@inproceedings{ff39ab58167941fca00d729d578abe4e,
title = "Validation of Inspection Reviews over Variable Features Set Threshold",
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.",
keywords = "class imbalance, faults, feature sets, inspection reviews, machine learning, part of speech, sampling",
author = "Maninder Singh and Walia, {Gursimran S.} and Anurag Goswami",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Machine Learning and Data Science, MLDS 2017 ; Conference date: 14-12-2017 Through 15-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/MLDS.2017.16",
language = "English (US)",
series = "Proceedings - 2017 International Conference on Machine Learning and Data Science, MLDS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "128--135",
booktitle = "Proceedings - 2017 International Conference on Machine Learning and Data Science, MLDS 2017",
}