Exploring Relevant Artifacts of Release Notes: The Practitioners' Perspective
Bibliographic record
Abstract
A software release note is one of the essential documents in the software development life cycle. The software release contains a set of information, e.g., bug fixes and security fixes. Release notes are used in different phases, e.g., requirement engineering, software testing and release management. Different types of practitioners (e.g., project managers and clients) get benefited from the release notes to understand the overview of the latest release. As a result, several studies have been done about release notes production and usage in practice. However, two significant problems (e.g., duplication and inconsistency in release notes contents) exist in producing well-written & well-structured release notes and organizing appropriate information regarding different targeted users' needs. For that reason, practitioners face difficulties in writing and reading the release notes using existing tools. To mitigate these problems, we execute two different studies in our paper. First, we execute an exploratory study by analyzing 3,347 release notes of 21 GitHub repositories to understand the documented contents of the release notes. As a result, we find relevant key artifacts, e.g., issues (29%), pull-requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%) in the release note contents. Second, we conduct a survey study with 32 professionals to understand the key information that is included in release notes regarding users' roles. For example, project managers are more interested in learning about new features than less critical bug fixes. Our study can guide future research directions to help practitioners produce the release notes with relevant content and improve the documentation quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".