Diagnosing and Resolving Android Applications Building Issues: An Empirical Study
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arXiv
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| Format: | Preprint |
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2025
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| author | Bodepudi, Lakshmi Priya Zhao, Yutong Fu, Ming Quan Wu, Yuanyuan He, Sen Zhao, Yu |
| author_facet | Bodepudi, Lakshmi Priya Zhao, Yutong Fu, Ming Quan Wu, Yuanyuan He, Sen Zhao, Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diagnosing and Resolving Android Applications Building Issues: An Empirical Study Bodepudi, Lakshmi Priya Zhao, Yutong Fu, Ming Quan Wu, Yuanyuan He, Sen Zhao, Yu Software Engineering 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. |
| title | Diagnosing and Resolving Android Applications Building Issues: An Empirical Study |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2511.06186 |