Enhancing Software Defect Prediction Using Principle Component Analysis and Self-Organizing Map

Novi Trisman Hadi, Siti Rochimah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Application of SDLC in software is needed to produce a quality software. One of the way to improve software quality is determined by checking and testing, to find defects in the software that can degrade software quality. Software defect datasets generally have problems with class imbalance and unrelated features that may cause performance degradation in learning algorithm. This research proposes the selection of Principle Component Analysis (PCA) feature to solve the problem of unrelated features, while to overcome the problem of class imbalance using Self Organizing Maps model (SOM) by compare some data classifications algorithm to find the optimum result. The experimental results in this study shows that Random Forest get the highest average of accuracy 96.87%, average of precision 96.88%, and average of recall 96.88%. For JMl dataset got the highest average of 96.06% accuracy, average of 96.44% Precision, and average of 96.06% Recall.

Original languageEnglish
Title of host publication2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages320-325
Number of pages6
ISBN (Electronic)9781538652510
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018 - Batu, East Java, Indonesia
Duration: 9 Oct 201811 Oct 2018

Publication series

Name2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018

Conference

Conference2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2018
Country/TerritoryIndonesia
CityBatu, East Java
Period9/10/1811/10/18

Keywords

  • class imbalance
  • pea
  • software defect
  • som

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