Portrait image of Rick Kazman

Rick Kazman

Shidler College of Business
University of Hawaii at Manoa
2404 Maile Way
Honolulu HI 96822
Tel: (808) 956-6948
Fax: (808) 956-9889

The Software Technology track at HICSS is about methods, tools and techniques related to software, as distinct from the context in which it is deployed or its applications. Software Technology is among the oldest tracks at HICSS and has provided a central point of interaction among all participants in the conference, as well as a natural forum to foster new technologies. Among the topics that the Software Technology track has covered are: software engineering, security, networking, software-based product-lines, open source software, pervasive computing, artificial intelligence, agile methods, mobile/ad hoc networking, cloud computing, virtualization, parallel and distributed computing, and software assurance. The Software Technology track continues to invite novel and emerging areas of research in what remains a dynamic and exciting field.

The expanding capabilities of Generative AI are forging new paths in software development, an area ripe with opportunities for innovation and transformation. This minitrack aims to encapsulate a comprehensive view of this evolving landscape, highlighting both the technological advancements and the broader implications of these emergent technologies.

We seek to attract a cadre of research that both delineates and critiques the concepts, methods, frameworks, architectures, functionalities, and broader implications of applying and integrating Generative AI in software development. The scope of this minitrack includes, but is not limited to, the following key areas:

  • Deployment of synthetic data for model training within development environments, facilitating advanced machine learning applications while addressing data privacy concerns.
  • Exploration of the ethical dimensions in the use of Generative AI in software development, focusing on intellectual property rights, bias in automated outputs, and accountability in decision-making.
  • Integration of Generative AI into coding practices, improving code quality and development speed, while considering issues of code originality and skill enhancement.
  • Utilization of Generative AI for creating comprehensive test datasets, enhancing software testing effectiveness, and uncovering critical edge cases.
  • Intersection with agile and DevOps methodologies, leveraging Generative AI for continuous integration and adaptive responses to evolving project requirements.
  • Implications of Generative AI in legacy system sustainment, addressing the challenges and opportunities in modernizing and maintaining older software infrastructures.
  • The role and impact of Generative AI in software innovation, fostering new methods in ideation, brainstorming and co-designing, thus revolutionizing traditional approaches to software development and project conceptualization.

This minitrack aspires to be a platform for rigorous scholarly inquiry into the multifaceted applications of Generative AI in software development, emphasizing both the innovative potential and the consequential ethical, legal, and operational challenges.

Minitrack Co-Chairs:

Johnny Chan (Primary Contact)
University of Auckland

Gabrielle Peko
University of Auckland

David Sundaram
University of Auckland

Ghazwan Hassna
Hawaiʻi Pacific University

Software and hardware -driven computation plays an increasingly critical role in many facets of our daily lives. This omnipresence combined with an increased convergence and reliance on software-driven functionality rightly gives cause to increasing security concerns associated with the computing systems we rely on. Applying cybersecurity appropriately into these systems requires care and thought to ensure security threats can be effectively addressed.

This minitrack embraces deep dives across multiple disciplines that supply rigorous, effective, and innovative approaches to managing cybersecurity issues across the Information Technology (IT) and Operational Technology (OT) spaces. Evolving threats, hyperconvergence, and increased attack surfaces all active ongoing challenges that need be addressed to ensure the computing systems we deploy for real-world applications are adequately and appropriately protected for assured proper usage. We seek high-quality research papers that apply scientific rigor to well-defined problems within this topic are. We specifically encourage concepts that explore multi-disciplinary approaches, considering application and interaction across hardware, physical engineering, mathematics, computer science, data and information sciences, human factors, psychology and more.

A partial list of topics of interest, especially with alignment to critical infrastructure, includes:

  • In-progress results in cutting-edge, high-risk, high-reward cyber research
  • Hardware vulnerabilities, side channels, and security solutions
  • Cross-disciplinary approaches to cyber security or trust
  • Cryptography, cryptanalysis, privacy, and security
  • Artificial and augmented intelligence applied to cyber systems
  • Network anomaly detection and novel approaches to securing IT/OT systems
  • Modeling and simulation of cyber systems, security, trust, and risk
  • Human-machine interaction and optimization
  • Data science and big data applications
  • Securing the cloud
  • System adaptation, organization, optimization, and resilience
  • Software technology applications to cyber systems
  • Policy, Techno-social, or Management needs within cybersecurity
Minitrack Co-Chairs:

Nick Tsamis (Primary Contact)
MITRE Corporation

Chad Bollmann
Naval Postgraduate School

Britta Hale
Naval Postgraduate School

James Scrofani 
Naval Postgraduate School

This minitrack intends to serve as a platform for presenting innovative research, discussing emerging trends, and setting the agenda for future exploration in the field of AI security. It will address the intersection of technology, safety, trustworthiness, responsibility, and policy, ensuring a comprehensive approach to AI security. The focus will be placed on developing and implementing mechanisms to ensure that AI systems are safe for users and operate in a trustworthy manner. This includes research on creating transparent AI systems that users can understand and trust, and methodologies to verify and validate the safety of AI algorithms in various applications. Recommended topics include, but are not limited to, the following:

