# User Review-Based Change File Localization for Mobile Applications

## TL;DR

This paper introduces RISING, an automated method that leverages user reviews, classification, clustering, and linking to improve change file localization in mobile app development, enhancing DevOps practices.

## Contribution

The paper presents RISING, a novel semi-supervised approach that effectively links user reviews to source code changes, improving localization accuracy over existing methods.

## Key findings

- RISING outperforms baseline methods in clustering accuracy.
- RISING improves localization precision and recall.
- Empirical results show more reliable change file identification.

## Abstract

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.