Supply Chain Management (SCM) plays a vital role in business operations, necessitating careful designing and planning. Emerging technologies, such as the Internet of Things (IoT), facilitate real-time, event-driven monitoring of supply chain performance. However, integrating IoT into SCM presents significant challenges for system architects, particularly in validating requirements due to the diversity of IoT technologies and communication frameworks. Model-Based Systems Engineering (MBSE) offers a structured approach to system design by providing models and views, along with tools that automate essential processes. In this paper, we propose an MBSE-based approach for requirements validation in event-driven supply chain management. Additionally, we introduce tool support to enhance usability. An architectural overview demonstrates how system designers can customize the tool to meet their specific requirements. Our approach is designed for system architects, supporting iterative modeling until the requirements are validated as illustrated through a sample case study.
Data exposures in software services consist of inadvertent or malicious leakages of confidential or sensitive information in software services. These leakages in software services are commonly caused by hardcoded secrets, misconfiguration in storage, insecure logging, improper transmission of data or unsafe deserialization. Existing approaches to detecting data exposures in software services rely on using regular expression with limited capabilities of detecting data exposure, specialized static analyses, deep learning technique, or a large language model. These approaches frequently yield high false-positive results or sometimes produce different results for the same input due to indeterminism of the large language model used in the approach. In this paper, an approach to detecting data exposures in software services using a large language model with low rank adapters and few-shot learning technique is presented. This approach can reduce false-positive results and avoid indeterminism of the large language model used in the approach.
A service robot is an Internet of Things (IoT) or a cyberphysical system comprising a robotic body integrated with one or more Cloud-based services. This architecture enables sophisticated human-machine interaction and expands the functional capabilities of conventional robotic systems. A service robot can take the form of anthropomorphic, zoomorphic, or even theomorphic design. Our research team is collaborating with the Canadian National Institute for the Blind (CNIB) to develop a prototype service dog robot. Due to this, this paper presents our current experiences in designing, developing, and evaluating a Do-It-Yourself (DIY) articulated robotic tail toolkit for zoomorphic robots at Ontario Tech University. We detail the design process that led to the development of a robotic tail mechanism, driven by microservices, which emphasizes reliability and expressive while utilizing readily accessible components. We range also conducted experiments and evaluations of the robotic tail during a hands-on workshop with students from diverse backgrounds, using a services computing approach. We continue to develop an effective DIY educational tool that promotes student engagement in Human-Robot Interaction (HRI) design for future service computing education.
This study aims to guide pre-service teachers in effectively utilizing Generative Artificial Intelligence (GAI) tools to enhance their Artificial Intelligence (AI) literacy. By ana-lyzing the impact of GAI on the AI literacy of pre-service teachers, this study proposes a “teacher-student-machine” triadic interactive teaching model based on self-organized learning and deep empowerment through GAI. Using an experimental class in a software engineering course as a case study, we construct an end-to-end experimental environment for the project-driven teaching lifecycle, covering requirement analysis, system design, development and implementation, as well as software testing and maintenance phases. GAI is used to support the completion of experimental tasks while fostering pre-service teachers' AI literacy in five core competencies: data literacy, digital communication and collaboration, critical thinking, computational thinking, and ethical literacy. The study demonstrates that this model effectively improves the AI literacy of pre-service teachers, provides a reference for innovating software engineering practice teaching, and offers a useful framework for the application of GAI in education.
[Context]: It is well known that understanding the evolution of technologies and its cause is essential for more discoveries and innovations. In the Artificial Intelligence (AI) domain, it has also been identified that scrutinising the development context and path of AI will be able to help both academia and industry better understand the current AI limitations, reveal future AI trends, and facilitate AI/digital transformations. [Objectives]: Given the dramatic boom of modern AI, this research aims to unearth the evolution pattern along the recent three AI waves (namely predictive AI, generative AI and agentic AI), and accordingly to guide AI research and development to focus on the most promising directions. [Method]: We employed analogical reasoning as the research method and referred to the existing software architectural styles to inspire our understanding of the architectural evolution of modern AI technologies. [Results]: We see a service-oriented trend in modern AI's working mechanisms, and the offering of AI power seems to be transiting from a heavyweight and monolithic paradigm to an organisational and collaborative paradigm with more and more specific separation of concerns. Following this service-oriented evolution trend, we borrow software architecture lessons and foresee opportunities to grow the current AI wave to a further height, e.g., standardising AI agent-friendly APIs and developing serverless AI agents. [Conclusions]: What is happening in the AI domain has happened before in the software engineering domain. It is worth reusing software architecture knowledge to evolve the architecture of AI technologies.
