Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048278207> ?p ?o ?g. }
Showing items 1 to 42 of
42
with 100 items per page.
- W3048278207 endingPage "64" @default.
- W3048278207 startingPage "49" @default.
- W3048278207 abstract "This chapter discusses the empirical approach for software measurements using machine learning (ML) techniques. It demonstrates the usage of ML techniques for both software quality and quantity measurements. With a basic introduction to the current trends of the field and moving through problem definition, the author reaches the experimental set-up and then draws inferences from the experiments. The chapter aims to provide the reader practical and applicable knowledge of ML and deep learning for empirical software measurements. Several software engineering tasks like various development and maintenance tasks that come under software engineering can be formulated as learning problems and can be handled as application of learning algorithms. The desired output is over a range of numbers, hence, the problem can be formulated as a regression-based learning problem. In empirical software measurement, effort estimation problem is formulated as a regression-based supervised learning problem." @default.
- W3048278207 created "2020-08-13" @default.
- W3048278207 creator A5047658011 @default.
- W3048278207 creator A5072215040 @default.
- W3048278207 date "2020-09-27" @default.
- W3048278207 modified "2023-09-23" @default.
- W3048278207 title "Empirical Software Measurements with Machine Learning" @default.
- W3048278207 cites W2007338412 @default.
- W3048278207 doi "https://doi.org/10.1201/9781003079996-4" @default.
- W3048278207 hasPublicationYear "2020" @default.
- W3048278207 type Work @default.
- W3048278207 sameAs 3048278207 @default.
- W3048278207 citedByCount "1" @default.
- W3048278207 countsByYear W30482782072023 @default.
- W3048278207 crossrefType "book-chapter" @default.
- W3048278207 hasAuthorship W3048278207A5047658011 @default.
- W3048278207 hasAuthorship W3048278207A5072215040 @default.
- W3048278207 hasConcept C119857082 @default.
- W3048278207 hasConcept C154945302 @default.
- W3048278207 hasConcept C41008148 @default.
- W3048278207 hasConceptScore W3048278207C119857082 @default.
- W3048278207 hasConceptScore W3048278207C154945302 @default.
- W3048278207 hasConceptScore W3048278207C41008148 @default.
- W3048278207 hasLocation W30482782071 @default.
- W3048278207 hasOpenAccess W3048278207 @default.
- W3048278207 hasPrimaryLocation W30482782071 @default.
- W3048278207 hasRelatedWork W2961085424 @default.
- W3048278207 hasRelatedWork W3046775127 @default.
- W3048278207 hasRelatedWork W3107474891 @default.
- W3048278207 hasRelatedWork W3170094116 @default.
- W3048278207 hasRelatedWork W4205958290 @default.
- W3048278207 hasRelatedWork W4285260836 @default.
- W3048278207 hasRelatedWork W4286629047 @default.
- W3048278207 hasRelatedWork W4306321456 @default.
- W3048278207 hasRelatedWork W4306674287 @default.
- W3048278207 hasRelatedWork W4224009465 @default.
- W3048278207 isParatext "false" @default.
- W3048278207 isRetracted "false" @default.
- W3048278207 magId "3048278207" @default.
- W3048278207 workType "book-chapter" @default.