Recommending and Release Planning of User-Driven Functionality Deletion for Mobile Apps

TL;DR

This paper introduces Radiation, a method that analyzes user reviews to recommend functionality deletions in mobile apps, aiming to improve usability and maintainability by systematically planning feature removals.

Contribution

The paper presents Radiation, a novel approach combining review analysis and developer surveys to support feature deletion decisions in mobile app release planning.

Findings

Radiation achieves an average F-Score of 74% in recommending deletions.

Most developers (77.3%) often or always plan for feature deletions.

Negative reviews significantly influence deletion recommendations.

Abstract

Evolving software with an increasing number of features poses challenges in terms of comprehensibility and usability. Traditional software release planning has predominantly focused on orchestrating the addition of features, contributing to the growing complexity and maintenance demands of larger software systems. In mobile apps, an excess of functionality can significantly impact usability, maintainability, and resource consumption, necessitating a nuanced understanding of the applicability of the law of continuous growth to mobile apps. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews. For most users, the removal of features is associated with negative sentiments, prompts changes in usage patterns, and may even result in user churn. Motivated by these preliminary results, we propose Radiation to input user reviews and recommend if any functionality should be deleted from an app's User Interface (UI). We evaluate radiation using historical data and survey developers' opinions. From the analysis of 190,062 reviews from 115 randomly selected apps, we show that Radiation can recommend functionality deletion with an average F-Score of 74% and if sufficiently many negative user reviews suggest so. We conducted a survey involving 141 software developers to gain insights into the decision-making process and the level of planning for feature deletions. Our findings indicate that 77.3% of the participants often or always plan for such deletions. This underscores the importance of incorporating feature deletion planning into the overall release decision-making process.