A reference from Weave

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

Terms beginning with S

160 terms

  • Software value stream

    A software value stream is the sequence of activities and handoffs required to turn a software request into a usable outcome. It includes the work that creates new value and the recovery work required to restore or protect an existing service.

    Engineering analytics

  • Source system

    Source system identifies where measurement records originate.

    Engineering analytics

  • SPACE framework

    The SPACE framework is a multidimensional approach to understanding developer productivity across Satisfaction and well-being, Performance, Activity, Communication and collaboration, and Efficiency and flow.

    Developer productivity

  • Span

    Span is a timed unit of work within a trace with operation, timing, status, attributes, and events.

    Reliability and observability

  • Span attribute

    Span attribute is a key-value property attached to a span to provide searchable operation context.

    Reliability and observability

  • Span event

    Span event is a timestamped annotation attached to a span to record something during its lifetime.

    Reliability and observability

  • Span ID

    Span ID is the identifier for one span within a trace that distinguishes it from its parent and siblings.

    Reliability and observability

  • Sparse autoencoder

    Sparse autoencoder 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

  • Special cause variation

    Special cause variation 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

  • Special token

    Special token is a language-model concept about representation and similarity. 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

  • Specialist queue

    A specialist queue is a set of items that require capability available from only a small number of people or systems. It is a queue caused by constrained expertise rather than by total team capacity alone.

    Flow and capacity planning

  • Specification by example

    Specification by example 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

  • Speculative decoding

    Speculative decoding is the serving concept concerned with speculative decoding during AI inference.

    Inference performance

  • Speculative generality

    A code smell in which abstractions or extension points exist for hypothetical future needs.

    Code quality and technical debt

  • Spend attribution

    Spend attribution is the mapping of AI spend to a product, team, workflow, user, or outcome. 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

  • Spend limit

    Spend limit 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

  • Spike testing

    Spike testing 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

  • Split brain

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

    Reliability and observability

  • Sprint planning

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

    Flow and capacity planning

  • Squash merge

    Squash merge is a practical concept in a pull-request workflow that shapes how people examine, discuss, own, or integrate a proposed change.

    Code review

  • Stacked pull requests

    Stacked pull requests divide dependent work into a sequence of smaller changes that can be reviewed in order.

    Code review

  • Staged rollout

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

    DORA and DevOps

  • Stakeholder alignment

    Stakeholder alignment 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

  • Stale review

    Stale review is a practical concept in a pull-request workflow that shapes how people examine, discuss, own, or integrate a proposed change.

    Code review

  • Standard change

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

    DORA and DevOps

  • Standard error

    Standard error 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

  • Standard item

    A standard item is work that follows the ordinary pull order, WIP policy, and service expectation of a workflow. It is distinguished from urgent, fixed-date, or other special classes of service.

    Flow and capacity planning

  • Startup probe

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

    Reliability and observability

  • State transition testing

    State transition testing 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

  • Static batching

    Static batching is the serving concept concerned with static batching during AI inference.

    Inference performance

  • Static routing

    Static 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

  • Static-analysis false-positive rate

    The share of reported static-analysis findings that reviewers determine do not represent actionable issues.

    Code quality and technical debt

  • Stationarity

    Stationarity 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

  • Statistical power

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

    Evaluations and benchmarks

  • Statistical process control

    Statistical process control 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

  • Step-back prompting

    Step-back prompting is a language-model concept about generation behavior and sampling. 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

  • Sticky routing

    Sticky 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

  • Story points

    Story points 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

  • Strangler fig pattern

    Strangler fig pattern 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

  • Stratified sampling

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

    Evaluations and benchmarks

  • Stream-aligned team

    Stream-aligned team 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

  • Streaming inference

    Streaming inference is the serving concept concerned with streaming inference during AI inference.

    Inference performance

  • Streaming normalization

    Streaming normalization is a model-routing or gateway concept used to manage interface consistency 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

  • Stress testing

    Stress testing 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

  • Structured logging

    Structured logging is writing log records as named fields instead of only free-form text.

    Reliability and observability

  • Structured output

    Structured output is a model response constrained to a defined shape such as JSON with named fields and types. It gives application code a predictable contract while leaving the model responsible for generating the field values.

    LLM fundamentals

  • Structured output normalization

    Structured output normalization is a model-routing or gateway concept used to manage interface consistency 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

  • Substantive review comment

    A substantive review comment identifies a behavior, risk, question, or improvement that could change the author's decision or understanding.

    Code review

  • Subsystem testing

    Subsystem testing 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

  • Summary metric

    Summary metric is a client-side statistical summary of observations, commonly count, sum, and selected quantiles.

    Reliability and observability