Deadline for submissions - Monday, 15 September 2025

This page is only for submissions for the Open Source AI (OSAI) meet-up on 1st November in Hyderabad.

AI in Software Lifecycle Development

→ Why Software Life Cycle Development?
The Software Development Life Cycle is the foundation of technology creation. By embedding AI into each phase - from initial planning and requirements gathering to coding, testing, deployment, and ongoing maintenance - we can unlock significant improvements in productivity, software quality, development speed, and cost-effectiveness. It’s about fundamentally transforming the craft of software development.

Topics for submission

We are looking for compelling talks and live demonstrations (approximately 25-35 minutes, including Q&A) that showcase practical applications, real-world use cases, and challenges in integrating open-source AI into the SDLC. Submissions should ideally focus on:

1. Leveraging open source NLP for AI-assisted requirements analysis and user story generation: Share your experiences with tools like llama, mistral, spaCy, NLTK, or Hugging Face models for analyzing user feedback, deriving requirements, or aiding product teams in creating structured user stories.
2. Practical applications of open source code generation and refactoring tools: Present demos and insights on using open-source LLMs and AI tools for code completion, suggesting refactoring patterns, identifying code smells, or automating code generation.
3. Enhancing test automation and strategy with Open Source AI tools: Discuss how AI (using open-source libraries) can improve test case generation, manage test data, predict defects, or optimize test execution strategies.
4. Implementing Open Source AIOps for intelligent monitoring and incident response: Showcase the use of open-source AI/ML platforms (e.g., Prometheus, Grafana, ELK Stack with AI plugins) for anomaly detection, root cause analysis, and automating operational tasks.
5. Role of Open Source AI in securing the SDLC: Explore how open-source AI tools can assist in static/dynamic code analysis, identify security vulnerabilities, and improve DevSecOps practices.
6. AI ethics and bias mitigation in open-source SDLC: Discuss tools like Fairlearn or AIF360 for detecting biases in code and requirements, emphasizing responsible AI practices.
7. Agentic AI for collaborative development: Explore multi-agent systems (e.g., using AutoGen) for automated workflows across teams.
8. Sustainable AI practices in SDLC: Focus on optimizing open-source models for lower carbon footprints, aligning with India’s green tech initiatives.

Workshop topics and master classes

We invite proposals for interactive workshops and in-depth masterclasses (typically 2-4 hours) that provide attendees with practical skills and foundational knowledge. These sessions can explore topics within the SDLC theme or extend to broader enabling technologies in the open-source AI ecosystem.

1. Hands-on: building a simple AI Assistant for developer productivity (using Open Source Libraries): A practical workshop using open-source, GenAI/ NLP/ML libraries (e.g., spaCy, scikit-learn, Hugging Face transformers) to build a tool automating a specific development task (e.g., documentation summarization, issue classification).
2. Prompt engineering for developers and QAs: getting the most from AI Assistants: A skill-building session on writing effective prompts for AI coding assistants, test case generators, and documentation tools.
3. Building your first open source chatbot with: A hands-on workshop on building a conversational AI agent, demonstrating principles of AI-powered digital product creation.
4. Understanding and contributing to Open Source LLMs: A deep dive into the architecture and potential of open-source Large Language Models and how developers can contribute or fine-tune them for SDLC tasks.

Guidelines for speaking

1. Here’s a guide to draft presentations.
2. Guidelines for speaking and how to draft your submission are explained at https://hasgeek.com/fifthelephant/osai-hyd-meetup-cfp/sub

About the curator

Ramakrishna Reddy Yekulla (Ramky) is a Senior Principal Product Manager at Red Hat’s data + AI group, leading the technical strategy and operationalization of AI models. He ensures seamless integration of AI into Red Hat’s products while managing compliance, governance, and scalability.
A seasoned open-source contributor, Ramky has worked on projects like Fedora, Django, GNOME, and GlusterFS. His expertise spans system design, functional programming, and AI infrastructure.

Queries & contact information

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