Enhancing Software Feature Extraction Results Using Sentiment Analysis to Aid Requirements Reuse

Abstract

Recently, feature extraction from user reviews has been used for requirements reuse to improve the software development process. However, research has yet to use sentiment analysis in the extraction for it to be well understood. The aim of this study is to improve software feature extraction results by using sentiment analysis. Our study’s novelty focuses on the correlation between feature extraction from user reviews and results of sentiment analysis for requirement reuse. This study can inform system analysis in the requirements elicitation process. Our proposal uses user reviews for the software feature extraction and incorporates sentiment analysis and similarity measures in the process. Experimental results show that the extracted features used to expand existing requirements may come from positive and negative sentiments. However, extracted features with positive sentiment overall have better values than negative sentiments, namely 90% compared to 63% for the relevance value, 74–47% for prompting new features, and 55–26% for verbatim reuse as new requirements.