From App Features to Explanation Needs: Analyzing Correlations and Predictive Potential

TL;DR

This study explores whether explanation needs in app reviews can be predicted from app properties, finding weak correlations and limited predictive power, emphasizing the importance of direct user feedback for explainability.

Contribution

It provides an empirical analysis of the correlation between app metadata and explanation needs, revealing their weak association and the limitations of predictive models.

Findings

Weak correlations between app properties and explanation needs

Limited predictive power of linear regression models

Certain categories like Security & Privacy show slightly higher predictability

Abstract

In today's digitized world, software systems must support users in understanding both how to interact with a system and why certain behaviors occur. This study investigates whether explanation needs, classified from user reviews, can be predicted based on app properties, enabling early consideration during development and large-scale requirements mining. We analyzed a gold standard dataset of 4,495 app reviews enriched with metadata (e.g., app version, ratings, age restriction, in-app purchases). Correlation analyses identified mostly weak associations between app properties and explanation needs, with moderate correlations only for specific features such as app version, number of reviews, and star ratings. Linear regression models showed limited predictive power, with no reliable forecasts across configurations. Validation on a manually labeled dataset of 495 reviews confirmed these findings. Categories such as Security & Privacy and System Behavior showed slightly higher predictive potential, while Interaction and User Interface remained most difficult to predict. Overall, our results highlight that explanation needs are highly context-dependent and cannot be precisely inferred from app metadata alone. Developers and requirements engineers should therefore supplement metadata analysis with direct user feedback to effectively design explainable and user-centered software systems.