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

1 Scopus citations

Abstract

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
Title of host publicationiSOFT - Proceedings of the 11th Innovations in Software Engineering Conference, ISEC 2018
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450363983
DOIs
StatePublished - 9 Feb 2018
Event11th Innovations in Software Engineering Conference, ISEC 2017 - Hyderabad, India
Duration: 9 Feb 201811 Feb 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference11th Innovations in Software Engineering Conference, ISEC 2017
CountryIndia
CityHyderabad
Period9/02/1811/02/18

Keywords

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

Fingerprint Dive into the research topics of 'Validating requirements reviews by introducing fault-type level granularity: A machine learning approach'. Together they form a unique fingerprint.

  • Cite this

    Singh, M., Anu, V., Walia, G. S., & Goswami, A. (2018). Validating requirements reviews by introducing fault-type level granularity: A machine learning approach. In iSOFT - Proceedings of the 11th Innovations in Software Engineering Conference, ISEC 2018 [a10] (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3172871.3172880