anhnd85

Call for Book chapters – Effective Software Development with Generative Artificial Intelligence

Book editors

  1. Prof. Anh Nguyen-Duc, University of South Eastern Norway.
  2. Prof. Pekka Abrahamsson, Tampere University, Finland
  3. Prof. Foutse Khomh SWAT Lab., Mila – Quebec AI Institute, Polytechnique Montréal, Canada

Important dates

  • Submission of the executive summary: 15th June 2023 
  • Submission of the first draft: 1st September  2023
  • Notification of acceptance: 15th September  2023
  • Camera-ready submission: 15th October 2023

All times are in Anywhere on Earth (AoE) time zone. 

Motivation

Generative Artificial Intelligence (GenAI) is a type of artificial intelligence (AI) system capable of learning the patterns and structure of the input data, and then generating new content, including text, images, media, and source code (Goodfellow et al. 2020; Ruthotto et al. 2021). Popular examples of GenAI tools applied for these purposes include ChatGPT and GitHub Copilot, which are rapidly adopted by software developers and applied to generate code and other artifacts associated with software engineering. ChatGPT from OpenAI offers various opportunities for problems relating to text processing and analysis. Copilot from GitHub specifically focuses on assistive features for programmers, such as the conversion of code comments to runnable code and autocomplete for chunks of code. These technologies are developing fast, i.e. GPT-5 version of ChatGPT or CoPilot X are planned to release. 

What makes GenAI tools differ from these works is the availability of functionalities and services around them (White et al. 2023). CoPilot is already integrated in IDE so that any developer can access to these model complex capabilities by simply typing a message and making their selections. ChatGPT is available as a free chatbot and also in API format. Previously, leveraging these capabilities required substantially more time and effort. In addition, prior state-of-the-art ML models were not widely accessible to users. We would expect the number of AI-based tools for software engineering and the amount of evidence in the adoption of AI-based technologies in software companies rapidly grown in the next few years.

We aim at synthesizing and extending knowledge about the latest development of GenAI and its application in Software Engineering. This includes providing a comprehensive overview of the technology and relevant concepts, i.e. language models, prompt engineering, etc. Our goal is to explore, identify and demonstrate how GenAI can be used to improve quality and productivity in software development processes and share best practices, case studies, and success stories of using GenAI in software development, including the benefits and limitations of these approaches. The book shall provide guidance and recommendations for developers and organizations interested in adopting GenAI tools for software development, including ethical considerations and potential risks.

While this book covers a broad range of topics related to GenAI and its practical applications in software development, some topics will not be included. Specifically, we do not focus on developing and evaluating machine learning models in depth, as these topics are more suited for specialized machine learning books. We also do not focus on topics in SE4AI, i.e. software engineering frameworks, techniques or practices to develop AI systems.

Contribution types

We invite both scientific and practical types of contributions, including:

  • Conceptual papers
  • Review, secondary papers
  • Empirical studies, including case study, experience reports, observation, action research, etc
  • Industry survey
  • Practitioner’s reports

Topics

This book is aimed at anyone interested in understanding the application of the latest AI technologies in software development and management, including software developers, architects, project managers, data scientists, machine learning engineers, researchers, educators, and students.

The book chapter should belong to one of three categories: (1) State-of-the-art Generative Artificial Intelligence, (2) GenAI tools in software engineering tasks and (3) GentAI tools in software project management and software business. 

Topics include, but are not restricted to:

  • Recent development on Generative Artificial Intelligence
  • The role of AI in Agile software development
  • AI-powered User Story Mapping and backlog prioritization
  • Open source AI models
  • Comparing different AI-assisted tools on Software Engineering tasks
  • Comparing between human and AI-assisted tools in task performance and quality
  • GenAI tools in Requirement Engineering and evaluation
  • GenAI tools in Software Architecture and evaluation
  • GenAI tools in Implementation and evaluation
  • The impact of AI-powered NLP tools on software testing and quality assurance
  • GenAI tools in Project Management
  • Application of AI-assisted tools in Agile software project
  • AI-assisted tools and project communication and collaboration
  • AI-assisted tools for no code/ low code development
  • Legal, social and ethical concerns with adopting GenAI tools in different contexts
  • Prompt engineering, prompt patterns for specific Software Engineering tasks
  • Process aspect of adopting GenAI in Software Engineering
  • GentAI tool business models
  • Case studies on AI/MLOps

While this book covers a broad range of topics related to GenAI and its practical applications in software development, some topics will not be included. Specifically, we do not focus on developing and evaluating machine learning models in depth, as these topics are more suited for specialized machine learning books. We also do not focus on topics in SE4AI, i.e. software engineering frameworks, techniques or practices to develop AI systems. All chapters should have empirical evidence in a form of case studies, experiments, action research and survey.

Submission process

Step1: Your confirmation on the contribution, please send us a (preliminary) executive summary:

  • EXECUTIVE SUMMARY: In a few paragraphs, explain the information your chapter will cover. This section should give people a reason to continue reading. Be sure to summarize your main points and key takeaways. No format is required!

Step2: Research and paper writing

We expect high quality contribution, which will pass a peer-review process. The book chapter should be written in the following format:

  • INTRODUCTION: Give a context of the investigated problem. Present the current understanding of the problem. State the purpose of the work in the form of the hypothesis, question, or problem you investigated. Use the active voice as much as possible. Some use of first person is okay, but do not overdo it.
  • RELATED WORK: briefly summarise relevant scientific or known publications, i.e. book, scientific papers, white papers, etc.
  • RESEARCH METHOD: briefly describes how the results were generated.
  • MAIN POINT 1: Cluster the points you want to make. Provide evidence to support your arguments
  • MAIN POINT 2:
  • CONCLUSION: Discuss the findings. Provide implications for entrepreneurs, and researchers.

Follow-up discussion will be done with each chapter author to ensure the development of chapters regarding its relevancy, quality and timeline. 

Step 3: Submission and revision

After submissions, your draft will be reviewed with a request for revisions. This will also be a collaborative process between editor teams and chapter authors

Submission

Submissions must conform to Springer’s LNCS format and should not exceed 15 pages, including all text, figures, references and appendices. Information about the Springer LNCS format can be found at http://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines.

The executive summaries and chapters should be submitted via Easy Chair:   https://easychair.org/conferences/?conf=genai4ese

Selected references

If you are interested in contributing to the book, please do not hesitate to write to us at angu@usn.no

Leave a comment