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

  • Safe deletion

    Safe deletion 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

  • Safety evaluation

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

    Evaluations and benchmarks

  • Sample size

    Sample size 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

  • Sampling

    Sampling selects a subset of observations for storage or analysis.

    Engineering analytics

  • Sandboxed Code Execution

    Sandboxed Code Execution is a software-engineering concept describing how an AI coding system, its tools, or human collaborators handle a defined task.

    AI coding and agents

  • Scalability debt

    Scalability 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

  • Scalability testing

    Scalability 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

  • Scale to zero

    Scale to zero is the serving concept concerned with scale to zero during AI inference.

    Inference performance

  • Scenario planning

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

    Flow and capacity planning

  • Scenario testing

    Scenario 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

  • Schedule risk

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

    Flow and capacity planning

  • Scheduled release

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

    DORA and DevOps

  • Schema compatibility

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

    DORA and DevOps

  • Schema debt

    Schema 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

  • Schema migration

    Schema migration 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

  • Schema release change

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

    DORA and DevOps

  • Schema validation

    Schema validation checks records against structural and semantic expectations.

    Engineering analytics

  • Scope buffer

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

    Flow and capacity planning

  • Scope change

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

    Flow and capacity planning

  • Seasonality

    Seasonality 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

  • Security debt

    Security 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

  • Security review

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

    Code review

  • Security testing

    Security 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

  • Segment

    Segment is an analytical concept for separating engineering observations into populations whose differences may matter to a decision.

    Engineering analytics

  • Segment Size

    Segment Size is an analytical concept for separating engineering observations into populations whose differences may matter to a decision.

    Engineering analytics

  • Segmentation Criteria

    Segmentation Criteria is an analytical concept for separating engineering observations into populations whose differences may matter to a decision.

    Engineering analytics

  • Selection bias

    Selection bias is an analytical risk or quality concern that can make an engineering analysis appear more certain, comparable, or causal than it is.

    Engineering analytics

  • Selective significance

    Selective significance is an analytical risk or quality concern that can make an engineering analysis appear more certain, comparable, or causal than it is.

    Engineering analytics

  • Self-consistency decoding

    Self-consistency decoding is a language-model concept about context selection and limits. 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

  • Self-critique prompting

    Self-critique 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

  • Self-review

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

    Code review

  • Self-service

    Self-service 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

  • Semantic caching

    Semantic caching stores a response and retrieves it for a later request judged similar in meaning. It uses a similarity method rather than requiring the later request to match the earlier text exactly.

    Inference performance

  • Semantic conventions

    Semantic conventions standardize names, attributes, units, and meanings.

    Engineering analytics

  • Semantic layer

    Semantic layer models governed concepts above raw data.

    Engineering analytics

  • Semantic routing

    Semantic 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

  • Semantic versioning

    Semantic versioning 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

  • Sensitivity analysis

    Sensitivity analysis 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

  • Sentence representation

    Sentence representation is a language-model concept about context selection and limits. 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

  • Separation of concerns

    The practice of organizing software so distinct responsibilities can change and be reasoned about independently.

    Code quality and technical debt

  • Sequence packing

    Sequence packing is the serving concept concerned with sequence packing during AI inference.

    Inference performance

  • Server sent events

    Server sent events is the serving concept concerned with server sent events during AI inference.

    Inference performance

  • Service catalog

    Service catalog 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

  • Service class

    A service class is a category of work with a shared policy for sequencing, WIP treatment, and service expectation. Examples include standard, expedite, fixed-date, and intangible work.

    Flow and capacity planning

  • Service Comparison

    Service Comparison is an analytical concept for separating engineering observations into populations whose differences may matter to a decision.

    Engineering analytics

  • Service discovery for models

    Service discovery for models 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

  • Service extraction

    Service extraction 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

  • Service level agreement

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

    Reliability and observability

  • Service level credit

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

    Reliability and observability

  • Service level indicator

    A service level indicator, or SLI, is a carefully defined quantitative measure of a service behavior that matters to users. Common examples include availability, request latency, error rate, and throughput.

    Reliability and observability