Terms extractions: an approach for requirements reuse

This paper presents a solution to a requirements reuse problem that utilises natural language processing and information retrieval technique. We proposed a semi-automated approach to extract the software features from online software review to assist the process to reuse natural language requirements. We have conducted an experiment to compare the manual feature extraction versus the semi-automated feature extraction. We used compilations of software review from the Internet as a source of this extraction process. The extracted software features are compared against the features obtained manually by human and the evaluation results obtained in terms of time, precision, recall, and F-Measure indicate a promising result

Customer Rating Reactions Can Be Predicted Purely Using App Features

In this paper we provide empirical evidence that the rating that an app attracts can be accurately predicted from the features it offers. Our results, based on an analysis of 11,537 apps from the Samsung Android and BlackBerry World app stores, indicate that the rating of 89% of these apps can be predicted with 100% accuracy. Our prediction model is built by using feature and rating information from the existing apps offered in the App Store and it yields highly accurate rating predictions, using only a few (11-12) existing apps for case-based prediction. These findings may have important implications for require- ments engineering in app stores: They indicate that app devel- opers may be able to obtain (very accurate) assessments of the customer reaction to their proposed feature sets (requirements), thereby providing new opportunities to support the requirements elicitation process for app developers