The field of software engineering is undergoing significant transformations with the integration of agile practices, machine learning (ML), and artificial intelligence (AI). A common theme among recent research areas is the focus on improving the efficiency, sustainability, and reliability of software development processes.

Researchers are exploring the impact of agile practices on employee performance, highlighting the need for comprehensive studies in diverse contexts. The sustainability of ML-enabled systems is also becoming a key concern, with a growing recognition of the need for structured guidelines and measurement frameworks to support environmentally, socially, and economically responsible ML development.

The integration of digital engineering and ML components is gaining traction, with a focus on improving the efficiency and predictability of complex system development and sustainment projects. Noteworthy papers in this area include proposals for integrating ML components into software product lines and studies on the return on investment of digital engineering for complex systems development.

The use of Generative AI (GenAI) and Large Language Models (LLMs) is becoming increasingly prevalent in software engineering. GenAI is moving beyond code generation, with an emphasis on collaborative approaches that position AI as a partner rather than a replacement for human expertise. LLMs are being used to automate tasks such as code migration, automated testing, and release note generation, with promising results in improving the efficiency and effectiveness of software development processes.

Innovative applications of LLMs include agentic LLMs for REST API test amplification and iterative feature-driven frameworks for end-to-end software development with LLM-based agents. The integration of LLMs with agent-based systems has enabled the development of more robust and scalable software engineering frameworks.

The field of compiler development and code analysis is also witnessing a significant shift with the integration of LLMs. Researchers are exploring the potential of LLMs in automating coding tasks, such as generating code from scratch, repairing software, and decompiling binaries. Noteworthy papers in this area include proposals for new methodologies for writing code from scratch using LLM assistance and evaluations of the capability of LLMs to repair build failures in cross-ISA settings.

Finally, the field of hardware design and optimization is leveraging LLMs as interactive agents that collaborate with compilers and hardware feedback to optimize code generation and improvement. Innovative frameworks and workflows are being proposed to address the challenges of noise propagation, constrained reasoning space exploration, and limited parametric knowledge. These advancements have the potential to revolutionize the field by enabling cost-effective, generalizable, and high-performance hardware design and optimization.

Overall, the integration of agile practices, ML, and AI is transforming the field of software engineering, with a focus on improving efficiency, sustainability, and reliability. As research in these areas continues to evolve, we can expect to see significant advancements in the development of innovative tools, frameworks, and methodologies that support the creation of high-quality software systems.