@inproceedings{01fe6cd3e1a648e186d7b4ef57318583,
title = "Validating requirements reviews by introducing fault-type level granularity: A machine learning approach",
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.",
keywords = "Ensemble, Fault types, Inspection reviews, Machine learning, Supervised learning",
author = "Maninder Singh and Vaibhav Anu and Walia, {Gursimran S.} and Anurag Goswami",
year = "2018",
month = feb,
day = "9",
doi = "10.1145/3172871.3172880",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "iSOFT - Proceedings of the 11th Innovations in Software Engineering Conference, ISEC 2018",
note = "null ; Conference date: 09-02-2018 Through 11-02-2018",
}