Context: The growing complexity of modern web and mobile applications, shaped by rapid UI/UX advancements and agile software development practices, demands efficient and automated recognition of graphical user interface (GUI) elements, as manual testing remains costly and time-consuming. Objective: This study aims to evaluate the performance of the YOLOv8 deep learning architecture for accurate recognition of diverse GUI components, such as buttons, fields, headings, links, and images, from real-world application screenshots. Method: A small YOLOv8 model was trained on the Roboflow GUI element detection dataset containing over 1,000 annotated website screenshots, using 35 training epochs, a batch size of 8, and an image resolution of 640×640. Results: The model achieved measurable improvements, with precision rising from 0.368 to 0.454, recall increasing from 0.296 to 0.425, and mAP50–95 improving from 0.101 to 0.232. Strongest detection was observed for buttons and input fields, while weaker performance was noted for iframes and labels due to their inherent ambiguity. Conclusions: The results demonstrate that YOLOv8 has significant potential in automating GUI recognition, reducing reliance on manual testing in agile workflows, and improving interface validation. Further optimization with larger datasets and advanced augmentation methods is recommended to enhance robustness and generalization

The implementation and deployment of machine learning models for the diagnosis of dominant illnesses among students require significant investment in technology and infrastructure, which is among the barriers for healthcare organizations with limited resources. In order to increase its adoption, this research suggests the creation of a Bi-Model Machine Learning Driven Application that will enable university students to get diagnosed with common ailments. The plan is to apply a high-level model using a hybrid methodology that combines the development of Machine Learning Models with Agile Software Development. In order to do this specifically, Python was used to implement exploratory data analysis, classification, and regression models, as they have proven to be highly effective in both diagnosing the primary illness and predicting the length of hospital stay. The bi-model were built with four different algorithms each, so as to adopt the ones with best performance for the deployment. The model built with Gradient Boosting Classifier has 100% accuracy, 100% precision, 100% recall as compared to other three algorithms through three repeated training of the model. On the prediction of admission duration task, Gradient Boost Regression works best, and this is because it has the least Root Mean Square Error of 0.57 and Mean Absolute Error to be 0.423 among other compared three algorithms, as measured. This was achieved through the use of fresh localized dataset from the Federal University Lokoja Health Center, which was pre-processed, and stored in the file manager/internal storage for visualization and modelling. Furthermore, the completed models was deployed to a web application using flask and Mysql Lite Database. In the end, the application reduced human error in diagnosis and care management of the student population while they are pursuing their education by enabling evidence-based awareness, educated public health policy, and individualized treatment.

This paper proposes a hybrid collaborative framework to optimize technology selection in Small and Medium-sized Enterprises (SMEs) by integrating machine learning (ML) predictions with Planning Poker, consensus-based estimation technique used in agile software development. Addressing known challenges such as cognitive bias, resource constraints, and the need for inclusive decision-making, the proposed model combines data-driven suitability analysis with stakeholder-driven consensus. ML generates quantitative, criterion-wise suitability scores based on historical SME data, providing transparent baselines for evaluation. Stakeholders independently assess candidate technologies using Planning Poker, and their consensus is blended with ML predictions through a flexible weighting mechanism. An illustrative case study on CRM tool selection illustrates the framework’s practical advantages: improved decision accuracy, transparency, and greater stakeholder engagement. The methodology is iterative, allowing for continuous learning and adaptation as new data emerges. This dual approach ensures that technology adoption decisions in SMEs are both empirically validated and contextually robust, offering a significant improvement over traditional, siloed methods.

In agile software development, defining functional requirements is essential for effective planning and implementation. This work proposes an artificial intelligence (AI)-based model to automatically classify user stories according to their feasibility for AI implementation, enabling the creation of an intelligent product backlog. A feedforward neural network (FNN) was implemented and evaluated using data from 22 software projects. The model's performance was compared against traditional approaches such as logistic regression and support vector machines (SVM), as well as large language models like Gemini and ChatGPT. The FNN outperformed all other models, achieving an accuracy of 80.3%. Automating this task helps optimize backlog management, reduce operational costs, and improve overall system efficiency, contributing to more agile, intelligent, and adaptive software development processes.

This exploratory research aims to develop, validate, and implement a semantic ontology in Spanish for the detection of anxiety symptoms in written narratives by young Colombians aged 18 to 30. 430 young people responded to a questionnaire on sociodemographic data, six open-ended questions, and the DASS-21 scale, in an online form. A mixed methods design was used, implementing a text analysis study using natural language processing, a semantic ontology of anxiety symptomatology, and agile software development methodology. The procedure included six phases: 1) development and validation of open-ended questions on anxiety symptomatology to collect information from narrative texts, 2) data collection, 3) ontology construction, 4) ontology validation through interjudge analysis, 5) software development and application, and 6) Efficacy of software for automatic symptom identification. The software developed in this project proved to be effective in its intended task of accurately identifying symptoms in both clinical and non-clinical participants, with more than 80% agreement with clinical experts. The results showed that the majority of symptoms had higher percentages in clinical participants compared to non-clinical participants.

