An Empirical Investigation to Overcome Class-Imbalance in Inspection Reviews

Maninder Singh, Gursimran S. Walia, Anurag Goswami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Background: software inspection results in reviews that report the presence of faults. Requirements author must manually read through the reviews and differentiate between true-faults and false-positives. Problem: post-inspection decisions (fault or nonfault) are difficult and time consuming. It is difficult to employ machine learning (ML) techniques directly to raw (unstructured) data because of class imbalance problem and possible fault-slippage through misclassification of fault. Aim: The aim of this research is to solve this problem with the help of ensemble approach and priority analysis to achieve significant accuracy in determining true-fault and false-positive reviews without losing any listed fault. Method: We conducted empirical experiment using two trained models (with reviews from inspection domain vs. movies domain) to address class imbalance problem. Our approach uses ensemble methods to develop classification confidence of inspection reviews and assigns them to appropriate priority class. Results: The results showed that movies trained model performed better than inspection trained and restricted any possible fault-slippage.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 International Conference on Machine Learning and Data Science, MLDS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages15-22
Number of pages8
ISBN (Electronic)9781538634462
DOIs
StatePublished - Mar 19 2018
Externally publishedYes
Event2017 International Conference on Machine Learning and Data Science, MLDS 2017 - Noida, India
Duration: Dec 14 2017Dec 15 2017

Publication series

NameProceedings - 2017 International Conference on Machine Learning and Data Science, MLDS 2017
Volume2018-January

Conference

Conference2017 International Conference on Machine Learning and Data Science, MLDS 2017
Country/TerritoryIndia
CityNoida
Period12/14/1712/15/17

Keywords

  • class imbalance
  • ensemble
  • Fault priority
  • fault slippage
  • inspections reviews
  • machine learning
  • part of speech
  • sampling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing

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