Glossary
By the Glue Team
Codebase intelligence is the application of AI and advanced analytics to source code to provide insights, answer questions, and drive product decisions. It's the category of tools that make codebases intelligible to product teams, developers, and leaders—regardless of technical background.
Codebase intelligence combines:
Codebase intelligence makes code knowledge accessible to non-technical stakeholders. A product manager can ask "What payment methods do we support?" and get an answer without reading code or asking engineers.
Codebase intelligence solves critical product and engineering problems:
Product Managers Lack Technical Context: 95% of product managers can't code. They can't open the codebase and understand what's built. This creates dependency on engineers for basic questions. "Is this feature already built?" blocks decision-making. Codebase intelligence makes this information accessible.
Technical Knowledge Concentrates: Critical knowledge about architecture, features, and capabilities lives in a few engineers' heads. This creates bottlenecks and risk. Codebase intelligence democratizes knowledge—it becomes available to the whole team.
Faster Decision-Making: Decisions about what to build, what to prioritize, and what to fix are made with incomplete information. Codebase intelligence provides the information needed for better decisions.
Reduced Meetings: Instead of scheduling meetings with experts to understand systems, teams can query the codebase. This saves time and lets experts focus on building.
Competitive Intelligence: Understanding your own codebase enables competitive gap analysis. What features do you have that competitors don't? What are you missing? Codebase intelligence surfaces this.
Onboarding Acceleration: New developers can ask the codebase questions instead of waiting for mentors. "What does this service do?" gets instant answers. Onboarding accelerates from weeks to days.
Feature Discovery: What features exist in the codebase? Where are they implemented? What backend logic supports them? Codebase intelligence catalogs all features automatically.
Codebase Q&A: Natural language questions about the codebase. "What payment methods do we support?", "Which services handle user authentication?", "Where is retry logic implemented?" Get answers immediately.
Complexity Visualization: Where is code most complex? Where would refactoring have biggest impact? Codebase intelligence maps complexity, making technical debt visible.
Dependency Mapping: Which services call which? Which features depend on which libraries? Change one component—what breaks? Dependency visualization shows these relationships.
Competitive Gap Analysis: Compare your codebase to competitors'. What features do they have that you don't? Where are you ahead? Codebase intelligence enables objective gap analysis.
Technical Debt Prioritization: What technical debt is slowing down your team most? Codebase intelligence identifies high-impact debt. Prioritize paydown efforts where they matter.
Architecture Understanding: How is the system organized? What are its layers? Codebase intelligence generates architecture documentation automatically.
Impact Analysis: Want to change something? What would break? Codebase intelligence traces dependencies to show impact.
Ingestion: Codebase intelligence tools read your codebase. They parse code into abstract syntax trees (ASTs) that represent structure.
Analysis: The AST is analyzed to extract information: function calls, variable usage, dependencies, data flow. This information is stored and indexed.
Semantic Understanding: AI language models process the code to understand semantics. Not just syntax (what the code structure is) but semantics (what it actually does).
Indexing and Storage: Extracted information is indexed for fast querying. This enables answering questions quickly.
Generation: Natural language models generate explanations from code. They can describe what a function does, summarize a service, or explain architecture.
Comparison: Codebase intelligence can compare your code to competitor code (if publicly available) to identify gaps and differentiation.
Glue combines these approaches to provide Feature Discovery (what features exist), Codebase Q&A (answer questions about your code), Gap Atlas (competitive gaps), Technical Debt Visualization, and more.
Code Search: Tools like grep find text. Codebase intelligence understands meaning. "Find payment-related code" is understood semantically, not just as text matching.
Linters: Linters check style and catch bugs. Codebase intelligence provides understanding and navigation.
Code Review: Code reviews evaluate changes. Codebase intelligence provides context about systems, dependencies, and impact.
Documentation: Manual documentation is static. Codebase intelligence is dynamic, always up-to-date with actual code.
Static Analysis: Static analysis identifies issues. Codebase intelligence explains systems and enables navigation.
Codebase intelligence doesn't replace these tools; it complements them.
"Codebase intelligence requires special tooling." Partly false. It's better with specialized tools, but principles apply to any codebase and any code language.
"AI understanding of code is unreliable." Partly true. AI isn't perfect, but modern language models understand code surprisingly well. Errors happen but are rare. Combined with static analysis, reliability is high.
"Codebase intelligence is only for large codebases." False. Small codebases benefit too. Faster onboarding, clearer decision-making.
"Codebase intelligence replaces code review." False. Code review is about evaluating changes. Codebase intelligence provides context. They're complementary.
Code Intelligence: Broader category. Codebase intelligence is specific to understanding entire codebases.
Feature Discovery: Finding features. Codebase intelligence enables it.
Technical Debt Visualization: Making debt visible. Codebase intelligence quantifies it.
Gap Atlas: Competitive gap analysis. Codebase intelligence makes it possible.
Q: What information does codebase intelligence need? A: Just your source code. No additional configuration or markup needed. Codebase intelligence reads code as-is.
Q: How accurate is codebase intelligence? A: Very accurate on straightforward questions ("What does this function do?"). Less certain on complex questions ("Why was this decision made?"). Accuracy improves with codebase quality and documentation.
Q: Does codebase intelligence work on all languages? A: Most modern languages (Python, JavaScript, Java, Go, Rust, etc.) are well-supported. Less common languages have less mature support. The core principles apply to any language.
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