The GOHIT platform is a web-based system designed to support the management of student competitions within higher-education institutions. The first version of the platform lacked structured requirements documentation, resulting in unclear user roles and inconsistent workflows. This study aimed to establish a lightweight yet disciplined requirements-engineering (RE) process for the redevelopment of GOHIT v2. Using a qualitative case-study approach, data were collected through semi-structured interviews and document analysis involving three key stakeholders: the founder, the developer, and the program advisor. Thematic analysis was applied to transform stakeholder inputs into structured, verifiable requirements based on IEEE 830 documentation principles. The process yielded seventeen functional requirements, each traceable to stakeholder sources and internally evaluated using IEEE 830 quality attributes. The resulting Software Requirements Specification demonstrated high levels of completeness, consistency, and traceability, confirming that a structured yet adaptable RE approach can be effective for small or academic development teams. This study contributes a replicable model for implementing lightweight requirements engineering in resource-limited settings and establishes a baseline for future validation and quality assurance using a test-based mechanism.

The development of e-commerce services requires a solution to facilitate the delivery of packages from couriers to users while maintaining package security. This research aims to design an automatic package receiving box using an ESP32 microcontroller as the control unit. The research method uses a prototyping approach through the stages of problem identification, prototype design, implementation, and function testing. The system is equipped with a GM66 sensor to scan barcodes as package validation, a load cell sensor to detect the presence of packages, and a Telegram application as a communication medium with users. The design process includes hardware and software requirements analysis, schematic creation, and draft design. When the system operates, the barcode is scanned by the courier and verified with the receipt number stored in the database, and the locker door will open automatically if the data matches. The test results show that the system functions properly, the GM66 sensor can read the barcode accurately so that the door lock solenoid can open the locker, and the message on the LCD can be read by the courier. This system shows potential in improving security and efficiency of delivery in e-commerce services.

Nonfunctional Requirements (NFRs) and User Experience (UX) are essential for developing robust, effective and user-centred software systems. However, these aspects are often addressed in a fragmented manner within Computing curricula, typically confined to specific subjects such as Software Engineering (SE) and Human-Computer Interaction (HCI). This article aims to map the content and competencies related to NFRs and UX across four key computing curriculum frameworks: the Brazilian Computing Curricula Guidelines (RF-CC-2017), the ACM/IEEE Computing Curricula 2020 (CC2020), the Software Engineering Body of Knowledge (SWEBOK v4.0) and the Software Engineering Competency Model (SWECOM 1.0). Using a structured mapping methodology supported by expert validation, the study reveals conceptual and educational similarities between the analyzed documents. The results suggest significant opportunities for interdisciplinary integration between SE and HCI in Computing education, emphasizing content alignment, practical competencies, and shared design principles. The findings offer theoretical and practical contributions to curriculum design by proposing concrete guidelines for cohesive, interdisciplinary integration of NFRs and UX in Computing programmes.

The development of software applications involves translating software requirement specifications (SRS) into structured models that guide system design. Among these, sequence diagrams are essential for visualizing dynamic interactions, but their manual construction from natural language descriptions is often error-prone and time-consuming. This study proposes an automated method for extracting sequence diagram elements namely classes, subclasses, and attributes from scenario sections of SRS documents. The approach leverages Natural Language Processing (NLP) techniques, combining Bidirectional Encoder Representations from Transformers (BERT) for contextual embeddings and Support Vector Machine (SVM) for classification. Noun phrases are identified and classified into UML-relevant entities using this hybrid model. To evaluate performance, two datasets SIData and SILo were used, each exhibiting distinct textual styles and domain characteristics. The system’s effectiveness was assessed using standard evaluation metrics such as precision, recall, and F1-score. Results indicate that the method is capable of capturing contextual relationships between extracted elements, although its performance varies across datasets, suggesting the need for further refinement. Overall, the study contributes toward automating early software design phases and reducing manual modeling effort.

Ensuring compliance with regulations poses considerable challenges for software development, particularly during the requirements specification phase. Traditional methods rely heavily on manual inspections that are time-consuming, and prone to errors. This research proposes an innovative framework that leverages the synergy of multiple AI agents to automate software requirement compliance verification partially. The framework integrates Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG) to analyze, detect, and revise non-compliant requirements. The core of our proposal lies in multi-agent communication, where distinct AI agents collaborate to achieve the overarching goal of compliance checking. LLMs comprehend requirements specifications, while prompt engineering guides LLMs towards compliance-related aspects. The RAG techniques detect non-compliant requirements and suggest changes. Finally, a robust Human-in-the-Loop mechanism ensures accuracy, reliability, and adaptability. A tool, available online, is implemented to translate the technology for effective application. We discuss its ability to identify non-compliant requirements in an extensive experimental evaluation.