Currently, there are various scientific and technological resource retrieval databases in the world. The resources stored in these databases may be identified by different identification systems. How to determine whether scientific and technological resources identified by different identification systems are the same resource is an urgent problem to be solved. This paper proposes a software service that leverages BERT and large language models to perform semantic analysis and similarity matching of scientific and technological resource content, and then maps the resources to their respective identifiers. The service effectively solves the problem of how to quickly retrieve the same resource from a large number of scientific and technological resources with diverse identification types, and improves the efficiency and quality of the retrieval. Implemented as a Chrome plugin, the service facilitates seamless mapping heterogeneous scientific and technological resources to their identifiers. We conduct a series of experiments. Their results demonstrate the effectiveness, scalability and stability of the service. To the best of our knowledge, we are the first to propose the service integrating BERT with large language models to extract and identify the key content of scientific and technological resources from web pages.
This research explores strategies for using one-way anonymous Q&A software to enhance class participation and teaching effectiveness of undergraduate students. The study finds that undergraduate students, influenced by cultural factors, ed-ucational systems, and upbringing environments, tend to display introverted psychological traits, resulting in insufficient class participation. One-way anonymous Q&A software significantly improves student participation enthusiasm through mechanisms such as breaking psychological barriers, promoting deep thinking, enhancing classroom interaction, and providing diverse feedback. Case analysis shows that by introducing the proposed anonymous Q&A software to teaching activities, students asked questions more frequently, and the quality of questions also improved accordingly with a wider adoption of the software. Besides, it also shows that the learning interests of students have increased after using the software. The research suggests that future studies should deepen technological innovation, pro-mote teaching model reform, and strengthen interdisciplinary integration to better meet the needs of a modern educational environment in universities.
While microservices architectures have significantly enhanced software modularity and scalability, they introduce unique security challenges that traditional monolithic approaches do not face. The proliferation of microservices often leads to homogeneity within systems, where multiple instances share iden-tical container images and software versions, creating a systemic security vulnerability that attackers can exploit. In homogeneous microservice environments, a single vulnerability can be exploited to compromise multiple services simultaneously, facilitating rapid lateral movement attacks and significantly reducing system re-silience against cyber threats. While existing security research has focused primarily on preventive measures like authentication protocols, the potential of strategic deployment patterns as a reactive defense mechanism remains largely unexplored. In this paper, we base our analysis on information theory to demonstrate that microservice deployment schemes affect system heterogeneity, which in turn influences overall security. Additionally, we proposed the Heterogeneous Microservices Placement Problem HMPP and developed a heuristic-based deployment algorithm, H- HMPP, to effectively address it. Extensive experimental evaluations demonstrate that our approach yields high-quality solutions while significantly reducing execution times.
Application Understanding task aims to help users comprehend an application's capabilities by systematically analyzing its artifacts. Ideally, such summaries should align with how the application is used in practice, highlighting essential workflows and functional modules in a structured manner. However, existing automated approaches often fall short of this expectation. Lack of application-specific background and domain knowledge limits the system's ability to present functionalities meaningfully. To address these challenges, we propose a novel agentic approach leveraging multimodal LLMs that integrate code analysis, textual artifacts, and domain knowledge to identify key business flow entities-such as programs and tables-within a repository and infer application workflows. This work opens new avenues in LLM-guided software comprehension, bridging the gap between code-centric insights and high-level business process understanding.
In the era of transformative artificial intelligence (AI), professional Master's programs must navigate both technological breakthroughs and pedagogical shifts. This paper presents an innovative approach adopted by the College of Software and Microelectronics at Peking University to train Electronic Information Master's students through a multi-dimensional framework encompassing academic research, industry applications, and in-service capacity building. By transitioning from code-focused instruction to a higher-level AI toolchain paradigm, the curriculum leverages large-model technologies (e.g., GPT, Chat-GPT) to promote greater efficiency in problem-solving, project-based learning, and critical thinking. The New Engineering Experimental Class exemplifies how enterprises partner with academia to guide real-world project cycles under dual mentor-ship, thereby accelerating technology transfer and strengthening student competencies in both theory and practice. In parallel, a three- tiered engineering practice system-ranging from course-embedded labs to full-scale industrial internships-amplifies hands-on development and integrates rigorous assessments of engineering proficiency. Furthermore, by incorporating cross-disciplinary mentorships, AI-enhanced campus tools, and cutting-edge lectures on new engineering and data-driven domains, the program produces graduates who are well-positioned in algorithm development, system architecture, and technology management. These reforms demonstrate an effective means of aligning educational supply with market demand, empowering students to address the evolving challenges of AI -driven industries while championing ethical and sustainable practices. The paper concludes by discussing the program's adaptability for continued innovation amid AGI-frontier breakthroughs and digital transformation imperatives.
