The National Board for Technical Education (NBTE) recently unbundled the Computer Science programme in the Nigerian Polytechnics into four options at the Higher National Diploma (HND) Level. These options are Software and Web Development, Cyber Security, Artificial Intelligence, Networking and Cloud Computing. The prospective students of HND for the options are at crossroad with the challenges of selecting the best or suitable option amongst the four available options. This challenges is caused as a result of inadequate knowledge about the prospects and professional skills/requirements that are abound in each of the options. It is in view of this that this research modelled an expert system that guides the prospective students of HND in decision making of selecting a suitable career option, with the adoption of expert system architecture and components, where the prospects and professional skills/requirements are domiciled for prospective students for adequate knowledge and guidance for right decision of career choice/option of the unbundled Computer Science programme. The development was carried out on a system running Microsoft Windows 7 Ultimate platform/Microsoft Windows 8 platform. Being a responsive web application, the client-side/user/web interface pages were encoded and developed using “Bootstrap 3” front-end framework for (cross-platform/responsive-web). The “Bootstrap 3” contains HTML5 (Hyper Text Markup Language), CSS3 (Cascade Style Sheet), and JavaScript. For the back-end, XAMPP was used as web server with support for PHP as a scripting language and MySQL for working memory functional database. Keywords: Domiciled, Expert System, Prospective, Unbundling. XAMPP Journal Reference Format: Alao Kazeem Akinbola & Iyanda Waleola Samsondeen (2026): Development of a Web-Enabled Expert System for Career Path Guidance of the Unbundled Computer Science Programme in the Nigerian Polytechnics. Journal of Behavioural Informatics, Digital Humanities and Development Research. Vol. 12 No. 1. Pp. 7-32. https://www.isteams.net/behavioralinformaticsjournal dx.doi.org/10.22624/AIMS/BHI/V12N1P2x

Classroom management in Indonesian schools faces inefficiencies from Microsoft Excel-based scheduling, causing data errors and delays for administrators and teachers, particularly digital immigrants. This study aims to design a desktop-based classroom management system using VB.NET and MySQL to enable real-time CRUD operations for students, teachers, courses, scores, and printing. Employing a software development approach with the waterfall model, the population consists of 50 potential users (10 administrators, 40 teachers) at Surabaya State University and partner schools; purposive sampling selected 5 key respondents (2 admins, 3 teachers) with 2+ years manual experience. Instruments include needs analysis documents, flowcharts, ERD, prototypes, and black-box testing sheets; data analysis used qualitative-quantitative triangulation validating functional/non-functional requirements. Results demonstrate 100% validity across 36 scenarios in 7 modules (login, dashboard, student/course/score/teacher management, print), confirming system reliability via Human Centered Design. In conclusion, the system enhances administrative productivity, serving as a practical foundation for digital transformation in education, although limited to desktop use.

Exploring the impact of personality traits and cognitive diversity on knowledge-sharing behaviour, team innovation climate, and team agility in Agile Software Development

Objective . The aim of the study is to develop an algorithm and software for evaluating alternatives based on the additive convolution method of criteria. Method . The additive convolution method allows summing up individual criteria based on their importance, forming a single integral value; the calculation algorithm includes sequential steps, normalization, determination of weights, and integration of the obtained data; the developed software product, implemented in the Python programming language using the Tkinter library, provides a convenient graphical representation of the result. Result . The stages of the method implementation are presented, including normalization of criteria, determination of importance weights, and the convolution procedure that combines different indicators into an integral indicator. The algorithm describes the calculation functions and provides a code listing. An algorithm for software development is presented. An example of applying the method to assess the effectiveness of antihistamines is given. Conclusion . The program allows calculating integral indicators and finding criterion weights using a pairwise comparison matrix, as well as presenting the result in the form of a graph. The proposed method and the developed software package represent an effective tool for decision-making with the selection of the optimal alternative. Using the software product simplifies the process of comparing and analyzing a large number of factors, increasing the accuracy and objectivity of selection.

