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Endigest AI Core Summary
This post explains how Grab enabled R8 optimization for their large-scale Android app by developing AI-assisted debugging techniques to overcome three core challenges.
•R8 optimization beyond basic shrinking provides method inlining, class merging, constant folding, dead code elimination, and devirtualization to improve runtime performance and reduce app size
•The app spans 9 million lines of code with 100+ engineers, making obfuscated stack traces and manual reverse engineering prohibitively slow to investigate
•A custom MCP server was built to automate APK decompilation, stack trace deobfuscation, and decompiled code context extraction for AI-assisted analysis
•AI uses GitLab CLI (glab) to create multiple Merge Requests in parallel for different fix approaches, each triggering a CI build to verify solutions
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R8 debug and release build alignment was enforced by matching QA build R8 config to production to catch issues before release
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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