  • System-Theoretic Process Analysis for Security (STPA-SEC) for AI Systems
  • AI Safety and Trustworthiness
  • AI Responsibility
  • AI Risk Models
  • AI Explainability
  • AI Ethics and Algorithmic Bias
  • AI Cybersecurity, Offensive, and Defensive Operations
  • Secure Machine Learning Operations (MLOps)
  • Secure AI Algorithm and Machine Learning Algorithms
  • Red Teaming AI systems and algorithms
  • Robustness and Resilience of AI Systems
  • Threat Detection and Response in AI Systems
  • Ethical and Regulatory Considerations in AI Security

There will be opportunities for researchers to fast track a journal publication in a Special Issue within the Proceedings of the IEEE.

Minitrack Co-Chairs:

Tyson Brooks (Primary Contact)
Department of Defense and Syracuse University

Shiu-Kai Chin
Syracuse University

William Young
Syracuse University

Erich Devendorf
Air Force Research Laboratory

Game engines have evolved beyond their traditional use in video game development and are now utilized in various application areas. Important examples of such application areas include film and animation production, architectural visualization and design, media arts, digital performance and theatre, digital health and well-being, training and simulation, data visualization, mixed reality, ar/vr, serious games and gamification, education and e-learning, interactive exhibits and installations, and non-technical aspects of game technology and game experience.

This minitrack is open for anybody working with gaming technology from implementation to content creation with a particular focus on applications beyond game development. Topics of interest include (but are not limited to):

  • New applications of game technology (e.g., virtual production)
  • End user programming with game engines
  • Extending game engines for new application areas
  • User experience design based on game technology
  • Content creation workflows
  • Teaching game technology
  • Experience reports from practical applications
Minitrack Co-Chairs:

Tim Schattkowsky (Primary Contact)
Hamm-Lippstadt University of Applied Sciences

Christian Geiger
University of Applied Sciences Düsseldorf

Stefan Albertz
Hamm-Lippstadt University of Applied Sciences

The societal importance of software systems and cyber-physical systems is increasing as they affect almost every aspect of our daily lives. With applications ranging from digital twins of individuals to entire societies or individual industrial processes to autonomous vehicle fleets, the impact of these systems has reached a new level where they can transform society and the daily lives of individuals. This presents various new and greater challenges that need to be considered from an interdisciplinary perspective. They include:

  • Systematic development of digital twins
  • Federated, cooperated digital twins
  • Architectural and design patterns
  • Privacy concerns, security risks, and safety implications
  • Increasing resilience, availability, and dependability of software systems
  • Environmental impact by digital twins on software systems
  • Regulations and regulatory compliance

Addressing these challenges requires collaboration among stakeholders from various domains, including science, technology, policy-making, and civil society, to develop comprehensive solutions that prioritize societal well-being and promote responsible technology usage.

This minitrack intends bring together these stakeholders to present research and practical experience from all related areas as a basis for an interdisciplinary discussion. Topics of interest include:

  • Technologies, frameworks, platforms, and architectures for modern software systems
  • Case studies of socio-technical ecosystems with federated, cooperating digital twins
  • Investigating the social impact of software- and cyber-physical systems
  • Incorporating social awareness in software engineering methods
Minitrack Co-Chairs:

Achim Rettberg (Primary Contact)
Carl-von-Ossietzky Universität Oldenburg

Gregor Engels
Paderborn University

Stefan Henkler
Hamm-Lippstadt University of Applied Sciences

Tim Schattkowsky
Hamm-Lippstadt University of Applied Sciences

As technology is incorporated into more aspects of daily life, cyber operations, defenses, and digital forensics solutions continue to evolve and diversify. This encourages the development of innovative managerial, technological, and strategic solutions. Hence, a variety of responses are needed to address the resulting concerns. There is a need to research a) technology investigations, b) technical integration and solution impact, c) the abuse of technology through attacks, and d) the effective analysis and evaluation of proposed solutions. Identifying and validating technical solutions to secure data from new and emerging technologies, investigating these solutions’ impact on the industry, and understanding how technologies can be abused are crucial to the viability of commercial, government, and legal communities.

