/ Building an Advanced Software Engineering Agent

Description

This presentation details the development of an experimental software engineering agent designed to answer complex questions about codebases. The core innovation lies in a graph-based approach where the agent iteratively gathers contextual information, using self-reflection to assess its confidence in answering the query. By treating accumulated context as a set and employing a greedy best-first search guided by confidence scores, the system aims to efficiently navigate the codebase and provide accurate answers. Initial findings are promising, with future benchmarks on real-world software engineering challenges planned to further validate its capabilities.

Session 🗣 Expert Track: AI, ML, Bigdata, Python