Studying Developer Reading Behavior on Stack Overflow during API Summarization Tasks
Bibliographic record
Abstract
Stack Overflow is commonly used by software developers to help solve problems they face while working on software tasks such as fixing bugs or building new features. Recent research has explored how the content of Stack Overflow posts affects attraction and how the reputation of users attracts more visitors. However, there is very little evidence on the effect that visual attractors and content quantity have on directing gaze toward parts of a post, and which parts hold the attention of a user longer. Moreover, little is known about how these attractors help developers (students and professionals) answer comprehension questions. This paper presents an eye tracking study on thirty developers constrained to reading only Stack Overflow posts while summarizing four open source methods or classes. Results indicate that on average paragraphs and code snippets were fixated upon most often and longest. When ranking pages by number of appearance of code blocks and paragraphs, we found that while the presence of more code blocks did not affect number of fixations, the presence of increasing numbers of plain text paragraphs significantly drove down the fixations on comments. SO posts that were looked at only by students had longer fixation times on code elements within the first ten fixations. We found that 16 developer summaries contained 5 or more meaningful terms from SO posts they viewed. We discuss how our observations of reading behavior could benefit how users structure their posts.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".