We welcome new, original ideas from academia, industry, government, and law enforcement participants interested in sharing their results, knowledge, and experience. Topics of interest include but are not limited to:

  • Detection and analysis of advanced threat tactics, techniques, and procedures
  • Applying machine learning tools and techniques in terms of cyber operations, defenses, and forensics
  • Case studies surrounding the application of policy in terms of cyber operations, defenses, and forensics
  • Approaches related to threat detection and Advanced Persistent Threats (APTs)
  • Solutions that secure different types of data stored in different layers of computer networks
  • “Big Data” solutions and investigations – collection, analysis, and visualization of “Big Data” related to cyber operations, defenses, and forensics
  • Malware analysis and the investigation of targeted attacks
  • Device investigations that assist with the recovery and reconstruction of digital artifacts
  • Digital evidence recovery, storage, preservation, and memory analysis
  • Event reconstruction approaches and techniques
  • Anti-forensics techniques and solutions
  • Investigations related to mobile devices, embedded systems, or Internet of Things (IoT) devices
  • Forensic investigations within emerging domains such as transportation systems, industrial control systems, and SCADA
  • Network investigations – collection, analysis, and visualization of network forensic data
  • Privacy implications related to security incident response and digital forensic investigations
  • Research in security incident management
  • Situational awareness related to security incident response
  • The impact of digital evidence on the legal system

The above list is suggestive. Authors are encouraged to contact the minitrack chairs to discuss related topics and their suitability for submission to this minitrack.

Accepted papers will be offered the opportunity to extend their submission by 50% and submit to a special issue of the Association for Computing Machinery (ACM) Digital Threats: Research and Practice (DTRAP) Journal.

Minitrack Co-Chairs:

William Bradley Glisson (Primary Contact)
Louisiana Tech University

Todd McDonald
University of South Alabama

Modern society is irreversibly dependent on software systems of astonishing scope and complexity. Yet despite best efforts, errors, vulnerabilities, failures, and compromises continue to persist. Networked systems with complex hardware and software components embody many pathways that adversaries can exploit. Experience shows that contemporary cybersecurity and software assurance methods are insufficient to meet this challenge.

This minitrack focuses on how to enable development and application of these foundations. We ask: How should research and development move us toward a solid basis in understanding and principle? The goal is to develop science foundations, technologies, and practices that can improve the security and dependability of complex systems. This minitrack will bring together researchers in cybersecurity and software assurance in a multidisciplinary approach to these problems. It addresses all aspects of cybersecurity viewed from an “enlightened security” perspective. The following topics will be included in the minitrack:

  • Security ecosystem
  • Designed-in security
  • Tailored trustworthy spaces
  • Moving target
  • Cyber economics
  • Science of security
  • Multivariate detection and response
  • Co-evolution of defense and offense
  • Biologically-inspired security models
  • Holistic risk analysis
  • Hardware-enabled trust
  • Layered adaptable defense
  • Real-time coordinated response
  • Automated system interoperability
  • Authentication in ecosystem
  • Practical use of continuous monitoring
  • Confidence in activity prediction
  • Security visualization and prediction
  • Theories of vulnerability classification and control
  • Security measurement
  • • Advances in information assurance theory and practice
  • Advances in specification, design, and implementation of assured systems
  • Advances in verification, testing, and certification of assured systems
  • Advances in software security analysis
  • Assurance for embedded systems and hardware components
  • Assurance for large-scale infrastructure systems
  • Information and software assurance in cloud computing environments
  • Assurance in system maintenance and evolution
  • Automated methods for information and software assurance
  • Assurance through computation of software behavior
  • Management of assurance operations
  • Processes and metrics for information and software assurance
  • Business case and ROI development for information and software assurance
  • Supply chain and standards issues in information and software assurance
  • Case studies of system assurance successes
  • Software testing
Minitrack Co-Chairs:

Luanne Burns Chamberlain (Primary Contact)
Johns Hopkins University Applied Physics Lab

Thomas Llanso
John Hopkins University Applied Physics Lab

Richard George
Johns Hopkins University Applied Physics Lab

Games for Impact minitrack intends to draw attention to the use of games and game technology for special purposes and positive outcomes where the created experience reaches beyond entertainment. Recognizing that games are a powerful vehicle to make emerging technologies accessible to society, this mini track creates a space to explore the many factors that influence the design, development, application, adoption, use, and impact of games and game technology.

The exploration of Games for Impact mini track falls under the umbrella of recent games research fields such as games for health/rehabilitation/therapy, games for learning, games for empathy, games for social innovation, and citizen science games. Potential subtopics or areas including but not limited to the following are listed below:

  • Case study on designing, developing, using, and evaluating games for special purposes
  • Best practices and guidelines on game design, study design, interaction design, user experience (UX) and user interface (UI)
  • The role and application of games and game technology in creating, disseminating, and evaluating social innovation
  • The application and impact of games and game technology in education and its accessibility
  • The application and impact of games for training, learning, and personal development (habit building, empathy, social skills, etc.)
  • Evaluation approaches, criticality, quality measures, and ethics of using and adapting games and games technology in other fields such as health, rehabilitation, education, social innovation, citizen science
  • Use of novel interaction modalities, platforms and/or controllers, IoT, VR-AR-MR
  • Analysis for socio-cultural context of games for impact
  • Demographics, persona studies, and ethics of the application, adoption, and impact of games and game technology for purposes beyond entertainment

We welcome contributions on design and development methods, technical studies that focus on implementation and development guidelines, case studies with novel interaction modalities including platforms (mobile, AR-VR-MR) and/or controllers, user experience (UX) approaches, user interface (UI) techniques, analysis for socio-cultural context of games for impact, demographics and persona studies, and ethical studies for the aforementioned research fields.

