ROM: A Requirement Opinions Mining Method Preliminary Try Based on Software Review Data

Requirement opinion mining aims to mine user opinions that can be used to help the mining of software requirements from various data sources. However, in the development of social network systems, software application platforms or stores and other data sources, the massive, noisy, non-standard data, makes the mining of effective requirement opinions more difficult. Therefore, there is less work in software requirements mining based on the data of software review in development social media or application market. This paper attempts to provide some knowledge support for requirement user story establishing in RE based on the opinion mining and clustering of massively software review data. First of all, this paper combines the requirements of the requirements engineering field to define the requirement opinions, functional requirement opinions and non-functional requirements opinions. Secondly, using the deep learning model to classify the functional requirement reviews and non-functional requirements reviews included in the reviews; Based on the differences between functional data and non-functional data, this paper defines three categories in the description of software functional data, and chooses to use sequence labeling methods to identify functional requirements. Then use the K-means clustering method based on word vector to cluster the review data, and combine TF-IDF and syntactic analysis to extract the aspect and aspect requirements or specific requirements of the requirement opinion respectively, so as to realize the requirement opinion mining of software review data. Finally, this article will give a case study based on the user review data of the mobile phone application service platform 360 mobile assistants.