The HICSS conference has several software-related minitracks that may interest the BSSw.io community.
| Event Information | Details |
|---|---|
| Event Name | Hawaii International Conference on System Sciences (HICSS 58) |
| Paper submission deadline | June 15, 2024 |
| Event Dates | January 7-10, 2025 |
| Website | https://hicss.hawaii.edu/ |
Several minitracks within the Software Technology track of the HICSS 58 conference may be of interest to this community:
Application Of Generative AI in Software Development
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.
Software Sustainability: Research on Usability, Maintainability and Reproducibility
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
Impact of AI on Software Engineering
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
Tools, Processes, and Models for Enabling Efficient and Agile Projects, Teams, and Organizations
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 team performance 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