Minitrack Co-Chair:

Aslihan Tece Bayrak
Media Design School

Generative AI is a type of artificial intelligence (AI) that generates new and original content based on patterns learned from existing data. This can include a range of media such as images, videos, audio, and text. Conversational AI focuses on enabling natural language interactions between humans and AI systems. It uses NLP (Natural Language Processing) and machine learning algorithms to understand and respond to human language input.

The growth of Generative and Conversational AI and the development of large language models like ChatGPT and Google Bard has created new possibilities for applications in various fields, including Information Systems (IS) research and education. This minitrack aims to explore the use of Generative and Conversational AI models in information systems, including natural language processing, recommendation systems, and personalization. The minitrack will be of particular interest to researchers and practitioners from the fields of IS and Generative AI, as well as those teaching information systems courses interested in incorporating Generative AI into the IS curriculum.

  • Applications of Generative AI in IS, such as data generation, image generation, text generation, video generation, and simulation
  • Applications of Conversational AI in IS, such as chatbots, virtual assistants, and voice assistants, among others
  • Best practices for integrating Generative and Conversational AI into the IS curriculum
  • Developing educational materials and resources for teaching Generative and Conversational AI
  • Preparing students for careers in Generative AI and IS
  • Integrating Generative AI with other AI technologies for improved results
  • Real-world applications of Generative AI in IS research and education
  • Generative AI for personalized recommendations and decision-making
  • Generative AI for improving the efficiency of IS
  • Generative AI for enhancing user experience in IS
  • Generative AI in various domains, such as healthcare, finance, and entertainment
  • Use of ChatGPT in natural language processing and information retrieval
  • Ethics, privacy, and regulatory considerations for the use of Generative AI in IS
  • Methodologies and frameworks for evaluating the effectiveness and impact of Generative and Conversational AI in IS research
  • The impact of Generative and Conversational AI on society and the economy
  • The effects of generative AI, such as ChatGPT, as an enabler versus a disabler
  • Challenges and limitations in the implementation of Generative and Conversational AI in IS
  • Detection of AI generated content
  • Platform governance relating to Generative and Conversational AI
  • Dark side of Generative and Conversational AI, such as misinformation and fake news on social media
  • Augmented Intelligence: Using Generative AI to enhance human capabilities
Minitrack Co-Chairs:

Nargess Tahmasbi (Primary Contact)
Pennsylvania State University

Elham Rastegari
Creighton University

Guohou (Jack) Shan
Temple University

Aaron M. French
Kennesaw State University

This minitrack aims to explore the intersection of AI and software engineering, focusing on the innovative ways in which AI technologies are influencing software development, testing, maintenance, and overall software lifecycle management. It invites researchers and practitioners to delve into the multifaceted implications of AI on software engineering practices, providing a platform for insightful discussions and the exchange of cutting-edge research findings.Topics of Interest include, but are not limited to:

  • AI-driven Software Development Processes:
    • Automated code generation and optimization
    • Impact on Software Design
    • Intelligent code completion and suggestion systems
    • AI-assisted requirement analysis and specification
  • AI in Software Testing and Quality Assurance:
    • Automated testing using machine learning algorithms
    • AI-driven fault prediction and localization
    • Quality assurance in AI-infused software systems
  • AI for Software Maintenance and Evolution:
    • Predictive maintenance and malfunction detection
    • Intelligent bug tracking and resolution
    • Adaptive software evolution with AI assistance
  • Ethical and Social Implications of AI in Software Engineering:
    • Bias and fairness in AI-enhanced software systems
    • Responsible AI practices in software development
    • Societal impact of AI-driven software solutions
    • How are the roles reshaped in software development teams
  • Educational Initiatives in AI and Software Engineering:
    • Integration of AI concepts into software engineering curricula
    • Training programs for AI-aware software engineers
    • Challenges and opportunities in AI education for software developers
Minitrack Co-Chairs:

Stefan Wittek (Primary Contact)
Clausthal University of Technology

Sandra Gesing
San Diego Supercomputing Center

Peter Salhofer

We would like to invite papers which focus on developing paradigms for addressing the synergy of software, devices, moveable objects, networks, and people, in one constantly changeable environment.  We would like to debate challenges of empowering edges of computer networks and misconceptions on their capabilities of carrying out modern computations. Examples of dynamic edge computing with the Internet of Things (IoT) / Internet of Everything (IoE) are more than welcome.  The IoT/IoE provide the possibility of constantly changeable sets of new types of connected devices, which might be wearables, belong to soft robotics, represent computational materials, and enable human machine augmentations.  In all these examples, edge computing plays an important role, and it is no surprise that we have started thinking how to equip the edges with intelligence.

