Glossary
By the Glue Team
Code intelligence is the ability to understand, analyze, and derive insights from source code programmatically. Code intelligence tools read and interpret codebases to answer questions, identify patterns, extract information, and provide visibility into how software systems are built. It's the foundation of modern codebase analysis.
Code intelligence encompasses techniques and tools that analyze source code to:
Code intelligence is more advanced than simple text search. It understands code semantically—it knows what a variable does, which functions call which, where data flows, and how systems interconnect.
Code intelligence solves critical problems in modern software development:
Product Teams Lack Codebase Visibility: The typical product manager cannot code. When they need to understand what features exist, what's possible to build, or what's slowing down development, they're dependent on developers for answers. Code intelligence makes this information accessible. Product managers can ask "Do we already have payment retry logic?" or "Which features use this library?" without blocking on developer availability.
Onboarding New Developers Takes Too Long: New developers spend weeks reading code to understand how systems work. Code intelligence accelerates this by providing answers on demand. "What does this service do?" gets answered immediately instead of in a meeting.
Technical Decisions Are Made Without Full Context: When deciding whether to refactor something, add a feature, or migrate to a new technology, teams lack complete information. Code intelligence surfaces what already exists, what's connected, and what would break.
Technical Debt Accumulates Invisibly: Technical debt grows because no one sees it. Code intelligence makes technical debt visible. You can identify where complexity is highest, where documentation is missing, where critical dependencies hide.
Competitive Analysis Becomes Guesswork: Understanding competitor capabilities requires analyzing their product, but this only shows what users see. Code intelligence on your own codebase shows what you could build but haven't exposed yet, helping with competitive positioning.
Code intelligence uses multiple approaches:
Static Analysis: Parsing code without executing it. Tools build abstract syntax trees (ASTs) that represent code structure. This reveals dependencies, function calls, variable usage, and control flow.
Semantic Understanding: Beyond just parsing structure, code intelligence understands semantics—what code actually does. This includes understanding type systems, data flow, and function contracts.
Pattern Recognition: Code intelligence identifies patterns. It can spot duplicate logic, similar implementations, and common architectural patterns.
Dependency Mapping: Understanding not just what code exists, but how it connects. Which services call which? Which databases do features use? What happens if you modify this module?
Metrics Extraction: Computing metrics like complexity, test coverage, maintainability indices. Quantifying technical debt.
Natural Language Processing: Modern code intelligence combines static analysis with AI. You can ask questions in English: "What payment methods do we support?" or "Which features are using deprecated libraries?" and get answers.
Comparative Analysis: Comparing your codebase to competitors'. Understanding what features they have that you don't. Identifying your unique capabilities.
Glue uses advanced code intelligence to provide Feature Discovery (what features exist in your codebase), Codebase Q&A (answer questions about how your code works), Gap Atlas (competitive feature gaps), and Technical Debt Visualization. This makes codebase knowledge accessible to non-technical stakeholders while helping developers understand their own systems better.
Product Discovery: Product managers use code intelligence to understand what features actually exist in the product, beyond what's documented in the product spec. This prevents building duplicate features and informs roadmap decisions.
Architectural Understanding: Engineers use code intelligence to understand system architecture—which services talk to which, where bottlenecks exist, what's tightly coupled.
Impact Analysis: When you want to change something, code intelligence shows what else might break. This reveals hidden dependencies and helps estimate effort.
Competitive Gap Analysis: Analyze competitor codebases (if publicly available, like open source projects) to understand what features they have. Compare to your own codebase to identify gaps.
Technical Debt Prioritization: Rather than guessing where technical debt is highest, use code intelligence to identify complexity hotspots and dependencies. Pay down debt that blocks the most other work.
Knowledge Preservation: Code intelligence makes codebases self-documenting. Rather than depending on one person's knowledge, the system extracts and shares information.
"Code intelligence is just static analysis." Partly true. Static analysis is part of it, but modern code intelligence adds AI, semantic understanding, and natural language interfaces on top.
"Code intelligence requires the team to use special tools." Partly false. Code intelligence works on any codebase. Developers don't need to use special tools—code intelligence reads their existing code.
"Code intelligence replaces code reviews." False. Code reviews provide human judgment and discussion. Code intelligence provides information that helps reviews be more effective.
"Code intelligence is only for large teams." False. Small teams benefit too. Code intelligence prevents knowledge concentration and helps small teams ship faster.
Codebase Documentation: Information about how code works. Code intelligence generates this automatically rather than relying on manual documentation.
Technical Debt Visualization: Making technical debt visible. Code intelligence is how you quantify and visualize debt.
Feature Inventory: Understanding what features exist. Code intelligence powers feature discovery.
Gap Atlas: Identifying missing competitive features. Code intelligence enables comparative analysis.
Q: Does code intelligence work on all programming languages? A: Most modern code intelligence tools support major languages (Python, JavaScript, Java, Go, Rust, etc.). Support varies—some languages have more mature tooling. But the principles apply to any language with a parser.
Q: Can code intelligence understand why code was written, not just what it does? A: Code intelligence can understand what code does through analysis. Understanding why requires looking at commit messages, tickets, or documentation. Code intelligence can correlate these sources to provide context, but "why" is harder to extract programmatically.
Q: How accurate is code intelligence? A: Accuracy depends on complexity. Simple questions ("Does this function exist?") are highly accurate. Complex questions ("What's the impact of removing this library?") require more analysis and have more uncertainty. Modern AI-powered code intelligence reduces false positives through semantic understanding.
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