Can Duplicate Questions on Stack Overflow Benefit the Software Development Community?
Bibliographic record
Abstract
Duplicate questions on Stack Overflow are questions that are flagged as being conceptually equivalent to a previously posted question. Stack Overflow suggests that duplicate questions should not be discussed by users, but rather that attention should be redirected to their previously posted counterparts. Roughly 53% of closed Stack Overflow posts are closed due to duplication. Despite their supposed overlapping content, user activity suggests duplicates may generate additional or superior answers. Approximately 9% of duplicates receive more views than their original counterparts despite being closed. In this paper, we analyze duplicate questions from two perspectives. First, we analyze the experience of those who post duplicates using activity and reputation-based heuristics. Second, we compare the content of duplicates both in terms of their questions and answers to determine the degree of similarity between each duplicate pair. Through analysis of the MSR challenge dataset, we find that although duplicate questions are more likely to be created by inexperienced users, they often receive dissimilar answers to their original counterparts. Indeed, supplementary textual analysis using Natural Language Processing (NLP) techniques suggests duplicate questions provide additional information about the underlying concepts being discussed. We recommend that the Stack Overflow's duplication policy be revised to account for the benefits that leaving duplicate questions open may have for the developer community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".