LLM-Based Repair of Static Nullability Errors
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917253230886912 |
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| author | Karimipour, Nima Joos, Pascal Pradel, Michael Kellogg, Martin Sridharan, Manu |
| author_facet | Karimipour, Nima Joos, Pascal Pradel, Michael Kellogg, Martin Sridharan, Manu |
| contents | Modern Java projects increasingly adopt static analysis tools that prevent null-pointer exceptions by treating nullness as a type property. However, integrating such tools into large, existing codebases remains a significant challenge. While annotation inference can eliminate many errors automatically, a subset of residual errors -- typically a mix of real bugs and false positives -- often persist and can only be resolved via code changes. Manually addressing these errors is tedious and error-prone. Large language models (LLMs) offer a promising path toward automating these repairs, but naively-prompted LLMs often generate incorrect, contextually-inappropriate edits. We present NullRepair, a system that integrates LLMs into a structured workflow for resolving the errors from a nullability checker. NullRepair's decision process follows a flowchart derived from manual analysis of 200 real-world errors. It leverages static analysis to identify safe and unsafe usage regions of symbols, using error-free usage examples to contextualize model prompts. Patches are generated through an iterative interaction with the LLM that incorporates project-wide context and decision logic. Our evaluation on 12 real-world Java projects shows that NullRepair resolves 63% of the 1,119 nullability errors that remain after applying a state-of-the-art annotation inference technique. Unlike two baselines (single-shot prompt and mini-SWE-agent), NullRepair also largely preserves program semantics, with all unit tests passing in 10/12 projects after applying every edit proposed by NullRepair, and 98% or more tests passing in the remaining two projects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20674 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Based Repair of Static Nullability Errors Karimipour, Nima Joos, Pascal Pradel, Michael Kellogg, Martin Sridharan, Manu Software Engineering Programming Languages Modern Java projects increasingly adopt static analysis tools that prevent null-pointer exceptions by treating nullness as a type property. However, integrating such tools into large, existing codebases remains a significant challenge. While annotation inference can eliminate many errors automatically, a subset of residual errors -- typically a mix of real bugs and false positives -- often persist and can only be resolved via code changes. Manually addressing these errors is tedious and error-prone. Large language models (LLMs) offer a promising path toward automating these repairs, but naively-prompted LLMs often generate incorrect, contextually-inappropriate edits. We present NullRepair, a system that integrates LLMs into a structured workflow for resolving the errors from a nullability checker. NullRepair's decision process follows a flowchart derived from manual analysis of 200 real-world errors. It leverages static analysis to identify safe and unsafe usage regions of symbols, using error-free usage examples to contextualize model prompts. Patches are generated through an iterative interaction with the LLM that incorporates project-wide context and decision logic. Our evaluation on 12 real-world Java projects shows that NullRepair resolves 63% of the 1,119 nullability errors that remain after applying a state-of-the-art annotation inference technique. Unlike two baselines (single-shot prompt and mini-SWE-agent), NullRepair also largely preserves program semantics, with all unit tests passing in 10/12 projects after applying every edit proposed by NullRepair, and 98% or more tests passing in the remaining two projects. |
| title | LLM-Based Repair of Static Nullability Errors |
| topic | Software Engineering Programming Languages |
| url | https://arxiv.org/abs/2507.20674 |