Recent advances in generative artificial intelligence (AI) and machine learning (ML) have renewed interest in realizing the long-standing goal of computer-aided software engineering by improving software quality and productivity. Although these techniques have been applied across many software engineering (SE) tasks, their effectiveness depends heavily on access to large, high-quality, labeled, domain-specific datasets, which remain limited, particularly in requirements engineering (RE) where research often relies on natural language artifacts. Existing, public datasets are typically small, contain labeling ambiguities, and show substantial class imbalance, which restricts the development, evaluation, and reproducibility of AI-driven SE approaches. To address these challenges, this paper presents the O3DG approach, a repeatable method for generating on-demand, high-quality, ontology-aligned datasets using large language models (LLMs). O3DG integrates prompt engineering strategies, domain-specific seed examples, and ML-based validation to synthesize diverse and cohesive datasets suitable for SE research. The approach is demonstrated through two representative RE case studies involving the classification of non-functional requirements and the detection of ambiguity in software requirements. For each case, the paper details the O3DG pipeline, ontology mappings, and validation steps that ensure dataset reliability and practical utility. Results show that O3DG produces datasets with strong category cohesion, improved balance across classes, and effective support for ML training. More broadly, the study illustrates how LLM-assisted dataset synthesis can help overcome persistent data limitations and provides a transferable process for producing high-quality datasets across additional SE domains.

Digital forensics often requires deriving meaningful and investigative intelligence from vast amounts of evidence scattered across various artifacts. In this study, we propose an automated approach to gain insights about criminal incidents using digital evidence networks constructed with the aid of Large Language Models (LLMs). Our method utilizes LLMs to extract evidence entities from mobile devices and infers relationships among them. Using this information, the model enables the generation of Forensic Intelligence Graphs (FIGs). These graphs visually represent evidence entities and their interrelations, providing an intelligence-driven approach to forensic data analysis. Using evidence extracted from Android mobile devices, an empirical evaluation demonstrates that the LLM-aided FIG achieves 93.33% coverage of evidence entities and 86.96% coverage of evidence relationships, effectively uncovering all relevant suspect scenarios. Moreover, our approach uncovered 27 additional evidence entities and 83 relationships beyond those recorded in the official documentation, highlighting its ability to reveal previously overlooked forensic artifacts.

Machine learning models are being increasingly relied on for many natural language processing tasks. However, these models are vulnerable to adversarial attacks, i.e., inputs designed to target models into making a wrong prediction. Among different methods of attacking a model, it is important to understand what attacks are effective, so that we can design countermeasures to protect the models. In this paper, we design and implement six adversarial attacks against natural language machine learning models. Then, we evaluate the effectiveness of these attacks using a fine-tuned distilled BERT model and 5,000 sample sentences from the SST-2 dataset. Our results indicate that the Word-replace attack affected the model the most, which reduces the F1-score of the model by 34%. The Word-delete attack is the least effective, but still reduces the model’s accuracy by 17%. Based on the experimental results, we discuss our insights and provide our recommendations for building robust natural language machine learning models.

With the rapid advancement of information tech-nology, the Internet of Things (IoT) has significantly impacted people's daily life. IoT devices not only bring comfort and convenience to every aspect of the world, but also appear to be a new target of cybercrimes. Thus, IoT forensics becomes a critical step in forensics investigation. Intelligent robot vacuums are one of the most popular IoT devices. As robot vacuums can connect to the Internet and be operated through mobile apps, a large amount of data may be stored and transmitted among the vacuums, mobile apps, and the network. The data may include the history of the robot's operation, network and user credentials, and layouts of the floor plan of a house. From the perspective of digital forensics, these data can be critical while collecting necessary evidence, investigating suspects and victims, and reconstructing crime scenes. To this end, this paper makes an initial attempt to conduct a digital forensic analysis on intelligent robot vacuum systems. Specifically, this paper retrieves and analyzes a robot vacuum's operation log, the installation details of the robot vacuum's control system, and the usage record of the application from the memory of a smartphone.

