Semantic Conflict Detection in Collaborative Software

• Semantic conflict detection is the process of identifying runtime inconsistencies in merged code that pass syntactic checks but manifest unintended behaviors during execution.
• It employs frameworks like SMAT to systematically generate and execute unit tests across different code versions, pinpointing behavioral deviations.
• Integrating large language models like Code Llama enhances test diversity and conflict detection, although it introduces challenges in computational scalability and configuration.

Semantic conflict detection refers to the identification of inconsistencies or unintended interferences at the behavioral (semantic) level that arise when integrating parallel changes in collaborative software development. Unlike textual or syntactic conflicts, which are flagged directly by version control or merge tools due to overlapping edits, semantic conflicts occur when merged code composes without compilation error yet exhibits undesired or incorrect runtime behavior. These conflicts typically surface only when the integrated system is executed or specifically tested, making their systematic detection a persistent challenge in modern software engineering.

1. Foundations of Semantic Conflict Detection

Semantic conflicts become especially prominent in environments where distributed version control systems (VCS) are used, allowing independent modification and integration of shared code bases. While a merge may succeed textually, unanticipated interactions between changes can silently introduce faults, e.g., by altering the order of method invocations, modifying related logic in non-overlapping code, or changing contracts in subtle ways. Standard VCS and merge tools are inherently limited in this context: their conflict detection is bounded to textual overlap and cannot capture inconsistencies that only manifest in the logical or dynamic behavior of the software.

The practical significance of semantic conflict detection lies in reducing integration-induced defects that are difficult to trace, thereby increasing the robustness and maintainability of collaborative software projects.

2. The SMAT Framework for Conflict Detection

To address the limitations of traditional tools, the Semantic Merge Analysis Tool (SMAT) was developed. SMAT operates by systematically generating and executing unit tests on four relevant corpus versions: Base (B), the two developers’ changed versions (commonly termed Left (L) and Right (R)), and the Merged (M) version. Its core operational principle is as follows: if a generated unit test

• fails on the base,
• passes on a developer’s individual version (L or R),
• but fails again after merging with the counterpart’s changes,

then a semantic conflict is indicated.

This execution-based verification extends beyond surface-level code inspection to actively probe whether the integrated behaviors of L and R, though individually valid, interfere destructively once composed. SMAT’s approach is modular in terms of the underlying test generation technique, allowing for the integration of various unit-test generators to expand the behavioral surface area explored.

However, empirical evaluation has shown that the effectiveness of SMAT is fundamentally constrained by the coverage and diversity of the generated unit tests. Tools such as Randoop and EvoSuite, which have been widely used within SMAT, can leave many semantic conflicts undetected (i.e., a high false-negative rate) due to insufficient exploration of realistic input domains or method invocation patterns, especially in complex or tightly coupled code.

3. Integration of LLMs for Test Generation

To augment the limitations of conventional test generators, recent developments have proposed the integration of LLMs—most prominently, Code Llama 70B—into the SMAT framework (Barbosa et al., 9 Jul 2025). Code Llama, trained specifically for program synthesis and code understanding, is leveraged to generate Java unit tests capable of capturing nuanced behavioral divergences arising from merges.

The integration process involves the LLM generating unit tests under multiple configurations, each attempting to maximize coverage and diversity:

• Zero-shot prompting: The model receives a role-defining prompt (e.g., “You are a senior Java developer with expertise in JUnit testing”) and is provided with relevant code context (fields, constructors, method bodies) without explicit test examples.
• One-shot prompting: The prompt additionally includes a sample method and a corresponding properly annotated JUnit test, offering a template and structuring guidance for the desired test code.

Variants in prompts also include summaries of Left/Right changes or snippets designed to direct model attention to suspected areas of conflict.

Parameter configuration is systematically varied:

• Temperature settings (e.g., 0.0 for deterministic, 0.7 for creative outputs) to balance diversity and precision,
• Random seeds (e.g., 42, 123) for output reproducibility and variance analysis.

This ensemble of interaction strategies and configurations aims to elicit a broader range of tests and thus probe for subtle and emergent semantic conflicts that might arise in merged code.

4. Empirical Evaluation and Outcomes

The extended SMAT with integrated LLM-based test generation was evaluated on two distinct datasets:

• mergedataset: A benchmark of 79 real-world merge scenarios from 29 open-source Java projects, containing both conflict and non-conflict cases.
• ASTER dataset: Simpler "toy" Java projects widely used in the literature.

Key findings include:

• Average tests generated per method in certain configurations exceeded 10,000, but compilation rates varied (e.g., ~12% for 1-shot at temperature 0.0). Compilation rate and test quality are sensitive not only to model configuration but also to code complexity.
• The Code Llama–augmented SMAT detected five semantic conflicts in aggregate across executions, surpassing traditional tools like Randoop (which detected two) and performing competitively with EvoSuite, all with no false positives.
• Notably, certain semantic conflicts were detected solely by unique configurations, e.g., zero-shot at temperature 0.0 finding a conflict in antlr4, while 1-shot at temperature 0.7 captured a conflict in spring-boot.
• Experiments highlight that computational cost is nontrivial: a full union of prompt/parameter sweeps can require several days of compute time, compared to hours for conventional tools.

This suggests that LLM-based test generation captures a broader behavioral spectrum but requires careful management of volume, test quality, and practical execution costs.

5. Strategies and Methodologies in Test Generation

The expanded SMAT approach underscores the criticality of prompt engineering and configuration diversity. Systematic exploration of prompt formats (zero-shot, one-shot, code summaries), temperature and randomness parameters, and even inclusion/exclusion of certain code artifacts are necessary to maximize the chance of triggering semantic divergences during testing.

Compiled test cases are validated across the four version contexts (B, L, R, M), and conflicts are triangulated through test outcome comparison:

• If a test passes in one developer’s version but fails in Base and Merge, interference from the other developer’s change is indicated.
• Absence of such pattern, especially across multiple diverse tests, reduces the likelihood that a semantic conflict has arisen.

Statistical tabulations and summary metrics provided in the paper facilitate detailed analysis and benchmarking of detection effectiveness and efficiency across method, configuration, and dataset boundaries.

6. Challenges, Limitations, and Future Directions

The principal challenges identified include:

• Computational Scalability: Generating and compiling thousands of tests per method using a large LLM can require prohibitive execution times, particularly for large or complex codebases.
• Sensitive Dependency on Prompt and Configuration: The likelihood of discovering semantic conflicts is nonuniform, with some configurations better suited for certain types of code or behavioral divergences.
• Complexity of Test Validation: High volumes of generated tests can stress build and test frameworks, necessitating automated filtering of non-compilable or irrelevant tests.

Looking forward, promising research directions encompass:

• More adaptive prompt engineering techniques that adjust in real time based on code complexity and history.
• Strategies to optimize, schedule, or prioritize test generations to reduce redundancy and cost.
• Smarter orchestration of model variants (including hydrated context from test history or code navigation tools) to target challenging code regions.
• Integration of LLM-generated test generation with methods such as retrieval-augmented generation or automated repair frameworks to both detect and resolve conflicts.

The positive detection outcomes, especially regarding previously unidentified semantic conflicts, indicate that LLM-based strategies—while currently computationally intensive—offer significant promise for scaling conflict detection in collaborative software environments, moving beyond the capabilities of traditional algorithmic or template-based tools.