Role of Acceptance Criteria and Developer Expertise in Enhancing the Quality of Robustness Diagrams in Agile Software Development

It all starts with structure: investigating learning dynamics in large-scale agile software development

This study developed the Psychosocial Support Management System (PSMS) to strengthen accessibility, efficiency, and confidentiality in providing mental health services within a State University. Using a descriptive-developmental research design, the system was created through the Agile Software Development Life Cycle (SDLC) framework with PHP and MariaDB. It features secure online request forms, structured case management, analytics, and Advanced Encryption Standard (AES) encryption to ensure compliance with the Data Privacy Act of 2012 (RA 10173). Usability was evaluated through standardized instruments, which revealed that both system users and clients perceived the platform as highly usable, reliable, and easy to navigate. Findings indicated that the PSMS effectively streamlined psychosocial service processes, improved record-keeping, and facilitated evidence-based decision-making through its analytics dashboard. Moreover, the integration of encryption and access control mechanisms ensured the protection of sensitive information and promoted user trust in the system. Overall, the PSMS demonstrated its potential as a dependable digital platform for managing university-based mental health services. It is recommended that the system be adopted institution-wide and continuously improved through enhancements such as automated notifications and multilingual support to expand accessibility and promote sustainable implementation.

Evolution and Current Trends in Agile Software Development Methodologies: A Comprehensive Analysis of Industry Adoption and Practices

This chapter provides a comprehensive examination of the synergistic relationship between serverless computing and event-driven architectures (EDA) in fostering agile software development within the evolving landscape of distributed computing. It begins by defining the core principles of serverless computing, including its "no server management" paradigm, pay-for-value billing, automatic scalability, and inherent fault tolerance. Subsequently, it delves into the foundational concepts of EDA, outlining its components, messaging models, and architectural topologies. The chapter then explores how the convergence of these two paradigms significantly accelerates development cycles, reduces operational overhead, enhances developer productivity, and enables dynamic scalability and cost efficiency, particularly within microservices and CI/CD frameworks. Real-world applications across various domains, from web backends to IoT, are highlighted to illustrate their practical impact. Finally, the report addresses critical challenges such as cold starts, vendor lock-in, debugging complexities, and security in a shared responsibility model, while also forecasting future directions, including AI-driven orchestration, serverless at the edge, and hybrid cloud strategies. This analysis aims to offer a rigorous academic perspective on how serverless EDA empowers organizations to navigate the complexities of modern distributed systems.

AI4SWEng, a Horizon Europe project that will be active from 2025 until 2028, unites 15 leading partners across the European Union, Switzerland and Turkey, combining experts in Model-Driven Software Engineering and trustworthy AI, with a special focus on applying Large Language Models. The project addresses complex challenges in industries such as healthcare, cyber-physical systems, and electric vehicles, focusing on multi-architectural and resource-constrained systems. Our mission is to transform agile software development by leveraging AI to boost efficiency, reliability, and security while ensuring ethical and regulatory compliance. The goal is to deliver scalable, sustainable, and socially responsible solutions that accelerate time-to-market without compromising quality. The AI4SWEng project will deliver an AI-powered software engineering suite providing end-to-end support for the software lifecycle, from code generation and advanced debugging to security, energy efficiency, and project management. By reducing pain points and enhancing productivity, the suite/platform aims to reduce developer stress, foster creativity, and improve job satisfaction. With a commitment to user-centred design and advanced prompt engineering, we empower developers to harness the full potential of AI. In this paper the AI4SWEng project consortium presents a novel and strategic approach proposed by the project consortium that will shape the future of software engineering, driving innovation and paving the way for more agile, intelligent, and sustainable software development.

While it is well known that organizational routines guide actions in practice, the prospective dimension of this guiding function remains underexplored. We examine the prospective dimension of routines through an ethnographic study of Scrum software development teams. Our findings show how actors continuously (re)create a tentative realm of possible paths in what we refer to as provisional directionality. The findings describe how provisional directionality is continuously (re)created through three patterning mechanisms: initiating possible paths, including or excluding possible paths, and reorienting possible paths. Provisional directionality contributes to routine dynamics research by, first, unpacking the prospective dimension of patterning in routines, second, emphasizing temporality in routine performances as flow, and, third, showing how heavily scripted routines enable action when it is unclear how to proceed. We also discuss how provisional directionality may inspire future research on routines in contexts such as product and service innovation, agile software development, and emergent strategizing.

