A Model for Detecting and Resolving Conflicts in Features Extracted from App User Reviews

Abstract

Many app developers use user feedback to improve their app’s quality. User feedback is a type of a review that originates from different user types and covers various parts of the app. This thesis employs in-depth review analysis to guide app developers in the requirement elicitation process to identify areas of an app where an upgrade is essential. However, reviews from various users might conflict based on their perspectives and feature interest in the app. This thesis addresses the problem associated with detecting and resolving conflicting user reviews by formulating a robust taxonomy of conflict types, causes, and effects. This is because establishing conflicts in user reviews has its own set of semantic and lexical concerns, such as identifying the conceptual overlap that exists between user reviews and decoding the semantic inference of a review, i.e., the "why, how, and what" of an app feature. To find the solution to the previously described conflict, 64, 964 app reviews were tested and evaluated from three different mobile app sources. As a result a semantic rule-based tool was developed that integrates knowledge representation and linguistic rules to detect conflicts in reviews gathered from app users. The result emphasised the holistic awareness of domain knowledge for effective conflict identification and resolution in app reviews. In addition, the findings suggested a methodical approach for applying conflict analysis to strengthen software design requirements. As a conclusion, considering the comprehensive and interpretable disposition of the conflict identification rules, the thesis presents a solution to resolve the conflicts identified in app reviews.