quality_assurance · saas · workflow
Pure Storage uses Augment to boost developer efficiency across a massive C++ codebase
Pure Storage's engineering teams struggled with a massive multi-language C++ codebase — debugging memory leaks and segmentation faults, navigating unfamiliar code sections, and accelerating onboarding ahead of an impending team merger. GitHub Copilot proved insufficient for these needs.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Engineering challenge triggers AI use
Engineers face challenges with a large C++ codebase, requiring assistance with debugging, onboarding, and code navigation.
Tools used
AugmentGitHub Copilot
Outcome
Pure Storage accepted over 130,000 AI completions into the codebase with 77% of chat-driven suggestions implemented, reduced onboarding time from months to weeks, and significantly accelerated debugging and code navigation across engineering teams.
What failed first
GitHub Copilot, their initial AI coding assistant, was insufficient — its suggestions were context-unaware and unprofessional, and it could not handle the complexity of navigating the large C++ codebase.
Results
Time savedreducing onboarding time from months to weeks
Volumeover 2.1 million lines of C++
Grounding & classification
Source type: vendor customer story
25 fields verified against source quotes.
agent assistcode generationknowledge searchcode diff prfailure mode describedmetric backednamed customerproduction runtime claimedsource backedtools describedworkflow describedsoftwarecycle time reductionemployee productivitytime savedvendor customer storyhr onboardingquality assuranceai draft human approval