Semantic conflict detection via dynamic analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During collaborative software development, a semantic conflict may occur when the individual behavior expected by different developers is no longer preserved after merging their branches. While potential semantic conflicts are not captured via textual merge tools, different approaches have already been proposed based on static analysis or automated test generation to verify behavioral changes given a merge scenario. However, these approaches share some limitations regarding scalability and reporting false positives and negatives. Trying to address these limitations, in this work, we assess the detection of conflicts by focusing on overriding assignments in JavaScript code through dynamic analysis. Dynamic analysis allows us to detect changes involving writing operations to the same state element at runtime and does not need assertions about the under-analysis code, such as in test-based approaches, requiring only the final version of the merged code (post-merge) to be executed. To evaluate our approach, besides translating test cases from related works, we empirically analyze merge scenarios from 50 JavaScript opensource projects hosted on GitHub, from which we correctly detected one scenario of overriding assignment representing a potential semantic conflict with no false positives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it