Building a Pilot Software Quality-in-Use Benchmark Dataset
Prepared domain specific datasets plays an important role to supervised learning approaches. In this article a new sentence dataset for software quality-in-use is proposed. Three experts were chosen to annotate the data using a proposed annotation scheme. Then the data were reconciled in a (no match...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/16384/ http://ir.unimas.my/id/eprint/16384/1/Building.pdf |
| _version_ | 1848838053435539456 |
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| author | Issa, Atoum Bong, Chih How Narayanan, Kulathuramaiyer |
| author_facet | Issa, Atoum Bong, Chih How Narayanan, Kulathuramaiyer |
| author_sort | Issa, Atoum |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Prepared domain specific datasets plays an important role to supervised learning approaches. In this article a new sentence dataset for software quality-in-use is proposed. Three experts were chosen to annotate the data using a proposed annotation scheme. Then the data were reconciled in a (no match eliminate) process to reduce bias. The Kappa, k statistics revealed an acceptable level of agreement; moderate to substantial agreement between the experts. The built data can be used to evaluate software quality-in-use models in sentiment analysis models. Moreover, the annotation scheme can be used to extend the current dataset. |
| first_indexed | 2025-11-15T06:49:26Z |
| format | Article |
| id | unimas-16384 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:49:26Z |
| publishDate | 2015 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-163842022-01-26T08:20:41Z http://ir.unimas.my/id/eprint/16384/ Building a Pilot Software Quality-in-Use Benchmark Dataset Issa, Atoum Bong, Chih How Narayanan, Kulathuramaiyer T Technology (General) Prepared domain specific datasets plays an important role to supervised learning approaches. In this article a new sentence dataset for software quality-in-use is proposed. Three experts were chosen to annotate the data using a proposed annotation scheme. Then the data were reconciled in a (no match eliminate) process to reduce bias. The Kappa, k statistics revealed an acceptable level of agreement; moderate to substantial agreement between the experts. The built data can be used to evaluate software quality-in-use models in sentiment analysis models. Moreover, the annotation scheme can be used to extend the current dataset. IEEE 2015 Article PeerReviewed text en http://ir.unimas.my/id/eprint/16384/1/Building.pdf Issa, Atoum and Bong, Chih How and Narayanan, Kulathuramaiyer (2015) Building a Pilot Software Quality-in-Use Benchmark Dataset. 9th International Conference on IT in Asia (CITA), 2015. ISSN ISBN: 978-1-4799-9939-2 https://www.researchgate.net/publication/281715795 |
| spellingShingle | T Technology (General) Issa, Atoum Bong, Chih How Narayanan, Kulathuramaiyer Building a Pilot Software Quality-in-Use Benchmark Dataset |
| title | Building a Pilot Software Quality-in-Use Benchmark Dataset |
| title_full | Building a Pilot Software Quality-in-Use Benchmark Dataset |
| title_fullStr | Building a Pilot Software Quality-in-Use Benchmark Dataset |
| title_full_unstemmed | Building a Pilot Software Quality-in-Use Benchmark Dataset |
| title_short | Building a Pilot Software Quality-in-Use Benchmark Dataset |
| title_sort | building a pilot software quality-in-use benchmark dataset |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/16384/ http://ir.unimas.my/id/eprint/16384/ http://ir.unimas.my/id/eprint/16384/1/Building.pdf |