• DOI:10.1145/3172871.3172880
• Corpus ID: 3438662

Validating Requirements Reviews by Introducing Fault-Type Level Granularity: A Machine Learning Approach

@article{Singh2018ValidatingRR,
  title={Validating Requirements Reviews by Introducing Fault-Type Level Granularity: A Machine Learning Approach},
  author={Maninder Singh and Vaibhav Anu and Gursimran Singh Walia and Anurag Goswami},
  journal={Proceedings of the 11th Innovations in Software Engineering Conference},
  year={2018}
}

• Maninder Singh, Vaibhav Anu, +1 author Anurag Goswami
• Published 9 February 2018
• Computer Science
• Proceedings of the 11th Innovations in Software Engineering Conference

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.