Feature extraction and performance measure of requirement engineering (RE) document using text classification technique

Saikia, L. P.; Singh, Shilpi

doi:10.1109/rait.2018.8389074

Cited by 3 publications

(1 citation statement)

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“…Acta Scientiarum. Technology, v. 44, e58658, 2022 Saikia and Singh (2018) analyzed the requirements of the RE documents to classify them as ambiguous or unambiguous. This approach involves data preprocessing to remove noisy data, POS tagging, feature extraction using the BOW technique by considering adverbs, adjectives, modals and determinants.…”

Section: Related Workmentioning

confidence: 99%

Extracting feature requests from online reviews of travel industry

Kumari

Memon

2022

Acta Sci Technol

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Before product development, Requirement Engineering (RE) is the fundamental need to know customer preferences for any product. Traditionally, RE is carried out in several ways, particularly by conducting interviews, questionnaires, surveys etc. but these methods provide limited amount of data. As user’s preferences vary from country to country for any type of application, it is very hectic and time consuming to collect user requirements from different countries manually. As the internet is widely used now a days, a large number of customer’s reviews are available online that can be used to obtain the requirements for any product without manual work. Online customer reviews can be divided into three types: user experience, bugs and feature requests. Among these 3 categories, feature requests can be very useful for stakeholders (analysts/ requirements engineers) to acquire the requirements of each application. So, the approach is proposed for feature requests extraction from mobile application reviews of travel industry. In this paper, 4 categories of mobile apps of travel industry belonging to 5 countries have been extracted from Google Play Store and Apple Store. For each category, data from 5 different mobile applications have been considered. Since, the review of users from different countries is in their respective language, those reviews are translated into a standard language i.e. English, and then feature requests from these reviews have been extracted. After that, features are retrieved from user reviews and topic modeling is performed on extracted features since one or more features can be modelled under one topic. To know the opinions of users for any feature request, sentiment analysis is also performed on feature request sentences. These feature requests are also classified as Functional and Non-functional Requirements since it is very useful for application developers to improve or maintain the product to better facilitate the application users

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Section: Related Workmentioning

confidence: 99%