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In software engineering and development, a software metric is a standard of measure of a degree to which a software system or process possesses some property. [1] [2] Even if a metric is not a measurement (metrics are functions, while measurements are the numbers obtained by the application of metrics), often the two terms are used as synonyms.
Software measurement is a quantified attribute (see also: measurement) of a characteristic of a software product or the software process. It is a discipline within software engineering. The process of software measurement is defined and governed by ISO Standard ISO 15939 (software measurement process).
An intuitive metric: line of code serves as an intuitive metric for measuring the size of software because it can be seen, and the effect of it can be visualized. Function points are said to be more of an objective metric which cannot be imagined as being a physical entity, it exists only in the logical space. This way, LOC comes in handy to ...
In software engineering, code coverage, also called test coverage, is a percentage measure of the degree to which the source code of a program is executed when a particular test suite is run. A program with high code coverage has more of its source code executed during testing, which suggests it has a lower chance of containing undetected ...
Metrics (networking), the properties of a communication path used by a router to make routing decisions; Software metric, a measure of some property of a piece of software or its specifications; Reuse metrics, a quantitative indicator of an attribute for software reuse and reusability
In software architecture, these attributed are known as "architectural characteristic" or non-functional requirements. Note that it's software architects' responsibility to match these attributes with business requirements and user requirements. Note that synchronous communication between software architectural components, entangles them and ...
Software analytics is the analytics specific to the domain of software systems taking into account source code, static and dynamic characteristics (e.g., software metrics) as well as related processes of their development and evolution.
Metrics, logs and traces are most commonly listed as the pillars of observability. [13] Majors et al. suggest that the pillars of observability are high cardinality, high-dimensionality, and explorability, arguing that runbooks and dashboards have little value because "modern systems rarely fail in precisely the same way twice."