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Exploring Machine Learning Techniques and Metric Feature Pairs for Software Defects Prediction
Rafique, Muhammad Ahmad Imran
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Kola, Lokesh
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2023 (English)
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Place, publisher, year, edition, pages
2023. , p. 70
National Category
Computer Sciences
Identifiers
URN:
urn:nbn:se:bth-25570
OAI: oai:DiVA.org:bth-25570
DiVA, id:
diva2:1810395
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVACC Master’s Programme in Computer Science, 120 hp
Presentation
2023-05-25, Karlskrona, 09:00 (English)
Supervisors
Abghari, Shahrooz, Dr
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Examiners
Mendes, Emilia, Prof.
Available from:
2023-11-08
Created:
2023-11-07
Last updated:
2023-11-08
Bibliographically approved
Open Access in DiVA
Exploring Machine Learning Techniques and Metric Feature Pairs for Software Defects Prediction
(4618 kB)
19 downloads
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FULLTEXT02.pdf
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4618 kB
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8285702a5f84b180b5db9722db57453386227f1d2d1c9522d743d1dfad3727841336e71f66b16a8994327fcb570d99dbdf8dad1a9159a3fd7545ecb141e2044a
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Department of Computer Science
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Computer Sciences
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Total: 19 downloads
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https://urn.kb.se/resolve?urn=urn:nbn:se:bth-25570
Direct link
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Cite
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ieee
modern-language-association-8th-edition
vancouver
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apa
ieee
modern-language-association-8th-edition
vancouver
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de-DE
en-GB
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nn-NB
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