Validating requirements reviews by introducing fault-type level granularity: A machine learning approach

Maninder Singh, Vaibhav Anu, Gursimran S. Walia, Anurag Goswami

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

10 Scopus citations


Inspections are a proven approach for improving software requirements quality. Owing to the fact that inspectors report both faults and non-faults (i.e., false-positives) in their inspection reports, a major chunk of work falls on the person who is responsible for consolidating the reports received from multiple inspectors. We aim at automation of fault-consolidation step by using supervised machine learning algorithms that can effectively isolate faults from non-faults. Three different inspection studies were conducted in controlled environments to obtain real inspection data from inspectors belonging to both industry and from academic backgrounds. Next, we devised a methodology to separate faults from non-faults by first using ten individual classifiers from five different classification families to categorize different fault-types (e.g., omission, incorrectness, and inconsistencies). Based on the individual performance of classifiers for each fault-type, we created targeted ensembles that are suitable for identification of each fault-type. Our analysis showed that our selected ensemble classifiers were able to separate faults from non-faults with very high accuracy (as high as 85-89% for some fault-types), with a notable result being that in some cases, individual classifiers performed better than ensembles. In general, our approach can significantly reduce effort required to isolate faults from false-positives during the fault consolidation step of requirements inspections. Our approach also discusses the percentage possibility of correctly classifying each fault-type.

Original languageEnglish (US)
Title of host publicationiSOFT - Proceedings of the 11th Innovations in Software Engineering Conference, ISEC 2018
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450363983
StatePublished - Feb 9 2018
Externally publishedYes
Event11th Innovations in Software Engineering Conference, ISEC 2017 - Hyderabad, India
Duration: Feb 9 2018Feb 11 2018

Publication series

NameACM International Conference Proceeding Series


Conference11th Innovations in Software Engineering Conference, ISEC 2017


  • Ensemble
  • Fault types
  • Inspection reviews
  • Machine learning
  • Supervised learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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