The increasing complexity of software applications and the necessity for minimizing software vulnerabilities has given rise to the use of machine learning techniques that can identify software vulnerabilities in source code. However, many of these techniques lack the accuracy needed for industrial practice. The contribution of this work is the novel use of an Abstract Syntax Tree Neural Network (ASTNN) to identify and classify software vulnerabilities in the Common Weakness Enumeration (CWE) types. We make two fundamental claims in this work. First, the use of an ASTNN performs better than prior machine learning neural network architectures. Second, the benchmark data set commonly used for machine learning vulnerability classification is flawed for this use. To illustrate these claims, we describe our ASTNN architecture and evaluate it with more than 44,000 test cases across 29 CWEs in the NIST Juliet Test Suite data set. Results show a minimum of 88% accuracy across all CWEs.

Advances in machine learning (ML) algorithms, graphics processing units, and readily available ML libraries have enabled the application of ML to open software engineering challenges. Yet, the use of ML to enable decision-making during the software engineering lifecycle is not well understood as there are various ML models requiring parameter tuning. In this paper, we leverage ML techniques to develop an effective approach to classify software requirements. Specifically, we investigate the design and application of two types of neural network models, an artificial neural network (ANN) and a convolutional neural network (CNN), to classify non-functional requirements (NFRs) into the following five categories: maintainability, operability, performance, security and usability. We illustrate and experimentally evaluate this work through two widely used datasets consisting of nearly 1,000 NFRs. Our results indicate that our CNN model can effectively classify NFRs by achieving precision ranging between 82% and 94%, recall ranging between 76% and 97% with an F-score ranging between 82% and 92%.

This special session is intended for people who are curious about integrating Humanitarian Free and Open Source Software (HFOSS) into their courses and involving students in HFOSS projects. HFOSS participation provides an excellent vehicle to introduce students to computing for social good while also giving them experience with real-world software development. The session will begin with a series of lightning talks by faculty who have incorporated HFOSS participation into one or more of their courses. This will be followed by a small group activity to allow attendees to ask questions and discuss individual interests in integrating HFOSS activities and to become familiar with HFOSS educational resources available for instructors. The session will conclude with some closing comments on current and planned activities of instructors who are teaching with HFOSS and suggestions for faculty who would like to pursue this teaching approach themselves.

A common refrain from Senior Exit Surveys and Alumni Surveys is the desire to work on "real-world," "practical" and "hands-on" projects using industry-ready tools and development environments. To assuage this, institutions have moved towards adopting Free and Open Source Software (FOSS) as an avenue to provide meaningful, applied learning interventions to students. Through these experiences, students benefit from engagement with various communities including: the community of contributors to the FOSS project; the community of local software developers; the community of citizens who reside in the local area; the community of students at their institution and others; and, the community of people impacted by the FOSS project. These engagements motivate students, enhance their communication and technical skills, allow them to grow and become more confident, help them form professional networks, and provide the "real-world" projects they seek. In this panel, we will discuss our experiences in engaging students with five different types of communities as part of incorporating FOSS into our courses, focusing on how other educators can provide the same benefits to their students as well. In order to satisfy the time constraints of the panel, the last two authors will present together.

The aim of this paper is to establish the foundations for developing a mental model that bridges the gap between usability and security in user-centred designs. To this purpose, a meta-model has been developed to align design features with the users’ requirements through tacit knowledge elicitation. The meta-model describes the combinatorial relationships of Security, Usability and Mental (SUM) and how these components can be used to design a usable and secure system. The SUM meta-model led to the conclusion that there is no antagonism between usability and security. However, the degree of usable security depends on the ability of the designer to capture and implement the user’s tacit knowledge. In fact, the SUM meta-model seeks the dilution of the trading-off effects between security and usability through compensating synergism of the tacit knowledge. A usability security cognitive map has been developed for the major constituents of usability and security to clarify the interactions and their influences on the meta-model stipulations. The three intersecting areas of the three components’ relationships are manipulated to expand the Optimal Equilibrium Solution (OES) (δ) expanse. To put the SUM meta-model into practice, knowledge management principles have been proposed for implementing user-centred security and user-centred design. This is accomplished by using collaborative brainpower from various knowledge constellations to design a system within the user’s current and future perception boundaries. Therefore, different knowledge groups, processes, techniques, tactics and practices have been proposed for knowledge transfer and transformation during the mental model development.

To attract and encourage the best and the brightest students to pursue state, federal and tribal government careers in cybersecurity, the National Science Foundation's CyberCorps: Scholarship for Service (SFS) program was created and funded in 2000 as part of the Federal Cyber Services training and education initiative. Only institutions with a strong and established academic program in cybersecurity are eligible to participate in the CyberCorps SFS program. For the past four years, Towson University (TU) has offered CyberCorps scholarships to highly-qualified undergraduate students in computer science with a track in computer security. The poster describes the TU cybersecurity curriculum and the extracurricular activities that have been essential in bringing the CyberCorps SFS program to TU and recruiting qualified undergraduate students, as well as, challenges and lessons learned.