Abstract Agile software development (ASD) emphasizes iterative development, continuous feedback, and team collaboration, addressing the limitations of traditional methodologies. This research explores the application of machine learning (ML) to improve story point estimation in ASD, a critical practice for planning and prioritization. Traditional methods like Planning Poker often suffer from human biases and inconsistencies, leading to unreliable estimates. This study introduces an innovative ML-based ensemble stacking technique, combining RoBERTa, a transformer model for natural language processing, with BiLSTM, a neural network adept at handling sequential data. The research involves reviewing existing ML methodologies, developing the proposed model, and evaluating its effectiveness using 21,064 data points from 14 open-source projects. The model’s performance was assessed through Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results show that the proposed ensemble model achieved lower MAE and MAPE, with performance improvements ranging from 4% to 32% over state-of-the-art models. While promising, the study suggests there is still room for further refinement, indicating the potential for ongoing advancements. This research contributes to the integration of ML in software engineering, offering a path toward more accurate and efficient project management.

Traditional physical door locks have various limitations, including the risk of loss, unauthorized duplication, and the inability to log access in real time. In a dynamic work environment such as PT Ekasa Teknologi Nusantara, this poses potential security risks to valuable company assets. A more flexible, efficient, and digitally monitored security system is therefore required. This study aims to design an Internet of Things (IoT)-based Smart Door Lock system capable of automatically recording and managing access. The method used involved observation of employee access patterns and the Agile software development approach, consisting of planning, design, development, testing, deployment, review, and launch phases. The system was developed using an ESP32 microcontroller, RFID module, and solenoid lock, all integrated with a user interface for managing access data. Testing results showed that the system can log entry activity in real time, send notifications, and provide management with flexibility in granting or revoking access rights. Thus, the system enhances company asset security, reduces reliance on physical keys, and offers an adaptive digital solution for workspace access control. The system also has potential for further development to meet organizational needs.

Teamwork in agile software development: A mixed-method study of gender diversity and collaboration dynamics

RAIDAD: A model-driven framework for automated and agile development of IoT data analysis software

Agile software development method cargo cult - Devising an analytical tool

This research examines leader–team interactions in agile software development within a global context, with a focus on effort estimation. Drawing on principal–agent theory, we analyse the interaction on the assumption that the Scrum Master guides the development team under imperfect information. We model the interaction as a sequential game with incomplete information. In the first stage, the Scrum Master allocates resources to the development team; in the second stage, the team exerts effort. Both parties are characterised by types that capture their knowledge and skills. As these types are private information, we derive the Bayesian Nash equilibrium to determine the equilibrium effort levels.

This study examines communication challenges in distributed teams operating within the Scrum framework. To enhance communication effectiveness, Berlo’s model was adapted. An instrument was developed and validated by experts through surveys and interviews with professionals experienced in agile project management. The instrument was subsequently applied to 44 practitioners across seven countries, assessing its capacity to improve communication and optimise the flow of information between teams and stakeholders. The findings confirmed that the adaptation of Berlo’s model is effective in addressing communication challenges in distributed teams. In conclusion, the implementation of Berlo’s model in Scrum teams reinforces clarity and cohesion in communication, thereby contributing to the success of agile projects in global environments.

The need to increase the efficiency and effectiveness of software development in the face of rapidly changing digital market dynamics continues to be crucial. In the era of rapid digitalization, TWD is required to produce high-quality products and services with shorter development cycles. Therefore, the adoption of the Scrum framework as an agile methodology becomes vital. This research examines in-depth the maturity level of software development processes using the Scrum Maturity Model (SMM) at Tribe Wholesale Digitization (TWD), a strategic unit at PT Telkom Indonesia (Persero) Tbk. This research employs a quantitative approach with a questionnaire survey method as the primary data collection instrument. The questionnaire is designed based on the dimensions in SMM, which cover various aspects such as basic Scrum management, software requirements engineering, customer relationship management, and performance management. The respondents in this research are individuals directly involved in the software development teams at TWD, including Product Owners, Scrum Masters, development teams, and other supporting roles such as Researchers, Designers, Frontend Developers, Backend Developers, Quality Assurance, and Document Engineering. The collected data is then analyzed using descriptive and inferential statistical methods. Descriptive statistical analysis is used to describe the characteristics of the respondents and the maturity level at each SMM level. Meanwhile, inferential analysis such as point-biserial correlation, Bivariate Pearson correlation, and Alpha Cronbach reliability test are used to test the validity and reliability of the instruments and to identify relationships between variables. The research results show that the maturity level of software development processes at TWD varies at each SMM level. At level 2, it was found that most teams have adopted basic Scrum practices. However, at levels 3, 4, and 5, several challenges and areas for improvement were found. Some of these challenges include an uneven understanding of roles among team members, issues of traceability (transparency) in the development process, and the rigidity of the organizational structure that hinders flexibility. This research concludes that the application of SMM provides a useful framework for evaluating and improving the maturity level of software development processes. The findings of this research are expected to be valuable input for TWD and PT Telkom Indonesia in general in their efforts to improve Scrum practices and achieve a higher level of maturity. Furthermore, this research also provides theoretical and practical contributions to the study of agile software development and the implementation of Scrum in the context of large organizations.