MobileDev-Bench: A Benchmark for Issue Resolution in Mobile Application Development
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915990423470080 |
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| author | Fakorede, Moshood A. Upadhyay, Krishna Siddique, A. B. Farooq, Umar |
| author_facet | Fakorede, Moshood A. Upadhyay, Krishna Siddique, A. B. Farooq, Umar |
| contents | Large language models (LLMs) have shown strong performance on automated software engineering tasks, yet existing benchmarks focus primarily on library-style repositories, leaving mobile application development largely unexplored despite its framework-specific build systems, heterogeneous artifact types, and coordinated multi-file fix requirements. We introduce MobileDev-Bench, a benchmark comprising 407 real-world issue-resolution tasks collected from 19 production mobile applications spanning Android Native (Java/Kotlin), React Native (TypeScript), and Flutter (Dart). Each task pairs a verified developer-reported issue with executable test patches, enabling fully automated validation of model-generated fixes within mobile build environments. The benchmark exhibits substantially greater patch complexity than prior benchmarks: fixes modify 12.9 files and 334.6 lines on average, and 41% of instances require coordinated changes across multiple artifact types, such as source, build configuration, and resource files. Evaluation of four frontier LLMs (Claude Sonnet 4.5, Qwen3-Coder, GPT-5.2, and Gemini 2.5 Flash) yields end-to-end resolution rates of only 3.23% - 4.23% under automated retrieval and at most 5.69% under oracle retrieval, well below resolution rates reported on existing benchmarks. We release MobileDev-Bench with task instances, an evaluation harness, and containerized environments to support reproducible research on AI-assisted mobile application development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24946 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MobileDev-Bench: A Benchmark for Issue Resolution in Mobile Application Development Fakorede, Moshood A. Upadhyay, Krishna Siddique, A. B. Farooq, Umar Software Engineering Machine Learning Large language models (LLMs) have shown strong performance on automated software engineering tasks, yet existing benchmarks focus primarily on library-style repositories, leaving mobile application development largely unexplored despite its framework-specific build systems, heterogeneous artifact types, and coordinated multi-file fix requirements. We introduce MobileDev-Bench, a benchmark comprising 407 real-world issue-resolution tasks collected from 19 production mobile applications spanning Android Native (Java/Kotlin), React Native (TypeScript), and Flutter (Dart). Each task pairs a verified developer-reported issue with executable test patches, enabling fully automated validation of model-generated fixes within mobile build environments. The benchmark exhibits substantially greater patch complexity than prior benchmarks: fixes modify 12.9 files and 334.6 lines on average, and 41% of instances require coordinated changes across multiple artifact types, such as source, build configuration, and resource files. Evaluation of four frontier LLMs (Claude Sonnet 4.5, Qwen3-Coder, GPT-5.2, and Gemini 2.5 Flash) yields end-to-end resolution rates of only 3.23% - 4.23% under automated retrieval and at most 5.69% under oracle retrieval, well below resolution rates reported on existing benchmarks. We release MobileDev-Bench with task instances, an evaluation harness, and containerized environments to support reproducible research on AI-assisted mobile application development. |
| title | MobileDev-Bench: A Benchmark for Issue Resolution in Mobile Application Development |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2603.24946 |