Enterprise architecture (EA) has demonstrated utility for improving overall Information System (IS)/Information Technology (IT) outcomes for institutions, particularly those with large-scale or integration-related needs. To achieve the core goal of an EA — integration, alignment and governance between enterprise goals and the enterprise IS/IT portfolio — institutional vision, mission and objectives must be elicited, analysed, understood and documented by the enterprise architects. This paper proposes and evaluates a lightweight EA elicitation technique to gather the required information about enterprise mission and objectives as a lightweight entry point to developing an EA. Specifically, we investigate the evaluation results of our proposed technique in EA data elicitation and analysis stemming from a case study that was conducted on an institution with a significant IS/IT asset portfolio. Our proposed EA elicitation technique utilizes the VMOST elicitation question, a structured elicitation vehicle, and Grounded Theory Method as the qualitative analysis technique to analyse elicited responses. Application of this approach in a real case study garnered sufficient understanding the vision, mission and objectives of an enterprise to articulate objectives in a way suitable to use as institutional goal as a part of the Zachman EA framework.

Computer Science educators are constantly reinventing introductory CS0, CS1 and CS2 courses to retain students and increase their learning and motivation. While the focus on drawing students into Computer Science is vital to maintain enrollment, it is also paramount to sustain student motivation by using new pedagogical approaches, contextualized to how students learn, throughout the curriculum to develop technical and interpersonal skills. Many educators have developed one-off courses employing new technology or a project-driven approach to engage students and transfer ready industry skills. Yet, too few have woven a combination of pedagogical approaches sensitive to how students learn with in-demand technology and skills contiguously throughout the upper-level curriculum and tied it to opportunities outside of the classroom. In this poster, we present the initial results of a project with two aims to: (1) design, pilot, and offer new curriculum materials for three upper-level Computer Science courses using a mobile computing platform paired with pedagogical approaches sensitive to the learning styles of today's student; and, (2) develop out-of-classroom learning opportunities including mobile computing student seminars, externship opportunities and service-learning projects reinforcing in-classroom technical skills. We believe that these two components synergistically, can be leveraged to revitalize the undergraduate Computer Science curriculum, better motivate student learning and provide in-demand technical skills to students without losing the fundamental Computer Science concepts. This project is partially supported by NSF DUE#1140781.

Computer Science (CS) educators are constantly reinventing introductory and advanced courses contextualized with new technology to better engage, retain students and increase their learning and motivation. Mobile and wearable computing are the most recent examples. However, while it is important to attract students into CS, it is equally vital to sustain student motivation by using pedagogical approaches contextualized to how students learn throughout the curriculum without losing fundamental, core concepts. Many educators have developed one-off courses employing new technology or a project-driven approach to engage students and transfer ready industry skills. Yet, too few have woven a combination of pedagogical approaches sensitive to how students learn with in-demand technology and skills contiguously throughout the upper-level curriculum and tied it to opportunities outside of the classroom. To ensure that CS educators are not simply adopting the newest technological fad at the expense of students learning and fundamental, core CS concepts, the CS education community must understand how mobile computing technology can be successfully leveraged to change education through measured, contextualized pedagogical approaches. To start facilitating this dialog, this Birds of a Feather session will provide a platform for the discussion of how mobile computing has been successfully (and unsuccessfully) incorporated into CS courses; the mobile platforms and tools used; and, the pedagogical utility of using mobile computing as a learning intervention. This project is partially supported by NSF DUE#1140781.

Existing Enterprise Architecture (EA) frameworks (EAF) focus on the governance and alignment of an enterprise's strategic business plans and operating model with its Information Technology (IT) capabilities. These technically-oriented processes attempt to simplify the verification and validation of design artifacts used during software development. Yet, many EA projects fail mostly for non-technical reasons. The development of an EA can necessitate organizational change which can influence stakeholder behavior in ways that may be detrimental to the EA. An analysis of EAFs finds that they are deficient in incorporating human behavior and that they avoid any of the consequences of human action during EA development. This paper proposes a behavior-driven EA requirements quality management program designed to encourage stakeholder collaboration and participation in EA.

