Diagnosing and Resolving Android Applications Building Issues: An Empirical Study

TL;DR

This empirical study analyzes common Android build failures in open-source projects, proposing diagnostic and repair strategies, and evaluating AI assistance, to improve build reliability and developer efficiency.

Contribution

It introduces a comprehensive empirical analysis of Android build errors, a repair strategy, and assesses Large Language Models' effectiveness in error diagnosis.

Findings

75.56% of build failures were resolved using the proposed repair strategy.

LLMs like GPT-5 achieved a 53.3% success rate in suggesting fixes.

Build success correlates with programming language, project age, and size.

Abstract

Building Android applications reliably remains a persistent challenge due to complex dependencies, diverse configurations, and the rapid evolution of the Android ecosystem. This study conducts an empirical analysis of 200 open-source Android projects written in Java and Kotlin to diagnose and resolve build failures. Through a five-phase process encompassing data collection, build execution, failure classification, repair strategy design, and LLM-assisted evaluation, we identified four primary types of build errors: environment issues, dependency and Gradle task errors, configuration problems, and syntax/API incompatibilities. Among the 135 projects that initially failed to build, our diagnostic and repair strategy enabled developers to resolve 102 cases (75.56%), significantly reducing troubleshooting effort. We further examined the potential of Large Language Models, such as GPT-5, to assist in error diagnosis, achieving a 53.3% success rate in suggesting viable fixes. An analysis of project attributes revealed that build success is influenced by programming language, project age, and app size. These findings provide practical insights into improving Android build reliability and advancing AI-assisted software maintenance.