Automated User Experience Testing through Multi-Dimensional Performance Impact Analysis

TL;DR

This paper introduces an automated testing approach that predicts how code changes affect user experience by analyzing code features and test durations, enabling early feedback within continuous integration workflows.

Contribution

It presents a novel method combining code feature analysis and machine learning to estimate user experience impact of software updates, which is not addressed by traditional testing suites.

Findings

Achieved 3.7% mean absolute error with random forest regression.

Successfully integrated into CI pipelines for immediate user experience feedback.

Utilized Abstract Syntax Tree embeddings for semantic code analysis.

Abstract

Although there are many automated software testing suites, they usually focus on unit, system, and interface testing. However, especially software updates such as new security features have the potential to diminish user experience. In this paper, we propose a novel automated user experience testing methodology that learns how code changes impact the time unit and system tests take, and extrapolate user experience changes based on this information. Such a tool can be integrated into existing continuous integration pipelines, and it provides software teams immediate user experience feedback. We construct a feature set from lexical, layout, and syntactic characteristics of the code, and using Abstract Syntax Tree-Based Embeddings, we can calculate the approximate semantic distance to feed into a machine learning algorithm. In our experiments, we use several regression methods to estimate the time impact of software updates. Our open-source tool achieved 3.7% mean absolute error rate with a random forest regressor.