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Context and Aims: Large Language Models (LLMs) in the context of Software Engineering (SE) refer to advanced AI models that are trained on massive amounts of text data, including code repositories, programming documentation, and other software-related resources, designed to enable automation, provide decision support, and process natural language instructions in the software development process. Software engineering process can include a number of activities such as software architecting, source coding, and system testing etc. that can be automated using LLM-based tools.
The aim of this call for book chapters is to establish a foundation that fosters research and development initiatives to produce publishable, applicable results and solutions that are focused on LLMs for SE. The chapters can provide a platform to discuss active and emerging challenges, design and development of solutions to address those challenges and identify areas of futuristic research to develop emerging and next-generation of LLMs for SE processes and practices.
Potential Contributors and Audience: Authors could be experts in the field of software engineering and AI, while readers may include software engineers, AI researchers, and technology enthusiasts.
Academic researchers focused on investigating the state-of-the-art, emerging and futuristic challenges, novel solutions, experiences, opinions and other relevant aspects of research and development of LLMs for SE.
Industrial practitioners who may be interested in notations, tools, frameworks and patterns for the engineering and development and application of LLMs in an industrial context.
Objective and Implications: The objective is to provide a comprehensive guide on how to use LLMs-based bots for streamlining software engineering processes. The implications include reshaping software engineering practices and potentially revolutionizing the industry by employing AI-driven automation. This call aims to find answers to some fundamental questions such as,
How LLMs can support SE processes?
What existing tools, technologies, and architectures are available to support LLM-driven SE?
Which systems, domains, and applications can benefit from application of LLMs in SE context?
Key Terms: Large Language Models (LLMs), Software Engineering (SE), Artificial Intelligence (AI)
Topics of Interest
Introduction
Introduction to LLM and its applications
Overview of existing LLM tools and frameworks
Towards a software engineering paradigm shift
Integration of Bots with Open and Commercial Software
Future directions for LLM research
Applications of LLMs in SE Processes
Enhancing requirements engineering with LLMs
LLM-supported software architecture design
Code generation with LLMs
LLMs as Debugging and troubleshooting assistance
Code review and analysis
LLMs in test case generation
Enhancing communication and collaboration
Integrating LLMs into agile development processes
LLM-driven project management assistance
Software documentation generation with LLMs
LLM-assisted codere factoring
Infrastructure planning supported by LLMs
LLM-informed deployment strategies
Configuration management using LLMs
LLMs-supported containerization and orchestration
LLMs-supported continuous integration and delivery (CI/CD)
Monitoring and logging support
Performance optimization using LLMs
LLMs in Software Engineering Research
Literature rereview
Identifying research gaps
Hypothesis generation
Experiment design
Data collecting and analysis
Addressing Bias and Ensuring Fairness in Research and Development with LLMs
Ethical, Legal, and Regulatory Considerations of LLMs in SE
Data privacy and security
Intellectual property concerns
Addressing bias and ensuring fairness
Legal and regulatory aspects, such as GDPR and CCPA
Exploring the Downsides of LLM Bots
Balancing human-AI collaboration in software engineering
The impact of LLMs on employment and workforce
Mitigating the risk of malicious use of LLM technology
Developing ethical guidelines for LLM deployment
Case Studies and Best Practices
Case study: LLMs improving code quality and maintainability
Case Study: Code generation with LLMs
Case study: LLMs in the development of secure applications
Case study: LLMs optimizing CI/CD pipelines
Case study: LLMs in generating automated tests
Case study: LLMs facilitating software documentation
Best practices for selecting and adopting LLM tools
Best practices for integrating LLMs in agile development
Best practices for human-AI collaboration in software teams
Important Dates
Chapter submission deadline (Revised) - 10 August 2024
Editorial review and feedback to authors - 10 Sept 2024
Revised chapter submission deadline - 15 Nov 2024
Final proofreading and submission to publisher - Dec 2024
Please submit your book chapters on : muhammad.m.waseem@jyu.fi
Editorial Team
Muhammed Waseem
University of Jyväskylä,
Finland
muhammad.m.waseem@jyu.fi
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