This paper examines the rise of high-skill migration from Asia to the United States since 1990 and its consequences for sending and receiving economies. Over 1990–2019, migrants from India, China, South Korea, Japan, and the Philippines accounted for over one-third of US growth in software developers and a quarter of the increase in scientists, engineers, and physicians. Using census microdata, visa records, and administrative sources, I show how growing US demand for talent in information technology, higher education, and healthcare interacted with Asia’s demographic and educational transformations. Policy reforms in the H-1B, F-1, and J-1 programs and sectoral shifts—such as the internet revolution and aging-related healthcare demand—generated persistent needs for foreign students and workers. Asian economies were uniquely positioned to meet this demand through tertiary expansion, strong STEM institutions, English proficiency, and diaspora networks. These inflows boosted US innovation while fostering “brain gain” and “brain circulation” in Asia.

This qualitative study analyzes barriers to Total Quality Management (TQM) implementation affecting programmer work quality at PT XYZ, a software development company certified with ISO 9001:2015. Using a descriptive case study approach, data were collected through in-depth interviews with seven informants and supported by secondary data including QA reports, internal audit results, SDLC audits, and training records from 2021–2025. Data analysis applied the Miles and Huberman model, supported by Fishbone Diagram and SWOT analysis. The study focuses on four TQM dimensions: management commitment, employee involvement, training and development, and continuous improvement. Findings indicate that TQM implementation barriers are interrelated, including weak enforcement of quality standards due to deadline pressure, limited employee involvement, inconsistent training programs, and ineffective PDCA cycles. These barriers negatively affect programmer work quality, reflected in recurring CAPA findings, increased audit non-conformities, high bug rates, declining code quality ratings, and skill gaps among programmers. The study recommends strengthening management commitment, enhancing employee involvement, establishing continuous training programs, and optimizing systematic continuous improvement practices to sustainably improve programmer work quality.

KIBO, a new hybrid software development method with enhanced information systems auditing capability

Increasing developers’ code accountability perceptions in open source software development

Best practices for work from home: A qualitative survey in open source and distributed software development

Abstract Low-temperature plasmas (LTPs) are essential to both fundamental scientific research and critical industrial applications. As in many areas of science, numerical simulations have become a vital tool for uncovering new physical phenomena and guiding technological development. Code benchmarking remains crucial for verifying implementations and evaluating performance. This work continues the Landmark benchmark initiative, a series specifically designed to support the verification of LTP codes. In this study, seventeen simulation codes from a collaborative community of nineteen international institutions modeled a partially magnetized E × B Penning discharge. The emergence of large scale coherent structures, or rotating plasma spokes, endows this configuration with an enormous range of time scales, making it particularly challenging to simulate. The codes showed excellent agreement on the rotation frequency of the spoke as well as key plasma properties, including time-averaged ion density, plasma potential, and electron temperature profiles. Achieving this level of agreement came with challenges, and we share lessons learned on how to conduct future benchmarking campaigns. Comparing code implementations, computational hardware, and simulation runtimes also revealed interesting trends, which are summarized with the aim of guiding future plasma simulation software development.

Communication factors and practices in distributed software development projects

Application Programming Interfaces (APIs) have become the backbone of modern software applications, enabling communication between heterogeneous systems such as web, mobile, and cloud-based applications. As APIs play a critical role in data exchange, their correctness, reliability, and performance are essential for overall software quality. This research paper presents a practical study of API testing using the Postman tool. The paper explains the fundamentals of API testing, different HTTP methods, and testing techniques applied using Postman. A real-time case study is conducted on a RESTful API to demonstrate request execution, response validation, and automation capabilities. The study highlights the effectiveness of Postman in detecting defects early, reducing manual testing effort, and supporting Agile and DevOps practices. The results show that Postman is a cost-effective and user-friendly tool for ensuring API quality in modern software development

Software testing is a critical phase in the software development lifecycle that ensures the delivery of reliable, defect-free software. With the increasing complexity of software systems, traditional testing methods are no longer sufficient to meet quality requirements. This paper presents a comprehensive survey of modern techniques in software testing, including automated testing, model-based testing, mutation testing, and machine learning-aided testing. We compare these approaches in terms of effectiveness, efficiency, and applicability across different software domains. Finally, we discuss open challenges and future research directions.