However, there is something more important on the horizon; the latest advances in predictive and learning software technologies, such as generative AI models, are democratizing AI and leading public fascination with the current AI and this is pushing forward ideas of introducing generative AI at computational edges. Given the computational demands of generative AI,  emphasis is needed on sustainability, energy efficiency and adaptability of edge solutions, bearing in mind that we may have to descale and distribute current generative AI models and their data sources, and thus rethink the role of generated contents suitable for edge computing.  There are other challenges in promoting intelligent edge.  Most of them resulting from the uncertain future of replacing powerful cloud computing with new paradigms in which intelligent edge computing may take centre stage. Topics of interests include:

  • Towards Edge Computing Paradigm
    • Assessing computational edge technology landscape and its potentials for creating computational paradigms for the edge
    • Analysis of computability within edge technology landscape: the power of data, scalability and sustainability of computations
    • Examples of computing paradigms suitable for localised computing and its extensions towards Fog/Cloudlets/Clouds
    • Pushing data and computational intelligence, with analytic platforms, away from centralised Clouds and the role of serverless computing
    • The current role of Edge/Fog computing in IoT applications and implementations: reconsidering the role of Clouds
  • Creating Intelligent Edge Computing
    • Roadmap for AI at the computational edge, challenges, and opportunities
    • Feasibility of creating intelligence “at the edge” which could share computations and data between Clouds, Fog and Cloudlets and accommodate predictive technologies
    • Software architectures for supporting intelligence at the computational edge for the purpose of creating solutions in automation, manufacturing, businesses, medicine, healthcare delivery, education, and governance
    • Edge computing hardware solutions, including neuromorphics, tensor processing units (TPUs), FPGAs, and other dynamically programmable edge devices
    • Potential convergence of humans,” things” and AI in creating edge intelligence
    • Possibilities of defining a computational continuum by utilizing the space between localized computing and clouds and thus enhance the intelligence at the edge
  • Generative AI and Sustainable Edge Computing
    • The power of edge computing when addressing sustainability of running applications of generative AI
    • Leveraging edge computing and generative AI and tuning LLM at computational edges for specific tasks
    • Deploying generative AI inference at the edge
    • Optimizing edge performance for specific generative AI tasks, in certain contexts with specific datasets close to edges
    • Software architectures for managing distributions and tuning of and inference in generative AI models at the edge
    • Developing and tuning of smaller Language Models at the edge, for specific tasks
    • Edge computing hardware solutions for performing generative AI tasks
    • Analyzing Investments of hardware manufacturers in generative AI and energy sustainable edge devices, including neural and tensor processors
  • Edge Computing for/in Human Augmentation, Internet of Materials and Human centered Cognitive IoT
    • The role of edge computing in creating augmentation of human with machines and vice versa.
    • Promoting intelligent engineering for designing cognitive devices ready to accommodate edge intelligence
    • Investigating computational materials, shape changing computing particles and internet of materials as potential factors for empowering intelligent edge
    • Pushing forward the fusion of computational materials with hardware/software synergy typical of binary/ternary/quantum computers
Minitrack Co-Chairs:

Radmila Juric (Primary Contact)
ALMAIS Consultancy

Elisabetta Ronchieri
INFN-CNAF Bologna and University of Bologna

Filippo Sanfilippo
University of Agder

Trevor Bihl
Air Force Research Laboratory

P2P systems can solve a multiplicity of problems. For example: An appropriate community-based implementation of a Peer-to-Peer, social media network can isolate all interactions among peers to the community in which it they occur. Thus, predation, disinformation, trolling and bullying can be immediately quenched if they are a violation of the community’s policies.  Any violation of these policies can result in the owner cancelling a miscreant’s membership. Similarly, any community member may leave a community whenever it is desired. This type of community is ideal as a shield for sensitive children and teenagers. Also, one can create a “Wild West” community where anything goes. The variations are unlimited. Simply stated, what happens in a community stays in the community, and undesired behavior may result in expulsion.

Among the many possible examples of P2P networks, we have: First, drones monitoring forest fires in separate communities, each community with a distinct responsibility, e. g, alerting firefighters who are approaching a dangerous area, looking for hot spots, and minimizing property damage; second, sensors monitoring faults on a bridge with shared knowledge of the bridge’s status and points of failure; and third, video cameras for crowd control or aiding docents in a museum. In each of these three examples the peers report back to central command control as is appropriate.