Many computing departments are integrating mobile computing into their curriculum to provide students with the requisite technical, entrepreneurial, software engineering and programming skills desired in today's job market. Mobile computing has also been used as a learning intervention to motivate student interest and contextualize learning. This work presents the results of a literature analysis investigating the state of practice in computing education to understand the contexts, themes and approaches that the academic community is taking to teach mobile computing.

The U.S. Department of Defense (DoD) Joint Staff, Force Structure Directorate (J-8), along with the Office of Under Secretary of Defense for Personnel and Readiness (OUSD(P&R)) put forth the Global Force Management Data Initiative (GFM DI) with the goal of developing a reliable and maintainable enterprise data source capturing Organizational and Force Structure Construct (OFSC) information. The Global Force Management Information Exchange Data Model (GFMIEDM) was developed as an enterprise solution to enable disparate systems throughout the DoD to share force structure, manpower, and equipment data. Extensible Markup Language (XML) was chosen as the data format because it is a popular format for the interchange of data between heterogeneous systems. XML is currently being used as a wrapper around relational data, and tools have been written to validate the data against structural constraints and business rules. In this paper, we discuss the GFM DI architecture, implementation details, and integration with legacy systems.

The goal of enterprise architecture (EA) artifacts is to create an information technology (IT) infrastructure that aligns with the institution's business environment. To facilitate this, enterprise architecture frameworks (EAF) have been used to understand an enterprise's strategy and business architecture to synthesize a supporting IT strategy. This understanding is critical to the success of an EA. This paper contrasts the use of a systematic elicitation approach, VMOST, to recently completed strategy capture artifacts. We assess our approach through interviews with a number of employees across several departments within a University library, then comparing the interview responses to the conventionally compiled Strategic Plan. Placing data gathered by the two efforts side by side will allow coverage comparison of EA-unguided artifacts against a lightweight-guidance method and determine the usefulness of a non-EA strategy effort in completing an EAF. Insights into the utility of the two methods will be provided by completing relevant aspects of the Zachman Framework, which will further the understanding of the use of EA methods and tools within organizations without EA expertise or significant experience.

Computational thinking has been identified as a necessary fundamental skill for all students. University curricula, however, are currently not designed to provide such knowledge to a broad student population. In this paper, we report on our experiences in the development of a model for incorporating computational thinking into the undergraduate, general education curriculum at Towson University. We discuss the model in terms of eliciting faculty interest, institutional support, and positive student response. In the first two years of this NSF-funded three-year project, we have developed, piloted and assessed five computational thinking general education courses - an Everyday Computational Thinking course, and four discipline-specific computational thinking general education courses. Initial assessments show promising and significant student, instructor and administration interest in computational thinking as a basis in courses covering multiple disciplines within the general education curriculum.

Agent-oriented software engineering (AOSE) has provided powerful and natural, high-level abstractions in which software developers can understand, model and develop complex, distributed systems. Yet, the realization of AOSE partially depends on whether agent-based software systems can achieve reductions in development time and cost similar to other reuse-conscious development methods. Specifically, AOSE does not adequately address requirements specifications as reusable assets. Software product line engineering is a reuse technology that supports the systematic development of a set of similar software systems through understanding, controlling, and managing their common, core characteristics and their differing variation points. In this article, we present an extension to the Gaia AOSE methodology, named Gaia-PL (Gaia-Product Line), for agent-based distributed software systems that enables requirements specifications to be easily reused. We show how our methodology uses a product line perspective to promote reuse in agent-based software systems early in the development life cycle so that software assets can be reused throughout system development and evolution. We also present results from an application to show how Gaia-PL provided reuse that reduced the design and development effort for a large, multiagent system.

A principal goal of agent-oriented software engineering (AOSE) is to provide the mechanisms for reusing, maintaining and allowing the evolution of agent-based software systems. Our AOSE methodology, Gaia-PL, enables the design and development of multi-agent system product lines (MAS-PL)1by providing the software engineering processes to define and reuse requirements specifications and design artifacts. In this paper we extend our Gaia-PL methodology with automated tool support to enable the reuse and verification of MAS-PL requirements to better facilitate specification reuse during both initial system development and evolution. Specifically, we show how use of our product-line requirements management and verification tool along with feature modeling can support correct variation point selection, reuse and MAS-PL evolution. We illustrate and evaluate this work through an application to a proposed NASA agent-based pico-spacecraft swarm.