Large Language Models (LLMs) such as GPT, LLaMA, and PaLM have transformed the field of Natural Language Processing (NLP) by achieving remarkable results in text generation, summarization, translation, question answering, and dialogue systems. Their wide adoption across industries highlights their usefulness but also exposes a critical limitation—hallucination. Hallucination occurs when models generate information that is false, misleading, or fabricated. These errors can vary from small factual mistakes, like incorrect dates or figures, to serious inaccuracies that may cause harm in sensitive areas such as healthcare, education, and software development. This paper explores the concept and classification of hallucinations in LLMs, examines techniques to reduce them—including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG)—and discusses ethical implications and real-world applications. By comparing multiple strategies, the study aims to contribute to developing more reliable and trustworthy AI systems.

A Programming Paradigm is the silent intelligence in any software design. Although many Programming Paradigms have evolved, only a few programming paradigms are actively used by the software industry. In addition, many hundreds of programming languages have been developed, but only a few are established and beneficial. The main aim of this paper is to provide an in-depth view into this area in order to give an opportunity for the Academia, Researchers, and the Software Industry to understand this domain in a different way. Basically, in this paper, a lot of relevant literatures have been reviewed and some useful facts, such as mainstream programming paradigms, suitable programming languages for the current software development scenario, weaknesses in the current research works in this domain, etc., have been derived as conclusions. The deduced facts would be beneficial for the education sector to decide the programming paradigms and programming languages to teach at this juncture, and as for the researchers, this paper would provide an alternative road map to conduct further research in this domain. Eventually, this work would benefit the software designers to choose appropriate programming paradigm concepts and their respective programming languages based on the deduced facts as the result of this study.

This paper examines the influence of ease of use, data security concerns, awareness, the cost of digital applications, and the technical support on the adoption of digital applications for financial planning in small and medium size enterprises (SMEs) in Kathmandu valley. This study has applied quantitative cross-sectional research methods for collecting quantitative data from 67 SMEs inside Kathmandu Valley with a sample size of 165 respondents associated with the account/finance department and involved in financial planning and decision-making. Results of this study indicate that ease of use and technical support availability are the predictors of digital applications adoptions among SMEs, whereas cost of digital applications data security and privacy concerns, and awareness are barriers to implementation of these applications for financial planning. Based on the findings, this study suggests software developers, suppliers, dealers and service providers of digital applications used for financial planning and management to prioritize user-friendly, easy-to-use features; focus on cost-effective applications; design training and awareness programs; and ensure access to adequate technical support to maximize adoption of their products among SMEs in the Kathmandu Valley.

Artificial intelligence is increasingly embedded in engineering team workflows; yet, sustained use often weakens after initial uptake, resulting in patterns of superficial compliance, concealed workarounds, or complete discontinuance. This article aims to explain post-adoption trajectories by developing a conceptual model of organizational barriers that shape long-term use of artificial intelligence tools in engineering work, with software development and coding assistants used as an illustrative domain. Based on a structured synthesis of recent scholarship on technology adoption, trust in artificial intelligence, cognitive load, and employee resistance to digital transformation, the study derives an integrative framework that links individual evaluations to organizational conditions. The model specifies five constructs—usefulness, trust, challenges (technical, cognitive, emotional, and process-related), organizational influence, and disengagement triggers—and articulates how their interaction shifts behavior from sustained engagement to minimal or avoided use. As a result, the article (i) proposes a barrier taxonomy relevant to engineering teams, distinguishing infrastructure and integration constraints, cognitive–emotional strain, procedural and regulatory misalignment, cultural and power dynamics, and managerial–strategic inconsistency; (ii) maps these mechanisms onto established adoption logics to show where intention-focused explanations fail to capture withdrawal dynamics; and (iii) formulates research propositions describing how organizational practices moderate the link between perceived value and real usage, and how status, identity, autonomy, and perceived fairness threats catalyze disengagement. To facilitate empirical verification, the paper outlines a planned mixed-methods design that combines large-scale secondary survey evidence on developer tool use with primary qualitative data from engineers and engineering leaders to reconstruct post-adoption pathways and identify disengagement triggers in situ. The article is intended for researchers studying technology adoption and organizational behavior, as well as for engineering managers, transformation leaders, and governance functions seeking to design workflows, policies, and incentives that support the durable, auditable, and trusted use of artificial intelligence.

