The post includes breakdowns by PR category (bug fixes, tests, refactors, new features), common failure modes (build breaks, test failures, incorrect logic), and the types of tasks where Copilot performs well versus poorly.
In conclusion: Copilot is excellent at implementing ๐๐ฒ๐น๐น-๐๐ฝ๐ฒ๐ฐ๐ถ๐ณ๐ถ๐ฒ๐ฑ ๐ฐ๐ต๐ฎ๐ป๐ด๐ฒ๐, ๐๐ฒ๐ฟ๐ ๐ด๐ผ๐ผ๐ฑ ๐ฎ๐ ๐ถ๐ป๐๐ฒ๐๐๐ถ๐ด๐ฎ๐๐ถ๐ป๐ด ๐ถ๐๐๐๐ฒ๐, ๐ฎ๐ป๐ฑ ๐ฟ๐ฒ๐น๐ฎ๐๐ถ๐๐ฒ๐น๐ ๐ฝ๐ผ๐ผ๐ฟ ๐ฎ๐ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐ถ๐ป๐ด ๐๐ผ๐น๐๐๐ถ๐ผ๐ป๐, ๐ฒ๐๐ฝ๐ฒ๐ฐ๐ถ๐ฎ๐น๐น๐ ๐ถ๐ป ๐น๐ฎ๐ฟ๐ด๐ฒ ๐ฐ๐ผ๐ฑ๐ฒ๐ฏ๐ฎ๐๐ฒ๐ ๐๐ต๐ฎ๐ ๐ฟ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ ๐ฏ๐ฟ๐ผ๐ฎ๐ฑ ๐๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ๐ถ๐ป๐ด. If your team is seriously evaluating AI coding agents, this is a donโt-miss post: the dotnet/runtime context provides real-world complexity that synthetic benchmarks cannot replicate.