Diagnosing and Resolving Android Applications Building Issues: An Empirical Study

2025-11-09

AI Summary

Android app builds frequently fail due to complex dependency graphs, heterogeneous configuration schemes, and rapid ecosystem evolution. To address this, we conduct an empirical study analyzing build logs from 200 open-source Android projects and propose a five-stage diagnosis-and-repair framework. We systematically categorize build failures into four canonical patterns—the first such taxonomy in the literature. Innovatively, we integrate a large language model (GPT-5) to generate context-aware repair suggestions, which are then validated through automated build execution and manual verification. Our evaluation shows successful repair of 102 out of 135 initially failing projects (75.6% success rate). Furthermore, we identify programming language choice, project age, and application scale as statistically significant factors influencing build reliability. This work contributes both a reusable methodology and empirically grounded insights to enhance Android build robustness and maintainability.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Diagnosing and resolving Android application build failures empirically
• Analyzing build errors including dependency and configuration issues
• Evaluating LLM-assisted error diagnosis and repair strategies effectiveness

Innovation

• Empirical analysis of 200 Android projects
• Five-phase process for diagnosing build failures
• LLM-assisted evaluation achieving 53.3% success rate