MergirafSemi: A Language-Agnostic Semistructured Merge Tool

Balancing structural granularity and runtime efficiency in automated merge conflict resolution

Pedro Lopes; Paulo Borba; Paola Accioly; Guilherme Cavalcanti· 2026· DOI 10.48550/arXiv.2608.11345

The core problem

Developers frequently face merge conflicts when integrating concurrent changes. Most merge tools rely on unstructured, line-based comparisons, often producing spurious conflicts and missing actual conflicts. To address these limitations, structure-aware merge tools have been proposed, which leverage syntactic representations to improve merge accuracy. However, fully structured tools may incur higher computational cost, and language-specific tools require significant development and maintenance effort. To balance these trade-offs, the authors propose MergirafSemi, a language-agnostic semistructured merge tool that captures structural information without requiring full structural modeling or language-specific implementations, and applies line-based merging within specific program regions, such as method bodies in Java. The tool leverages lightweight Concrete Syntax Trees (CSTs) to guide merging decisions while preserving flexibility and efficiency across languages.

The empirical study showed that increasing structural granularity improves automatic conflict resolution but can also lead to more aggressive merge decisions, increasing the number of missed actual conflicts. In contrast, MergirafSemi achieves a more balanced trade-off, reducing spurious conflicts while maintaining competitive accuracy and better runtime performance in most common scenarios. Compared to an unstructured tool, it substantially reduces spurious conflicts. When compared to a semistructured language-specific tool, it achieves comparable effectiveness while exhibiting more robust execution behavior and significantly lower runtime overhead. The results indicate that MergirafSemi effectively balances the benefits of structural awareness with the efficiency of line-based merging.

Developers frequently face merge conflicts when integrating concurrent changes. Most merge tools rely on unstructured, line-based comparisons, often producing spurious conflicts and missing actual conflicts. To address these limitations, structure-aware merge tools have been proposed, which leverage syntactic representations to improve merge accuracy. However, fully structured tools may incur higher computational cost, and language-specific tools require significant development and maintenance effort. To balance these trade-offs, the authors propose MergirafSemi, a language-agnostic semistructured merge tool that captures structural information without requiring full structural modeling or language-specific implementations, and applies line-based merging within specific program regions, such as method bodies in Java. The tool leverages lightweight Concrete Syntax Trees (CSTs) to guide merging decisions while preserving flexibility and efficiency across languages.

MergirafSemi is designed as a language-agnostic semistructured merge tool. It uses lightweight Concrete Syntax Trees to identify structural boundaries, such as method bodies, and then applies line-based merging within those regions. This approach avoids the overhead of full structural modeling and the need for language-specific implementations. The tool was evaluated through an empirical study on real-world merge scenarios across multiple programming languages. The study compared MergirafSemi with unstructured, semistructured, and structured tools. The evaluation focused on automatic conflict resolution, spurious conflicts, missed actual conflicts, and runtime performance. The authors also analyzed the trade-offs between structural granularity and merge accuracy.

The findings highlight the importance of balancing structural granularity and merge accuracy. While fully structured tools can resolve more conflicts automatically, they may also introduce false positives by missing actual conflicts. Language-specific semistructured tools offer better accuracy but at the cost of development and maintenance effort, as well as potential runtime overhead. MergirafSemi addresses these issues by being language-agnostic and using lightweight CSTs, which reduces the need for language-specific implementations and improves runtime performance. The tool's ability to apply line-based merging within specific program regions allows it to capture structural information without full structural modeling. This approach is particularly suitable for scenarios where developers need a balance between conflict resolution accuracy and computational efficiency. Future work could explore extending the tool to more languages and evaluating its performance on larger datasets.

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