Accurate classification of software requirements is a crucial task in Software Engineering (SE) that prioritizes development efforts and ensures the holistic quality of the system, encompassing both Functional Requirements (FRs) and Non-Functional Requirements (NFRs). While the majority of requirements classification research has so far focused on binary classification, fine-grained multi-class classification still encounters the challenge of extreme class imbalance in requirements datasets. To mitigate this limitation, we present TextAttack Oversampling (TAOS), a novel method that utilizes a Natural Language Processing (NLP)-based text augmentation technique to address this imbalance, thus reducing dependence on expensive expert labeling. An empirical assessment of a 12-class requirements dataset indicates that TAOS considerably outperforms standard classification techniques. Our approach achieves a 24% gain in F1-score, increasing from 0.75 to 0.93, and effectively improves the performance on minority requirement classes that were previously undetectable. This study demonstrates the effectiveness of context-aware text augmentation in improving multi-class requirements classification, thus providing a proven method to enhance the reliability and usefulness of automated requirements analysis and management tools for software engineers.

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.

ABSTRACT Requirements engineering is one of the most crucial parts of the lifecycle of software engineering. Many programs fail annually due to deficiencies in requirements engineering. Requirements engineering documents are written in natural languages, which can lead to ambiguities. The presence of ambiguity in natural language causes misunderstandings. Accurate and timely identification of these requirements is vital for the development process. However, manual classification is time‐consuming and necessitates automation. Today, with the rapid advancement of technology, machine learning and deep learning are being used to detect these ambiguities in requirement specification documents. The BERT word embedding technique and the Bi‐LSTM algorithm were used in this research. We have used meta‐heuristic algorithms to choose the best value of hyperparameters of our deep learning algorithm. The publicly available Fault‐Prone SRS dataset was utilized to train the models. This dataset was also used to evaluate the performance of the proposed algorithm in terms of F1‐score, accuracy, and other statistical metrics. The BERT‐BiLSTM model outperformed other models in classifying and detecting ambiguities in requirement specification documents, achieving an F1‐score and higher than 81% accuracy.

The need for IT specialists in radiation oncology - A position paper by the International Society for radiation oncology Informatics, endorsed by DGMP, SASRO, ÖGRO, ÖGMP, SRO and DEGRO.

Cloud-based machine learning for scalable classification of software requirements: Insights from the PROMISE dataset

Software requirements prioritization: IDOCRIW and QUALIFLEX approach

Artificial Intelligence (AI) has evolved from a niche research topic into a core enabling technology for modern enterprises. Recent advances in machine learning, deep learning, and large-scale data processing have enabled organizations to deploy AI-driven systems for forecasting, process automation, customer analytics, supply chain optimization, fraud detection, and decision support. However, the translation of AI potential into real, measurable business value remains uneven across industries and organizations. This paper presents an extended study of AI in business, integrating conceptual foundations, system architectures, imple- mentation methodologies, algorithmic techniques, and industrial case studies. We begin with an overview of key AI capabilities relevant to enterprises and motivate adoption using economic and strategic arguments. We then formalize the problem of enterprise AI adoption and examine the scope of technologies and functional areas involved. The paper further elaborates on technical methodologies, including supervised, unsupervised, and reinforcement learning; system design patterns; hardware and software requirements; and AI-specific SDLC and MLOps practices. We present detailed algorithm descriptions for commonly used models such as Random Forests, K-Means, Gradient Boosting, and Transformer- based architectures, and relate them to concrete business tasks. Additionally, we analyze real-world outcomes reported in the literature, discuss evaluation metrics and validation strategies, and identify key challenges such as data quality, scalability, bias, explainability, and governance. Finally, we outline future research directions including large language models, agentic AI, federated learning, edge AI, and trustworthy AI frameworks. The paper is intended as a comprehensive reference for students, practitioners, and decision-makers who seek to understand both the technical and managerial dimensions of AI in business.

Software requirement specifications (SRS) often contain repetitive, ambiguous, or inconsistent requirements, which drives up costs and delays project timelines. Because they mainly rely on syntactic similarity, traditional redundancy detection methods like TF-IDF and Word2Vec have trouble detecting semantic overlaps. This study proposes a semantic pruning framework that uses advanced NLP techniques, with a focus on transformer-based models like BERT, to find and eliminate superfluous requirements from SRS documents. Precision, recall, F1-score, and runtime were used as evaluation criteria to compare several methods, including CountVectorizer, TF-IDF, Word2Vec, and BERT. The findings show that deep learning models outperform conventional methods, which yield high precision but poor recall. Despite having a longer runtime, BERT outperformed Word2Vec with F1 = 0.87 and recall = 0.77. The outcomes show the effectiveness of transformer-based embeddings for re-dundancy detection and provide a scalable approach to improve SRS quality while reducing the amount of manual review effort

This research focuses on the development of a freelance e-commerce website for WardellTech using the Laravel framework. The objective is to build a web-based platform that is secure, responsive, and user-friendly, supporting freelance service transactions. Laravel was selected for its structured MVC architecture, built-in security features, and its ability to streamline development and maintenance. A Software Requirements Specification (SRS) document was created to define both functional and non-functional requirements in detail, ensuring that all stakeholders—developers, testers, and project managers—have a unified understanding of the system. WardellTech specializes in web and mobile application development, emphasizing quality design, user experience, and data security. Therefore, the website integrates key features such as secure payment gateways, intuitive navigation, and optimized performance. Continuous analysis and improvement of the user interface are also prioritized to enhance usability and customer satisfaction. The final system is expected to not only meet WardellTech’s business needs but also compete effectively in the freelance digital services market. Additionally, it is designed to adapt to ongoing technological advancements and evolving user demands in the dynamic e-commerce landscape.

