Can We Identify Stack Overflow Questions Requiring Code Snippets? Investigating the Cause & Effect of Missing Code Snippets
Bibliographic record
Abstract
On the Stack Overflow (SO) Q&A site, users often request solutions to their code-related problems (e.g., errors, unexpected behavior). Unfortunately, they often miss required code snippets during their question submission. Such a practice could prevent their questions from getting prompt and appropriate answers. In this study, we conduct an empirical study investigating the cause & effect of missing code snippets in SO questions whenever required. In this paper, our contributions are threefold. First, we analyze how the presence or absence of required code snippets in SO questions affects the correlation between question types (missed code, included code after requests & had code snippets during submission) and corresponding answer meta-data, such as the presence of an accepted answer. According to our analysis, the chance of getting accepted answers is three times higher for questions that include required code snippets during their question submission than those that missed the code. We also investigate the confounding factors (e.g., user reputation) that can affect questions receiving answers besides the presence or absence of required code snippets. We found that such factors do not hurt the correlation between the presence or absence of required code snippets and answer meta-data. Second, we surveyed 64 practitioners to understand why users miss necessary code snippets. About 60% of them agree that users are unaware of whether their questions require any code snippets. Third, we thus extract four text-based features (e.g., keywords, POS-based patterns) and build six Machine Learning (ML) models to identify the questions that need code snippets. Our models can predict the target questions with 86.5 % precision, 90.8 % recall, 85.3 % F1-score, and 85.2 % overall accuracy, which are highly promising. Our work has the potential to ($a$) save significant time in programming question-answering and (b) improve the quality of the valuable knowledge base by decreasing unanswered and unresolved questions.
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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 | 0.020 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".