Validation of Inspection Reviews over Variable Features Set Threshold

Maninder Singh, Gursimran S. Walia, Anurag Goswami

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

6 Scopus citations

Abstract

Background: Mining software requirement reviews involve natural language processing (NLP) to efficiently validate a true-fault as useful and false-positive as non-useful. Aim: The aim of this paper is to evaluate our proposed mining approach to automate the validation of requirement reviews generated during an inspection of NL requirements document. Method: Our approach utilized two training models; one from requirement reviews and other from online movies. We conducted an empirical study to test our approach using part of speech (POS) against these two trained models and observed trends w.r.t. F-measure and G-mean along with percentage of features used to train two models. Results: The results showed that using training reviews from two different domains report similar trend across evaluation metrics. Our results show that the most stable and promising validation results for F-measure and G-mean are obtained when a model over inspection and movies reviews are trained using feature set threshold value 65% and 45% respectively.

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.
Pages128-135
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
  • faults
  • feature sets
  • inspection reviews
  • machine learning
  • part of speech
  • sampling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing

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