Incident prediction is critical in industrial settings to prevent disruptions and optimize operations by anticipating failures. To this end, equipment logs are commonly utilized and converted into an XES-like log format, wherein each event is associated with a single case object (i.e., equipment) reporting its status. However, the resulting event log overlooks other data sources, such as logs of pre- and post-incident processes. These logs report activities applied to the equipment's related objects (e.g., hardware, software) in non-structred way, presenting challenges when using the XES-like log format. This paper introduces a meta-model to integrate and represent these diverse data sources in formalized format. To this end, we propose a domain-specific object log tailored to the incident-monitoring domain to represent multiple equipment-related objects. This meta-model was validated with a real-life dataset, showing how it provides an effective and structured representation of incident-related data.
Building robust data pipelines often requires spe-cialized engineering skills, creating barriers for domain experts with limited coding expertise. We introduce Prompt2DAG, a modular prompting methodology that transforms natural language descriptions into executable Apache Airflow workflows by decomposing generation into three sequential stages: structured analysis, configuration generation, and code implementation. This approach aligns with established software engineering principles of separation of concerns and progressive refinement. Our evalu-ation across five different LLMs demonstrates that Prompt2DAG significantly outperforms conventional end-to-end generation, im-proving code quality (+78.4 %) and structural integrity (+43.2 %) of generated pipelines. Using a data enrichment case study, we show how this approach enables the development of high-quality workflows through natural language, effectively democratizing data pipeline development.
As the complexity of business and scenarios contin-ues to grow, the traditional, inefficient, and cumbersome domain modeling process can no longer adapt to the rapid iteration requirements. In recent years, technological breakthroughs in generative artificial intelligence (Generative AI), particularly in large language models (LLMs), present novel opportunities to enhance domain modeling efficiency. While LLMs demonstrate baseline capabilities in information extraction, their potential for constructing complex domain-specific models remains underexplored. This study investigates how LLMs can facilitate automated domain modeling tasks and support domain modeling education. Through systematic experimentation, we evaluate the impacts of LLM fine-tuning techniques and prompt engineering strategies on model outputs, while comparing two distinct generation modes: indirect model construction via domain element extraction versus direct domain model generation. Our empirical results demonstrate that LLMs exhibit significant potential in supporting high-efficiency domain modeling, with fine-tuning techniques and indirect generation modes yielding superior out-comes. Furthermore, we illustrate LLMs' utility as pedagogical tools for domain modeling education while identifying critical limitations and implementation risks that warrant consideration in both practical applications and future research.
The rapid expansion of the Internet of Things (IoT) has brought transformative benefits across various domains and introduced significant security challenges, especially in resource-constrained edge gateways. This paper proposes an innovative Intrusion Detection System (IDS) powered by Deep Reinforcement Learning (DRL) to dynamically detect and mitigate network threats by selecting IoT security patterns. Leveraging adaptive IoT security patterns, the system addresses diverse attack scenarios (e.g., Distributed Denial of Service (DDoS), DoS GoldenEye, DoS Hulk, and Port Scanning) with significant efficiency. The system achieves an average detection accuracy of 97% and demonstrates reduced response times and efficient resource utilization, making it well-suited for edge gateways. The experimental evaluations validate the proposed model's ability to enhance security while optimizing CPU and memory usage, reducing energy consumption, and lowering carbon emissions. Furthermore, its adaptability to evolving cyber threats and alignment with green computing principles highlight its potential to support secure and sustainable IoT networks.
Software service discovery and recommendation help data scientists build scientific workflows - multi-step data analytics procedures - by automating the manual selection of services. Previous research shows that recommending chainable units of work (UoWs), rather than individual services, improves efficiency and reduces data shimming issues. However, UoW recommendation remains an NP-hard problem. To tackle this challenge, this study introduces a novel framework tailored to recommend UoWs in a goal-driven, context-aware manner, thereby facilitating workflow development. The framework is built around layered structure of software service social networks. At its foundation lies a service dependency network, where each edge represents a dependency between a two-service UoW within a specific context. The next layer abstracts each of these edges into a node, with new edges now representing three-service UoWs. This layering process continues iteratively, with subsequent layers capturing increasingly complex UoWs at higher levels of granularity. At high-order layers, UoW nodes are clustered based on their semantic embeddings, with each cluster represented by an intelligent agent. This approach transforms the workflow recommendation problem into a multi-agent collaboration task, where agents work together to identify high-level UoW groupings before refining selections by navigating down the layered structure for finer-grained recommendations. Experimental results over a real-world dataset confirm the effectiveness of the proposed framework in enhancing workflow composition efficiency.