In the light of what is discussed above, and the hardware and software technological advances achieved in the third decade of the new millennium, we invite contributions from practitioners as well as researchers in academia and industry on the use of Novel Peer-to-Peer Ecosystems to help solve the problems that now confront us. A general list of submissions includes but is not limited to the following:

  1. Various Internet-of-Things viewed as P2P Networks
    • Drone based P2P systems.
    • Sensor based P2P systems.
    • Intelligent Home appliances.
  2. AI machine learning based surveillance using AI enabled devices like video cameras and audio monitors.
  3. Distributed file systems that both secure and hide data.
  4. Mediated P2P Overlay Networks with private protocols that bind to Internet transport. In situations where Peers cannot connect to one another when, for example, they are behind a modem or router that uses NAT, known mediators are placed in the open Internet to host Peers on the same P2P Network. As such a mediator in the simplest case can be used for Peer discovery as well as a communication hub. It can also have a large selection of features such as VPN, Peer-to-Peer, and mediator-to-mediator routing; community management; intermediate data storage; and Peer public key distribution. It is always a cloaking device hiding the access to Peers it hosts.
    • Here ISP’s will not be able to monitor and collect user data because the services that are used on the overlay network are private. The actual destination may be a known website and its port/IP address are encrypted in the private protocol until the last hop mediator finalizes the connection and this mediator as primary source is not a person.
  5. P2P user moderated social media services as described above.
  6. The novel use of  LAN based, P2P Networks to maximize security
    • It is not always desirable to use the open Internet given the ever present threat of DDoS
        attacks, ransomware, etc.
    • A mediator may be placed on a LAN and have the same functionality as in (4) above.
  7. P2P Overlay Network routing algorithms
  8. P2P peer discovery
Minitrack Co-Chairs:

William Yeager (Primary Contact)
Stanford University Knowledge Systems Lab, Retired

Jean-Henry Morin
University of Geneva

Information security and privacy are a non-negotiable factor in the design and operation of information systems. Especially users – the so-called human factor – are a pivotal role in information security and user-privacy concepts. Often, their knowledge about security aspects and ways of user-manipulation tactics are the last line of defense against cyber-attacks. However, they are also the primary target of attackers and need to be sensitized about security-compliant behavior.

In addition to the traditional forms of user-computer-interactions in the form of mouse-keyboard-input-devices, new ways of system-interactions, e.g., physiological data from fitness-trackers, eye-tracking devices or even pupillary responses indicating cognitive-load-levels, are increasingly feasible as everyday HCI-components. With the interest in data privacy increasing, are users aware how valuable those personal input data is and how do they value data privacy measures?

Therefore, we have identified two main aspects relevant to researchers within the domain of Software Technology: 1) how to securely deal with input data (also focusing on privacy aspects) 2) how this data can be utilized to increase secure behavior or to raise awareness among users (help the users to make better security-related decisions) In this minitrack we seek papers that explore concepts, prototypes, and evaluations of how users interact with information systems and what implications these interactions have for information security and privacy. Further, we welcome new and innovative ways of human-computer-interaction and security-related concepts currently examined in the field. Topics of interest include but are not limited to:

  • Security related devices
  • Physiological sensors
  • Human-Computer-Interaction
  • (Conversational) Artificial intelligence
  • Blockchain applications
  • Sensor analysis
  • Data visualization
  • Biometrics authentication
  • Security and privacy awareness
  • Accessibility
  • Usable security design
  • Privacy and security by design
  • Privacy and smart contracts
  • User valuation of privacy
  • Validation of user data
Minitrack Co-Chairs:

Nicholas Müller (Primary Contact)
Technical University of Applied Sciences Würzburg-Schweinfurt

Kristin Weber
Technical University of Applied Sciences Würzburg-Schweinfurt

Paul Rosenthal
University of Rostock

This minitrack is dedicated to exploring the critical intersection of software usability, sustainability, and reproducibility, acknowledging the expanding role of software in shaping research across diverse domains. The three concepts usability, sustainability and reproducibility are interconnected with each other and cover a wide range of application areas. They affect all layers of the software process – from enabling reproducing experiments via an easy user interface to using containerization for application portability. Such concepts are also relevant in the building of Science Gateways (also known as virtual laboratories or virtual research environments), which by definition serve communities with end-to-end solutions tailored specifically to their needs. The mini-track will focus on the broad spectrum of submissions that deal with complex scenarios such as containerization, strategies for long-lasting software, usability and user interface issues, handling data curation and provenance and more.

