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All Journal JOIV : International Journal on Informatics Visualization
Goh Eg Su
Universiti Teknologi Malaysia, Johor, Malaysia
Computer Science & IT

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Software Defect Prediction Framework Using Hybrid Software Metric Amirul Zaim; Johanna Ahmad; Noor Hidayah Zakaria; Goh Eg Su; Hidra Amnur
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1258

Abstract

Software fault prediction is widely used in the software development industry. Moreover, software development has accelerated significantly during this epidemic. However, the main problem is that most fault prediction models disregard object-oriented metrics, and even academician researcher concentrate on predicting software problems early in the development process. This research highlights a procedure that includes an object-oriented metric to predict the software fault at the class level and feature selection techniques to assess the effectiveness of the machine learning algorithm to predict the software fault. This research aims to assess the effectiveness of software fault prediction using feature selection techniques. In the present work, software metric has been used in defect prediction. Feature selection techniques were included for selecting the best feature from the dataset. The results show that process metric had slightly better accuracy than the code metric.
Co-Authors Amirul Zaim Hidra Amnur Johanna Ahmad Noor Hidayah Zakaria