signal recovery after encryption have been established for different cryptographic algorithms (AES, ChaCha20 and the Kyber) in the context of their use in resource-constrained infocommunication systems. Practical value. Implementing the computer model proved its suitability for studying cryptographic algorithms in resource-constrained environments, as well as its potential for improving information security protocols and selecting optimal algorithms based on processing speed requirements and desired security levels.

Fault concealment in complex software programs and the difficulty of generating test cases to detect such faults present significant challenges in software testing. To resolve these challenges, this paper suggests a novel method that integrates mutation testing, fuzzy clustering, convolutional neural networks (CNN), and particle swarm optimization (PSO) to efficiently generate test cases that cover multiple paths with numerous faults (mutant branches). Initially, mutation-based paths are classified using fuzzy clustering based on their coverage difficulty and similarity. A multi-feature CNN model (MF_CNNpro) is then constructed and trained on the paths of each cluster. Finally, the predicted particles from the MF_CNNpro model are used as the initial population for PSO, which evolves to generate the test cases. The proposed method is evaluated on six test programs, and the results demonstrate that it significantly improves clustering separation and reduces clustering compactness. By selecting only the cluster center paths to construct the MF_CNNpro model, training and prediction costs are effectively reduced. Moreover, the use of MF_CNNpro and PSO to select representative individuals as the initial population greatly enhances the evolutionary efficiency of PSO. The proposed method outperforms traditional approaches in clustering, prediction, and test data generation. Specifically, the SC clustering method improves cluster separation (SP) by 0.021, reduces compactness (CP) by 0.054, and decreases clustering rate (CR) by 4.97%, thereby enhancing clustering precision. The MF_CNNpro model improves the IA metric by 38.2% and reduces the U-Statistic and MSE by 83.0% and 97.9%, respectively, optimizing prediction performance. The MF_CNNpro+PPSOpro method increases the path coverage success rate from 47.9% to 97.4% (a 103.3% improvement), reduces the number of iterations by 84.1%, and decreases execution time by 95.6%, significantly improving generation efficiency.

This study was conducted to investigate the intrinsic mechanisms underlying the teaching effectiveness and student experience of university interactive learning platforms supported by mobile technology. Grounded in the technology acceptance model (TAM) and contextualized within mobile learning environments, the research introduced interactivity, compatibility, self-efficacy, subjective norm, and perceived enjoyment as external variables. An extended model was thereby constructed to reveal the complete causal pathway from platform characteristics to students’ cognitive perceptions, behavioral intentions, and eventual learning outcomes. A questionnaire survey was administered to university students who had utilized such platforms, yielding 250 valid responses. Structural equation modeling (SEM) was performed using SmartPLS software, with reliability and validity tests as well as bootstrapping employed to verify the proposed hypotheses. The empirical findings confirmed all 14 hypotheses. External variables such as interactivity, compatibility, and self-efficacy were identified as key antecedents influencing perceived usefulness and perceived ease of use. The core TAM pathway (perceived ease of use → perceived usefulness → attitude toward use → behavioral intention → actual use) received strong empirical support. Ultimately, actual use was found to exert a direct and significant positive effect on both student satisfaction and learning outcomes. The results demonstrated that the pedagogical value of mobile interactive platforms is realized through the process of students’ technology acceptance. This study not only provides empirical evidence for the application and extension of TAM in the field of educational technology but also offers theoretical and practical implications for university administrators in optimizing digital education strategies, for educators in designing effective interactive learning activities, and for platform developers in enhancing system design.

Objective: The present study aimed to determine the relationship between resilience and turnover intention among emergency department nurses. Methods: This cross-sectional descriptive-correlational study was performed on emergency department nurses in hospitals of Guilan (North Iran). Simple random sampling was used. Data was collected using a questionnaire on personal and job characteristics, the Conner-Davidson Resilience Scale, and the Anticipated Turnover Scale. Data were analyzed using SPSS V. 16 software and statistical tests. Results: The study, mean of resilience and turnover intention were 52.84±21.97, and 37.20±15.46, respectively. Nurses reported average resilience and higher than average turnover intention. Resilience among nurses was related with gender and work shift (P=.01). Resilience and turnover intention among nurses had a statistically significant and inverse correlation (P=.0001, W=-0.40). Conclusion: According to the results, increasing resilience among nurses can reduce turnover intention, and resilience is related to the type of work shift. Therefore, creating favorable conditions and reducing job stressors can promote a resilient environment and prevent nurses from leaving their service.

Large Language Models (LLMs) have demonstrated significant potential in transforming software testing by automating tasks such as test case generation. In this work, we explore the integration of LLMs within a Model-Driven Engineering (MDE) approach to enhance the automation of test case generation for smart contracts. Our focus lies in the use of Role-Based Access Control (RBAC) models as formal specifications that guide the generation of test scenarios. By leveraging LLMs’ ability to interpret both natural language and model artifacts, we enable the derivation of model-based test cases that align with specified access control policies. These test cases are subsequently translated into executable code in Digital Asset Modeling Language (DAML) targeting blockchain-based smart contract platforms. Building on prior research that established a complete MDE pipeline for DAML smart contract development, we extend the framework with LLM-supported test automation capabilities and implement the necessary tooling to support this integration. Our evaluation demonstrates the feasibility of using LLMs in this context, highlighting their potential to improve testing coverage, reduce manual effort, and ensure conformance with access control specifications in smart contract systems.