Topics of interest include but are not limited to:

  • Web-based solutions (web sites, science gateways, virtual labs, etc.)
  • Application Programming Interfaces (APIs)
  • Computational and Data-Intensive Workflows
  • Novel approaches in containerization
  • Sustainability practices in software development
  • System architectures for testing and continuous integration
  • Emerging best practices in Machine Learning software
  • Best practices and Key Success Factors for usability, sustainability and reproducibility
  • Community building practices
  • Sustainability practices in software development, with a focus on AI applications
  • System architectures for testing and continuous integration in AI systems
  • Emerging best practices in AI and Machine Learning software
  • Addressing ethical considerations in AI-related software
  • Best practices and Key Success Factors for usability, sustainability, and reproducibility in the context of AI
Minitrack Co-Chairs:

Maytal Dahan (Primary Contact)
University of Texas at Austin

Joe Stubbs
University of Texas at Austin

Sandra Gesing
San Diego Supercomputing Center

The development of software has provided ample opportunities for research, provides ample oppor-tunities, and likely will provide ample opportunities. Not long ago, the proliferation of mobile computing opened up a new stream of research, then the same happened with the Internet-of-Things (IoT) and Cyber-Physical Systems (CPS). Fog, edge, and dew computing and the convergence of technologies likely will continue this trend. All these topics seemingly provide completely new endeavors; with a closer look, however, they can draw from what is already known – both regarding typical problems and regarding solutions.

Experiences and methods from classical software development can only be utilized to some degree when solving challenges that arise from new application, changing environment, and demanding do-mains. Development is complicated by the often faced need to develop for a multitude of platforms. With the emergence of multi-platform and multi-device, the new golden standard are applications not only across software ecosystems, but across hardware platforms such as laptop, mobile, tablets, em-bedded devices, sensors, and wearables. Therefore, new threads of research are needed to tackle these issues and to pave the way for improved software development, better business producibility and improved user experience (UX).

Further, there are novel developments in machine learning and analysis, and the emergence of multi-faceted aspects of artificial intelligence (AI), ranging from algorithms to ethical AI, secure AI and sustainable AI perspectives. This creates new opportunities for groundbreaking research through distributed machine learning, federated learning, edge analytics and computational collaboration between several heterogenous systems and device forms.

The minitrack is devoted to the technological background while keeping an eye on business value, user experience, and domain-specific issues. Contributions may take a socio-technical view or report on technological progress. We are particularly interested in applied software technology but also welcome theoretical work. Topics of interests include, but not limited to:

  • Case studies of development
  • Development methods, software architectures, and specification techniques
  • Economic and social impact, behavioral aspects
  • Software engineering education
  • User interface (UI) design and user experience (UX) research
  • Hybrid and cross-platform development
  • Web technology
  • Security, safety, and privacy
  • Energy-efficient computing
  • Machine learning on device
  • The convergence between mobile devices, IoT, and CPS
  • Fog, edge, and dew computing and their computational applications
Minitrack Co-Chairs:

Tim A. Majchrzak (Primary Contact)
University of Agder

Tor-Morten Grønli
Kristiania University College

Hermann Kaindl
TU Wien

New approaches, methods, and tools (some of which are AI-enabled) for facilitating more responsive organizations are proliferating rapidly. Among the evolving methods are approaches such as Agile, lean, DevOps, BizDevOps. In addition, low-code and co-pilot platforms are being increasingly used to accelerate software development but can create organizational challenges.

The organizational challenges are numerous. For example, Agile was initially designed for colocated, onsite teams, but organizations today cope with scaling issues and remote and hybrid work. Low code software development enables non-software development personnel to create applications, but those personnel may lack sufficient knowledge of good software development practices. The challenges with artificial intelligence-developed code are similar, but perhaps even more extreme. Lean business models assume collocated access to the customer, but often startups now serve geographically dispersed customers.

In this minitrack, we seek research papers and experience reports that explore practices, tools, and techniques for rapid development. We also seek to explore how these concepts can be leveraged in other contexts (such as data science or physical product development). Practitioners interested in submitting an experience are welcome to reach out to a minitrack co-chair for support and guidance, if desired.

Our minitrack seeks to answer questions such as:

  • How can emerging technologies like AI and machine learning be seamlessly integrated into existing software development practices to enhance efficiency and effectiveness?
  • How to balance team autonomy and decentralized decision-making with the need for organizational control and alignment in large-scale agile development?
  • How can agile and lean can be integrated within a single coherent approach?
  • Which metrics help enterprises, teams and individuals adapt and improve? What common behaviors do we see in agile or lean teams and how do those behaviors affect outcomes?
  • How do organizations implement, monitor and improve hiring, coaching, training and mentoring?
  • How to scale agile (how to effectively manage dependencies, teams, stakeholders, processes, technologies, and tools)including comparative results on the use of different agile scaling frameworks?
  • How can agile be implemented within other contexts (e.g., data science,BizDevOps)?
  • What organizational structures are required to enable shared leadership in self-managed teams?
  • How to balance the need for effective coordination and focused work in an agile team?
  • How do agile and lean principles extend to DevOps environments? Is there a difference between agile and lean before and after deployment? How are post-deployment issues and opportunities in software projects impacting planning and development of software development projects?
  • What organizational structures and novel tools are required to leverage AI, low-coding, and rapid-prototyping as part of the project management process?
  • What are the best practices for maintaining efficiency and effectiveness in remote or hybrid agile teams?
  • How can agile teams ensure inclusivity and leverage diversity to enhance teamperformance and innovation?

