Leveraging Informal Documentation to Summarize Classes and Methods in Context
Bibliographic record
Abstract
Critical information related to a software developer'scurrent task is trapped in technical developer discussions,bug reports, code reviews, and other software artefacts. Muchof this information pertains to the proper use of code elements(e.g., methods and classes) that capture vital problem domainknowledge. To understand the purpose of these code elements,software developers must either access documentation and onlineposts and understand the source code or peruse a substantialamount of text. In this paper, we use the context that surroundscode elements in StackOverflow posts to summarize the use andpurpose of code elements. To provide focus to our investigation,we consider the generation of summaries for library identifiersdiscussed in StackOverflow. Our automatic summarization approachwas evaluated on a sample of 100 randomly-selectedlibrary identifiers with respect to a benchmark of summariesprovided by two annotators. The results show that the approachattains an R-precision of 54%, which is appropriate given thediverse ways in which code elements can be used.
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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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".