A reference from Weave

Engineering & AI glossary

Understand the metrics, models, and methods behind modern engineering. Clear definitions, practical examples, and a closer look at what the numbers actually mean.

All terms

2,008 terms

  • Branch coverage

    Branch coverage is a software testing or test-design practice used to gather evidence about a defined risk, behavior, boundary, or operating condition. It makes the question under test explicit, identifies the inputs and observations that matter, and gives a team a repeatable basis for deciding whether the result is acceptable.

    Code quality and technical debt

  • Branch lifetime

    Branch lifetime is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Branch protection

    Branch protection rules define merge conditions for important branches, such as required reviews, status checks, or restrictions on direct pushes.

    Code review

  • Breaking change

    Breaking change is a software maintenance concern describing a condition that can make future changes, verification, operation, or ownership harder. Its practical importance depends on supported behavior, rate of change, and the consequences of delay.

    Code quality and technical debt

  • Breaking change rate

    The proportion of public interface changes that require consumers to modify or redeploy.

    Code quality and technical debt

  • Budget policy

    Budget policy is a model-routing or gateway concept used to manage policy enforcement for AI requests. It describes a distinct decision, control, interface, or observation point between an application and one or more model providers.

    Model routing and gateways

  • Build artifact

    Build artifact is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Build debt

    Build debt is a software maintenance concern describing a condition that can make future changes, verification, operation, or ownership harder. Its practical importance depends on supported behavior, rate of change, and the consequences of delay.

    Code quality and technical debt

  • Build failure rate

    Build failure rate is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Build feedback time

    Build feedback time is the elapsed time between a software change entering a build or validation workflow and the point when useful build or test feedback is available to the team. It includes execution and queueing time when the boundary includes both.

    Developer productivity

  • Build provenance

    Build provenance is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Build queue time

    Build queue time is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Build reproducibility

    Build reproducibility is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Burn-rate alert

    Burn-rate alert is detection of how quickly a service consumes error budget relative to its SLO window.

    Reliability and observability

  • Bus factor

    The minimum number of people whose loss would put a project or critical area at serious operational risk.

    Code quality and technical debt

  • Cache eviction

    Cache eviction is the serving concept concerned with cache eviction during AI inference.

    Inference performance

  • Cache hit rate

    Cache hit rate is the serving concept concerned with cache hit rate during AI inference.

    Inference performance

  • Cache invalidation

    Cache invalidation is the serving concept concerned with cache invalidation during AI inference.

    Inference performance

  • Cache key

    Cache key is the serving concept concerned with cache key during AI inference.

    Inference performance

  • Cache ttl

    Cache ttl is the serving concept concerned with cache ttl during AI inference.

    Inference performance

  • Cache warming

    Cache warming is the serving concept concerned with cache warming during AI inference.

    Inference performance

  • Cached token ratio

    Cached token ratio is the share of input tokens served from a reusable context cache. It gives teams a way to name, measure, or reason about an economic property of an AI workload without treating raw usage as proof of value.

    Token costs and AI ROI

  • Calibration error

    Calibration error is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.

    Evaluations and benchmarks

  • Calibration set

    Calibration set is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.

    Evaluations and benchmarks

  • Canary deployment

    Canary deployment is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps

  • Canary rollout

    Canary rollout is a release engineering and DevOps concept for controlling how software changes are prepared, introduced, or understood.

    DORA and DevOps

  • Canary routing

    Canary routing is a model-routing or gateway concept used to manage traffic selection for AI requests. It describes a distinct decision, control, interface, or observation point between an application and one or more model providers.

    Model routing and gateways

  • Capability routing

    Capability routing is a model-routing or gateway concept used to manage operational visibility for AI requests. It describes a distinct decision, control, interface, or observation point between an application and one or more model providers.

    Model routing and gateways

  • Capacity allocation

    Capacity allocation is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.

    Flow and capacity planning

  • Capacity buffer

    A capacity buffer is intentionally uncommitted capacity held available for variation or work that cannot be forecast precisely. It is a planning policy, not evidence that people should remain idle.

    Flow and capacity planning

  • Capacity constraint

    Capacity constraint is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.

    Flow and capacity planning

  • Capacity forecast

    Capacity forecast is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.

    Flow and capacity planning

  • Capacity headroom

    Capacity headroom is a reliability concept used to describe a specific condition, control, or decision in the operation of software services.

    Reliability and observability

  • Capacity planning

    Capacity planning is the practice of estimating the work a delivery system can complete and comparing it with expected demand, constraints, and service commitments. It supports tradeoffs rather than promising exact output.

    Flow and capacity planning

  • Capacity reserve

    Capacity reserve is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.

    Flow and capacity planning

  • Capacity scenario

    Capacity scenario is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.

    Flow and capacity planning

  • Capacity utilization

    Capacity utilization is the ratio of capacity used for a defined class of work to the capacity available for that class during the same interval. It is meaningful only when both numerator and denominator are defined consistently.

    Flow and capacity planning

  • Capacity variance

    Capacity variance is a concept used in software delivery planning to describe a condition, relationship, estimate, or decision about engineering work.

    Flow and capacity planning

  • Capacity-constrained flow

    Capacity-constrained flow occurs when a stage, skill, environment, or policy has less effective capacity than the demand arriving at it. The result is usually a growing queue, longer waiting, or reduced throughput.

    Flow and capacity planning

  • Cardinality management

    Cardinality management is control of distinct attribute combinations so telemetry remains queryable and affordable.

    Reliability and observability

  • Catastrophic forgetting

    Catastrophic forgetting is a language-model concept about serving behavior and operational tradeoffs. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Causal diagram

    Causal diagram is a statistical or measurement concept used to describe, compare, or interpret engineering data. Its meaning depends on the unit of analysis, data-generating process, and question being asked.

    Measurement and experimentation

  • Causal language modeling

    Causal language modeling is a language-model concept about evaluation design and failure analysis. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Causal mask

    Causal mask is a language-model concept about evaluation design and failure analysis. It names a mechanism, representation, training practice, or operational behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Cause-effect graphing

    Cause-effect graphing is a software testing or test-design practice used to gather evidence about a defined risk, behavior, boundary, or operating condition. It makes the question under test explicit, identifies the inputs and observations that matter, and gives a team a repeatable basis for deciding whether the result is acceptable.

    Code quality and technical debt

  • Central limit theorem

    Central limit theorem is a statistical or measurement concept used to describe, compare, or interpret engineering data. Its meaning depends on the unit of analysis, data-generating process, and question being asked.

    Measurement and experimentation

  • Chain-of-thought prompting

    Chain-of-thought prompting is a language-model concept about instruction design and control. It names a mechanism, representation, prompting pattern, decoding control, or context behavior that can change how an AI system processes input and produces output.

    LLM fundamentals

  • Challenge set

    Challenge set is a defined lens for examining AI system behavior with a stated task, evidence, and interpretation rule.

    Evaluations and benchmarks

  • Change adoption

    Change adoption is a way to organize, support, or evaluate software work so that teams can make useful progress with less avoidable friction. It is most valuable when connected to a concrete outcome and the local conditions of the team using it.

    Developer productivity

  • Change approval

    Change approval is a software delivery concept used to describe a specific event, interval, control, or operating condition in the path from source change to production behavior. A useful definition names the boundary, unit, and decision the measure supports.

    DORA and DevOps