Possible additional topics for the minitrack include but are not limited to:

  • AI-enabled code development tools
  • AI-enabled team collaboration and communication tools
  • New frontiers in agile or lean management – going beyond software development.
  • Forecasting, planning, testing, measurement, and metrics
  • Exploring the fit between agile (or lean) organizations and their environmental context
  • Agile and lean requirements engineering, and risk management
  • Agile in hybrid digital/physical contexts
  • What cultures, team norms and leadership characteristics lead to sustained agility?
  • Empirical studies of agile or lean organizations
  • Impact of tool use on agile or lean management
  • Education and training –new approaches to teaching and coaching agile
  • Global software development and offshoring/multi-shoring
  • Rapidly reconfigurable multi-sided platform ecosystems
  • Project management methods, low code development
Minitrack Co-Chairs:

Jeffrey S. Saltz (Primary Contact)
Syracuse University

Edward G. Anderson
University of Texas at Austin

Alex Sutherland
Scrum Master

Viktoria Stray
University of Oslo

With the advancement of AI technology, AI algorithms start to match human performance for certain tasks (e.g. ChatGPT) and discover loopholes in systems that were not previously found.  AI in general and ML methods specifically are increasingly used with scientific data and applied with great promise to solve a broad variety of scientific problems.  With the increased use of AI comes an increase in inherent complexity.  Deep Learning (DL) models with billions of parameters, operating with very large data volumes on heterogeneous architectures, obscure their inner workings to human understanding.  Unlike traditional ML algorithms, such as rule-based decision trees or linear-regression models where the decision boundary is clear, interpreting a learned model is difficult.

The increased need for transparency is compounded by that of avoiding bias in predictions.  Numerous examples of bias have been discovered in image recognition, classification, and text generation. Formal explanations of how models achieve results, explicit representations of data, comprehensiveness and diversity of datasets used for training are crucial to foster trust in AI. Additionally, the accuracy of results obtained with AI is often the product of customization; experiments show that many are not reproducible at scale, even within expected error bounds. While reproducibility may not be needed for some uses of AI (e.g. when AI is used for the purpose of preliminary triage in drug discovery) in other uses, reproducible AI is paramount.    

Researchers need to understand how Artificial Intelligence (AI) and Machine Learning (ML) results are obtained in order to gain new insights and to establish confidence in the validity of these results. The promises of AI/ML will not be realized if scientists cannot trust the results, understand how they were obtained, gain transparency into what datasets, models and model parameters have been used or what features in the data lead to results. Like any good scientific results, AI/ML pipelines should be reproducible to the most possible extent. 

This minitrack will explore a number of themes related to explainable, reproducible, ethical, and trustworthy AI.  Papers will be published with conference proceedings. Topics of interest include but are not limited to the following:

 Computational and foundational methods for explaining AI models (XAI)

Minitrack Co-Chairs:

Line Pouchard (Primary Contact)
Brookhaven National Laboratory

Peter Salholer

There has been an explosion of work using Large Language Models (LLMs) to assist with the development and evaluation of programs. There is a wide diversity of opinion ranging from one extreme claiming that LLMs will replace programmers to suggesting that the use of LLMs will fast track poor and vulnerable code into enterprise systems to the expected use of LLMs to generate malicious code. This minitrack solicits empirical results and evaluations of LLMs used to create software or to evaluate software. Topics include but are not limited to:

  • Methods for using LLMs to generate or evaluate software
  • Comparisons of LLMs vs other source code analysis tools
  • Evaluation of the suitability of domains and/or languages for using LLMs
  • Comparisons of LLMs for effectiveness in creating or evaluating software
  • Methods of training LLMs to improve performance in creating or evaluating software
  • Effects of prompt-engineering on the effectiveness of LLMs in creating or evaluating software
  • Detection of LLM-generated source code in programs
  • Integration of LLMs with other software engineering and development tools and environments
  • Teaching the use of LLMs as part of the software development process
  • Guardrails for LLMs generating software to prevent poor or malicious software
  • Use of LLMs for SCA (Software Composition Analysis) and supply chain risk management
  • Presence of LLM generated software in the open source community
  • Use of LLMs to reverse engineer binary software to source code
  • Using LLMs to create invariants for use in proof systems
  • Using LLMs as proof assistants for programs
  • Veracity of explanations from LLM summarization of software
  • How to find evidence of poisoned LLMs used to generate vulnerable software
Minitrack Co-Chairs:

Mark Sherman (Primary Contact)
Carnegie Mellon University

Douglas Schmidt
Vanderbilt University