Bibliographic record
Abstract
Documentation is an important mechanism for disseminating software architecture knowledge. Software project teams can employ vastly different formats for documenting software architecture, from unstructured narratives to standardized documents. We explored to what extent this documentation format may matter to newcomers joining a software project and attempting to understand its architecture. We conducted a controlled questionnaire-based study wherein we asked 65 participants to answer software architecture understanding questions using one of two randomly-assigned documentation formats: narrative essays, and structured documents. We analyzed the factors associated with answer quality using a Bayesian ordered categorical regression and observed no significant association between the format of architecture documentation and performance on architecture understanding tasks. Instead, prior exposure to the source code of the system was the dominant factor associated with answer quality. We also observed that answers to questions that require applying and creating activities were statistically significantly associated with the use of the system's source code to answer the question, whereas the document format or level of familiarity with the system were not. Subjective sentiment about the documentation format was comparable: Although more participants agreed that the structured document was easier to navigate and use for writing code, this relation was not statistically significant. We conclude that, in the limited experimental context studied, our results contradict the hypothesis that the format of architectural documentation matters. We surface two more important factors related to effective use of software architecture documentation: prior familiarity with the source code, and the type of architectural information sought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".