Unsupervised software defect prediction using median absolute deviation threshold based spectral classifier on signed Laplacian matrix
Penulis/Author
ARIS MARJUNI (1); Teguh Bharata Adji, S.T., M.T., M.Eng., Ph.D (2); Prof. Dr. Ir. Ridi Ferdiana, S.T., M.T., IPM. (3)
Tanggal/Date
2019
Kata Kunci/Keyword
Abstrak/Abstract
Area of interest
The trend of current software inevitably leads to the big data era. There are much of large software developed from hundreds to thousands of modules. In software development projects, finding the defect proneness manually on each module in large software dataset is probably inefficient in resources. In this task, the use of a software defect prediction model becomes a popular solution with much more cost-effective rather than manual reviews. This study presents a specific machine learning algorithm, which is the spectral classifier, to develop a software defect prediction model using unsupervised learning approach.
Rumpun Ilmu
Teknologi Informasi
Bahasa Asli/Original Language
English
Level
Internasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
03 Similatiry Unsupervised software defect prediction using median absolute deviation threshold.pdf
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2
03 Unsupervised software defect prediction using median absolute deviation threshold based spectral.pdf
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3
Journal of Big Data - Springer.pdf
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4
Bukti Korespondensi Journal of Big Data (Software Defect).pdf
