1 publication

• Published Subscription 10 Feb 2021

  A Review of Code Defect Likelihood Using ML Methods

  By UKPE KUFRE, AMANNAH *, Constance Izuchukwu

  Abstract: AbstractSoftware begets defects and defects are inevitable in software design and coding. This paper focused on five machine learning analysis models (MLAM); Support Vector Machine, Random Forest, K-Nearest Neighbor, Classification and Regression Tree, and Linear Discriminant Analysis. These models are trained to detect software defects using the software defect dataset from Promise data repository. The collected dataset is preprocessed to reduce the amount of redundant features using a dimension reduction …

  Published in Journal of Open Source Developments · Vol. 7, Issue 3, 2020 · pp. 1–6 Read article →