This paper presents an ontology developed to address challenges such as com munication gaps, risks of errors, and inconsistencies during the manual process of creating software requirement specifications (SRS). The proposed ontology offers a systematic and formal depiction of the requirements, enhancing consis tency and communication among stakeholders. The ontology has been devel oped from the software requirements documents to facilitate the development process. This paper discusses the process of creating the ontology and demon strates using Pellet Reasoner for inference and Prot´eg´e for ontology construction to save and reuse information. The ontology seems to be efficient in manag ing complex software projects, enabling accurate requirement retrieval through SPARQL queries. This study emphasizes how incorporating ontologies into re quirement engineering can significantly enhance the quality and reliability of SRS.

Causal models for specifying requirements in industrial ML-based software: A case study

In the software development, high impact on diverse industries are expanding at a fast pace. Early defect fixation and adjustment according to needs are essential to increase the performance, accuracy, durability and reliability of the software. Machine Learning (ML) approaches have used to analyze the Software Requirement (SR) in software development offer a great way to accelerate symmetric development and defect correction, especially because software failures are unpredictable. This research utilizes classification and regression-based models like Random Forest (RF), Artificial Neural Network (ANN), and Adaptive Moment Estimation (AME) to forecast bug resolving times from applicable asymmetric data attributes. ML models are used to predict defect resolution and feature completion time. The data takes into consideration several stages including issue detection, testing, and validation. The outcome of ML models, K-Nearest Neighbors (KNN) algorithm has an accuracy of 66% but the proposed approach of RF, ANN, and ADE illustrates an astonishing 98% accuracy. The dataset with symmetric attributes has trends and strong correlations. The proposed model result is better than other available software failure time prediction in terms of precision and accuracy, providing a sound to defect solution and prediction in SR.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22193-x.

BackgroundDespite the potential for digital tools to facilitate guideline uptake, translating paper-based narrative guidelines into digital formats is resource-intensive and may compromise the fidelity to the recommended content. The World Health Organization (WHO) launched the SMART Guidelines initiative, in which digital adaptation kits (DAKs) are a foundational component. DAKs comprise software requirements documentation, including detailed data dictionary and algorithms--derived from WHO guidelines =for encoding within digital systems.MethodsThis implementation research consists of a formative assessment and impact evaluation on integrating DAKs within national digital systems to improve service delivery outcomes for antenatal care (ANC), family planning, and HIV in two countries (Ethiopia and Ghana). The formative phase will assess the requirements to customize the DAKs to align with the national protocols and subsequently incorporate the localized DAKs’ content into the respective nationally endorsed digital systems: Bahmni in Ethiopia and DHIS2 tracker in Ghana. The impact evaluation will assess the effect of using the DAK-upgraded digital systems using pre–post designs in Ethiopia and Ghana. Primary outcomes of adherence to guideline recommendations will be assessed when digital systems incorporate country-adapted DAK content in comparison with the existing practice. Guideline knowledge questionnaires and in-depth interviews with software developers, health workers and facility managers will supplement the impact evaluation.DiscussionThis research represents one of the first impact evaluations focused on integrating DAKs into existing national digital systems and the effect on service delivery outcomes. The mixed-methods study design will provide learnings for future scale-up and replication across other countries. We expect final results to be available in 2026, and preliminary findings will be shared at relevant fora.Trial registrationhttps://www.isrctn.com/ISRCTN18394724. Registration date: 21 December 2022.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12961-025-01397-7.

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.

The Syariat Islam Office (DSI) of Banda Aceh plays a crucial role in supporting the strategic objectives of the Aceh Government in achieving its vision and mission. One of DSI’s primary tasks is to inventory the number of vehicle assets at the DSI office in Banda Aceh. The asset data is currently recorded manually in the form of archives and documents, and sometimes using Microsoft Excel. This process leads to inefficiencies in terms of space, security, time, and cost, making it difficult to retrieve the required vehicle asset data. Therefore, there is a need for the development of a Vehicle Asset Information System at the Syariat Islam Office. With this system, the vehicle asset data can be accessed anytime, anywhere, and from any computer. The system includes software requirements analysis, design, code generation, testing, and maintenance. This government vehicle asset management system is web-based, using MySQL as the storage medium and PHP as the programming language. The outcome of this system is the ability to process information related to vehicle assets, such as managing vehicle asset data comprehensively. Through this application, it is expected that staff will become more familiar with and utilize a more efficient information system.