Data and databases are the most important assets for enterprises today, driving informed decision-making, enhancing operational efficiency, serving as valuable intangible assets, enabling digital transformation, and mitigating risks through proper management and security. Organizations are working on solutions to ensure data security, integrity, and availability while enabling smooth data interoperability and convergence. However, data security of sensitive information remains a big challenge, particularly in the healthcare sector. Built on basic data security principles like hashes and encryption, blockchain is a distributed system that keeps identical copies across multiple nodes, therefore ensuring data consistency and integrity. In this paper, we explored the potential of integrating blockchain technology with contemporary relational database systems. We used Python and SQL to simulate and analyze the compatibility between blockchain data formats and regular relational databases. Using experimental scenarios within a RDBMS environments, we explored how blockchain's immutable and decentralized record system might increase data security and integrity when integrated with conventional database systems. This paper investigates the possibilities of combining blockchain's unchangeable and distributed document-retaining mechanism with the well-established structure of relational databases with an eye toward how blockchain and relational databases might be used together to improve the security and alignment of healthcare data. The aim of this paper is to discuss the issues in existing systems and to characterize both blockchain and relational database technologies. We explore blockchain data system-oriented techniques and scripting in the RDBMS environment to develop practical approaches to enhance data security and interoperability in healthcare systems.
Cloud platforms and open-source model repositories offer advanced AI services. These services are becoming essential components for building AI-enabled software. The opacity and lack of explanation have become new challenges to address within the software service lifecycle. Recent studies demonstrate that augmenting explainability requires the integration of diverse algorithms, models, and data pipelines. To address this, we present XAIpipeline, a service that interfaces with cloud AI services and open-source models to provide detailed model explanations. XAIpipeline automates structured approaches to apply multiple explainable AI (XAI) techniques, enhancing the explainability and quality assurance of AI-based software service systems. This work implements the XAIpipeline's design and technology stack, demonstrating its integration of XAI algorithms, cloud AI services, and open-source models with a DevOps-style toolkit. The service executes parallel pipelines and produces end-toend explanation visualizations from data samples. XAIpipeline offers APIs, CLIs, and web portals, enabling users to configure tasks to their specific requirements. We showcase three XAI service scenarios where AI models are applied to support decision-making: (1) Tabular classification model, (2) Image vision model, and (3) Video action recognition model. The source code and supplementary materials are available on GitHub (https://github.com/ZeruiW/XAIpipeline).
A picture is worth a thousand words. NonFunctional Requirements (NFRs), such as security and usability, are modeled using Softgoal Interdependency Graphs (SIGs) to capture potential conflicts and synergies. However, the practice of NFR modeling remains limited, partly due to unfamiliarity with modeling languages like SIG and insufficient understanding of relevant NFRs. Large Language Models (LLMs) show some knowledge of NFRs and SIG concepts, such as goal decomposition and operationalization, but often lack precise knowledge of formal SIG syntax. We introduce SIG-GPT, a GPT-4-based LLM augmented with SIG knowledge using text-based grammar supplied and Retrieval Augmented Generation (RAG). RAG enhances LLM responses by retrieving relevant external knowledge, while the grammar enforces correct syntax, guiding the LLM to generate SIGs align with formal notation. To help practitioners better understand SIG modeling, reduce time and effort, and enhance NFR proficiency, we apply textual grammar to SIGGPT, ensuring it is ready for seamless integration with visual modeling tools like RE-Tool, enabling the LLM to generate correct SIG structures without requiring a large dataset of SIG examples. Results show that SIG-GPT with grammar and RAG achieves 100% syntactic accuracy, 95% semantic accuracy, and 98% cohesion (CCR) while aligning with Bloom's Taxonomy to enhance structured reasoning in SIG modeling.
Identifying anomalous commits is essential for maintaining software quality and reliability, as these anomalies can indicate potential issues in code, development practices, or repository management. Current anomaly detection methods typically rely on prede-fined rules or supervised learning, which suffer from limitations such as dependence on labeled datasets, rigid rule definitions, and high maintenance overhead in rapidly evolving repositories. This paper introduces a novel unsupervised framework for effectively detecting anomalous commits without requiring labeled data or rigid rules, providing a scalable and adaptable solution to enhance code quality in modern version control systems. To address the high-dimensional and mul-tifaceted nature of commit data, our approach com-bines dimensionality reduction techniques with tar-geted feature engineering, enhancing both precision and adaptability in anomaly detection. We systematically evaluate three state-of-the-art unsupervised techniques-Local Outlier Factor (LOF), Isolation Forest (IF), and Histogram-Based Outlier Score (HBOS)-across five diverse open-source repositories. Our results demonstrate that Isolation Forest achieves the highest detection accuracy, effectively balancing precision and recall while capturing both global and local anomalies. Additionally, expert validation confirms the practical relevance of our approach, providing insights into frequent and high-impact anomalies encountered in real-world repositories.
