Feature Selection in Cross-Project Software Defect Prediction

Autor:	Yaya Heryadi, Chul-Ho Kang, Wayan Suparta, A. Saifudin, Agung Trisetyarso, Bachtiar Saleh Abbas
Rok vydání:	2020
Předmět:	History Software bug Computer science Feature selection Data mining Cross project computer.software_genre computer Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1569:022001
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1569/2/022001
Popis:	Advances in technology have increased the use and complexity of software. The complexity of the software can increase the possibility of defects. Defective software can cause high losses. Fixing defective software requires a high cost because it can spend up 50% of the project schedule. Most software developers don’t document their work properly so that making it difficult to analyse software development history data. Software metrics which use in cross-project software defects prediction have many features. Software metrics usually consist of various measurement techniques, so there are possibilities for their features to be similar. It is possible that these features are similar or irrelevant so that they can cause a decrease in the performance of classifiers. In this study, several feature selection techniques were proposed to select the relevant features. The classification algorithm used is Naive Bayes. Based on the analysis using ANOVA, the SBS and SBFS models can significantly improve the performance of the Naïve Bayes model.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6059f89fb0b9fa40d2a7d377969527ec https://doi.org/10.1088/1742-6596/1569/2/022001 Zobrazit plný text záznamu