arXiv:2611.15001v1 Announce Type: new Abstract: Suggested edits allow low-reputation users to improve Stack Overflow posts and contribute to the platform's knowledge base. However, we still know little about why these edits are accepted or rejected and how reviewers can efficiently identify problematic edits. In this study, we analyze 3,888,805 suggested edits submitted between 2011 and May 2023. First, we compare accepted and rejected edits using 35 features. We find significant differences between them, with the largest differences related to users' editing history. Second, we manually analyze rejected edits and identify 17 fine-grained rejection reasons. These reasons provide a more detailed understanding of why edits are rejected than Stack Overflow's predefined rejection reasons. Third, we evaluate three models for prioritizing edits based on their likelihood of rejection. The Feature Model, built using the 35 studied features, provides the best trade-off between review rate and rejected-edit recall. At a decision threshold of $\tau = 0.40$, it prioritizes 52.3% of edits for human review while capturing 80.8% of rejected edits. In comparison, the Embedding Model captures 78.8% of rejected edits while prioritizing 60.9% of edits, and the EditEx Model captures 72.5% while prioritizing 59.0%. We also conduct a complementary zero-shot evaluation on a separate set of 500 edits. The Feature Model achieves 78.0% recall at $\tau = 0.40$, compared with 23.6%-31.2% for GPT-4o, Qwen2.5-32B, and Llama-3.3-70B. Overall, our findings provide a detailed understanding of suggested-edit rejection and show that rejection-risk prediction can help prioritize potentially problematic edits for human review.
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