Key terms in codebase intelligence, product management, technical debt, and software engineering — defined clearly.
Agile estimation is the process of predicting work effort using iterative, team-based methods like story points and velocity.
AI competitive analysis uses artificial intelligence to automate competitor monitoring, feature tracking, and gap identification.
An AI product roadmap plans the development and iteration of AI-powered features and products over time.
AI feature prioritization uses machine learning to score and rank features based on impact, effort, and strategic alignment.
Automated code insights use AI and static analysis to surface patterns, risks, and opportunities from codebases without manual review.
An AI product manager specializes in building and managing AI-powered products. Gartner calls it a 'critical missing role.'
A DevEx platform improves how developers interact with their tools, codebases, and infrastructure.
AI for product strategy uses artificial intelligence to inform product decisions, from roadmapping to competitive positioning.
A competitive battlecard is a one-page reference document that helps sales reps compete against specific competitors.
A feature inventory is a complete catalog of all features in your product, mapped to code, usage, and ownership.
Knowledge silos form when information is trapped in individuals or teams and not shared across the organization.
Code quality metrics quantify how maintainable, reliable, and efficient a codebase is. Essential for engineering management.
Codebase search tools help developers and product teams find specific code, patterns, and features across repositories.
Code dependencies are the relationships between different parts of your codebase that determine what breaks when something changes.
Code coverage measures the percentage of code that's tested by automated tests. 80%+ is a common target.
Code complexity measures how difficult code is to understand, test, and maintain. Higher complexity = higher risk.
Codebase intelligence uses AI to make codebases understandable to everyone on the product team, not just developers.
Codebase documentation explains how code works, why it was built that way, and how to navigate it.
Competitive gap analysis identifies differences between your product and competitors to find strategic opportunities.
Code intelligence uses AI to understand, analyze, and surface insights from codebases for technical and non-technical users.
Code health measures the quality, maintainability, and sustainability of a codebase. Learn the key metrics.
A collection of proven approaches to making software estimates more accurate, from evidence-based methods to reference class forecasting.
Effort estimation predicts the time and resources needed to complete a software project. Mean overrun: 30%.
Measuring technical debt requires combining static analysis, developer surveys, and business impact data.
Machine learning for product managers is the set of ML concepts PMs need to understand to build and manage AI products.
Story points estimate the relative effort of work items. Controversy: many teams find them useless.
Software project estimation predicts the time, cost, and resources needed. Mean cost overrun: 1.8x.
Scope creep is the uncontrolled expansion of project scope without corresponding increases in time, budget, or resources.
Sprint estimation is the process of predicting how much work a team can complete in a sprint cycle.
Technical debt assessment is a structured review of a codebase to identify, quantify, and prioritize accumulated debt.
Technical product documentation describes how a product works, how it's built, and how to use it — for both users and developers.
Technical debt reporting communicates the state and impact of tech debt to technical and non-technical stakeholders.
Technical debt prioritization is the process of deciding which debt items to fix first based on impact and effort.
Technical debt tracking is the systematic monitoring of accumulated debt in a codebase using metrics and dashboards.
Technical debt is the implied cost of future rework caused by choosing quick solutions over better approaches.
Tribal knowledge is undocumented institutional know-how that exists only in people's heads. Learn why it's dangerous.