Software Fault Prediction Using Classification Algorithm

EasyChair Preprint 6246

Abstract

Software fault prediction is a valuable exercise in software quality assurance to best allocate the limited testing resources. Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. Previous research on software metrics has shown strong relationships between software metrics and faults in object-oriented systems using a binary variable. Practically, it would be helpful if developers could identify the most error-prone modules early so that they can optimize testing-resource allocation and increase fault detection effectiveness accordingly. The findings can provide an effective foundation for managing the necessary activities of software development and testing.

Keyphrases: Classification, Open Source Software, Pareto principle, fault distribution, software quality

@booklet{EasyChair:6246,
  author    = {Pranita Ingale and Nilesh Alone},
  title     = {Software Fault Prediction Using Classification Algorithm},
  howpublished = {EasyChair Preprint 6246},
  year      = {EasyChair, 2021}}