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This article discusses a set of tactics useful in software testing.It is intended as a comprehensive list of tactical approaches to software quality assurance (more widely colloquially known as quality assurance (traditionally called by the acronym "QA")) and general application of the test method (usually just called "testing" or sometimes "developer testing").
Exploratory testing is an approach to software testing that is concisely described as simultaneous learning, test design and test execution. Cem Kaner, who coined the term in 1984, [1] defines exploratory testing as "a style of software testing that emphasizes the personal freedom and responsibility of the individual tester to continually optimize the quality of his/her work by treating test ...
Shift-left testing [1] is an approach to software testing and system testing in which testing is performed earlier in the lifecycle (i.e. moved left on the project timeline). It is the first half of the maxim "test early and often". [2] It was coined by Larry Smith in 2001. [3] [4]
Software testing can also be performed by non-dedicated software testers. In the 1980s, the term software tester started to be used to denote a separate profession. Notable software testing roles and titles include: [65] test manager, test lead, test analyst, test designer, tester, automation developer, and test administrator. [66]
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
Model-based testing is an application of model-based design for designing and optionally also executing artifacts to perform software testing or system testing. Models can be used to represent the desired behavior of a system under test (SUT), or to represent testing strategies and a test environment.
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
Deep semantic parsing, also known as compositional semantic parsing, is concerned with producing precise meaning representations of utterances that can contain significant compositionality. [23] Shallow semantic parsers can parse utterances like "show me flights from Boston to Dallas" by classifying the intent as "list flights", and filling ...