Conventional uses of building information modeling (BIM) in existing-building representation tend to prioritize geometric consistency and efficiency, but often at the expense of interpretive depth. This paper challenges BIM’s tendency to promote epistemic closure by proposing a method to foreground relational ambiguity, transforming view reconciliation from a default automated process into a generative act of critical inquiry. The method, implemented in Autodesk Revit, introduces a parametric reference frame within BIM sheets that foregrounds and manipulates reciprocal relationships between orthographic views (e.g., plans and sections) to promote interpretive ambiguity. Through a case study, the paper demonstrates how parameterized view relationships can resist oversimplification and encourage conflicting interpretations. By intentionally sacrificing efficiency for epistemic resilience, the method aims to expand BIM’s role beyond documentation, positioning it as a tool for architectural knowledge production. The paper concludes with implications for software development, pedagogy, and future research at the intersection of critical representation and computational tools.

Abstract. This study introduces a scalable, cloud-native, no-code Geographic Information System (GIS) platform tailored for large-scale spatial decision-making in logistics operations. The platform aims to enable non-technical users such as field operators and branch managers—to design, deploy, and visualize spatial queries and dashboards without requiring software development skills. Its architecture integrates a multi-layered microservices framework with a PostGIS backend and supports dynamic visualization of millions of spatial points in real time. Advanced optimization modules, such as clustering (k-means, DBSCAN) and locationrouting algorithms (p-median, set covering), are encapsulated as configurable plug-ins within the platform. Designed and tested in a national courier network, the system addresses major technical challenges such as query performance on fragmented big data, visual responsiveness under heavy load, and inter-database integration via engines like Trino. Experimental evaluations demonstrate sub-second response times for complex spatial aggregations over datasets exceeding 1.5 million records. The platform fosters improved operational visibility, reduced IT dependency, and faster decision loops in dynamic geospatial environments. The architecture and findings of this work present a practical contribution to enterprise-grade GIS systems and open new research directions in user-centric geospatial analytics.

AI-driven coding assistants are transforming software development, yet their full potential in Integrated Development Environments (IDEs) remains underutilized. A key challenge is their tendency to hallucinate, producing plausible but incorrect code and leading developers down unintended paths. Current static file-based IDEs also lack support for tracking the provenance of AI-generated code or integrating version control in ways that match the dynamic and iterative nature of AI-assisted workflows. Consequently, developers lack tools to systematically manage, refine, and validate Generative AI (GenAI) code, making correctness, maintainability, and trust difficult to ensure. Inspired by the art of Japanese Bonsai gardening—emphasizing balance, structure, and pruning—we propose a new paradigm: an IDE where AI is free to generate, and developers guide evolution by pruning and shaping alternatives. We present BonsAIDE, a prototype that supports branching, comparison, and pruning of AI-generated code. In an initial study with ten participants, we observed: (1) diverse exploration strategies across identical tasks; (2) high tool acceptance with low perceived difficulty; (3) benefits of branching and pruning, including clutter reduction and parallel exploration; and (4) concrete feedback on desired features such as side-by-side diffs and improved navigation. These findings motivate future research on provenance, prompt-aware navigation, and scalable human–AI interaction.