DevOps is a paradigm shift in software development today, aimed at the rapid delivery of software through a task-oriented approach to work and the integration of development and operations teams. Software testing is one of the most important and challenging steps in the DevOps pipeline because it should be effective and stable without being slow. To improve the process of software testing among DevOps professionals, this paper suggests a semi-automatic testing approach as a more viable solution between the extremes. The semi-automated methodology involves automation of processes that are tedious and time-consuming (such as unit testing, regression testing, and continuous integration), but human inspection in processes that require critical thinking, business domain knowledge, and exploratory insight (such as UI validation, business logic, and end-user behavior simulation). The combination creates the ability to do faster feedback loops, reduced test maintaining burden, improved test coverage, and quality assurance (QA) in general. The study utilizes a combination of case study evaluation, the implementation of the tools, and the reaction of the practitioner to the survey to gauge the performance of semi-automation in the DevOps context. The core performance indicators assessment of performance includes reduced test cycle time, defect leakage rate, productivity of the team, and the rate of deployment. The findings indicate that semi-automatic solutions result in radical improvements of test efficiency, scalability, and flexibility, especially in groups with dynamic codebases and limited automation budgets. The proposed study will integrate an affordable yet quality testing model that is dynamic enough to keep up with the DevOps and the advancement of the ideal balance between human judgment and automation. The method described here does not only enhance the effect of testing as an undertaking but also does the more universal DevOps objectives of quicker delivery, quality, and better teamwork.

BackgroundAmidst the rapid reshaping of the healthcare sector by New Quality Productive Forces (NQPFs), an advanced productivity paradigm is characterized by innovation-driven, high-tech, high-efficiency, and high-quality features. This study investigates young medical professionals (18–40 years). As the core workforce driving medical innovation, their professional identity and job autonomy directly influence the development of NQPFs.ObjectiveThis study aims to explore the relationship between perceived social support and professional identity among young medical professionals within the New Quality Productive Forces (NQPFs) framework, using a parallel mediation model to examine the independent mediating roles of role cognition and work autonomy. The findings are intended to generate hypotheses and provide a theoretical basis for future longitudinal research, which can inform the development of evidence-based policies.MethodsA total of 730 young professionals (aged 18–40 years) from 12 Grade A Tertiary Hospitals in Guangzhou, Guangdong Province, China, completed online questionnaires. Measurement instruments included Chinese versions of the Professional Identity Scale, Role Cognition Scale, Perceived Social Support Scale, and Work Autonomy Scale. Data analysis involved the use of descriptive statistics with SPSS software and structural equation modeling (SEM) for testing mediating effects via AMOS software.ResultsPerceived social support significantly influenced professional identity, with a total effect of b = 0.625. This comprised a direct effect of b = 0.374 (accounting for 59.80% of the total effect) and an indirect effect mediated by role cognition of b = 0.251. The mediating effect of work autonomy was non-significant.ConclusionThis study confirmed that perceived social support among young medical professionals directly influences their professional identity, while also indirectly affecting it through role cognition. The mediating role of work autonomy was not substantiated. These findings deepened the understanding of professional identity formation mechanisms in healthcare talent and highlighted the pivotal role of social support and role cognition within the New Quality Productive Forces (NQPFs) context.

AI agents demonstrate autonomy in planning, execution, analysis, and maintenance, offering potential to addresspersistent challenges in quality assurance such as maintenance overhead, lengthy debugging cycles, and limited predictivecapacity. To structure the investigation, seven core agentic use cases were defined, covering the full lifecycle of test automation.A comparative evaluation of three SaaS platforms—KaneAI, Zephyr Scale Automate, and TestRigor—was conducted using adual framework that combines quantitative scoring with qualitative assessment. This approach enables an integratedunderstanding of both the maturity of support for agentic functionality and the qualitative depth of its implementation,contributing to ongoing discourse on the evolution of software testing practices.

Relevance. Given the rapid development of software engineering practices and the need for cost-effective quality assurance in competitive environments, the relevance of developing strategic planning approaches for combined testing is growing steadily. The object of research is the strategic planning process for combined software testing that integrates multiple testing methodologies through systematic framework implementation, risk assessment, and resource optimization algorithms. Purpose of the article. This study explores strategic planning approaches for combined software testing and assesses their effectiveness across various application domains. The article aims to provide a structured framework for testing strategy integration and evaluate optimization mechanisms for resource allocation in complex testing environments. Research results. A comprehensive Strategic Planning Framework for Combined Software Testing (SPF-CST) was developed, consisting of six interconnected components: context analysis, multi-dimensional risk assessment, AI-driven prioritization, strategy selection, resource optimization, and monitoring systems. Empirical validation across eight industry case studies demonstrates a 35% reduction in defect leakage rates, 28% improvement in testing efficiency, and 45% decrease in regression testing costs. The study revealed that strategic planning significantly enhances testing effectiveness through systematic methodology integration and adaptive resource management. Conclusions. The study demonstrates the effectiveness of risk-based prioritization and mathematical optimization in testing strategy selection. The proposed framework provides practical tools for organizations to implement comprehensive testing strategies while managing resource constraints and project timelines.

The application of the Internet of Things (IoT) in greenhouses provides innovative solutions to enhance the efficiency and quality of chrysanthemum cultivation for the Primadona farmer’s group in Tomohon. The application of the automated system created is a condensation system that activates when the average temperature inside the greenhouse reaches 28 °C during the vegetative phase and 23°C during the generative phase, a drip irrigation system that turns on automatically when the average soil moisture value reaches 50%, as well as UV lights and exhaust fans that operate at night. The application of IoT also enables farmers to monitor and control greenhouse climate conditions in real-time using the Blynk application. The research method employed is experimental, incorporating a literature study to understand the application of IoT in greenhouses for chrysanthemum cultivation, as well as analysis of hardware and software requirements, system design, and system testing for real-world operations. The evaluation of the results provides insights into the effectiveness of IoT implementation in greenhouses for chrysanthemum cultivation, particularly for the Primadona Tomohon farmer group.

Prosthetic complications associated with implant-supported overdentures with the Locator system: A retrospective observational study.