Using development history sticky notes to understand software architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maintenance of evolving software systems has become the most frequently performed activity by software developers. A good understanding of the software system is needed to reduce the cost and length of this activity. Various approaches and tools have been proposed to assist in this process such as code browsers, slicing techniques, etc. These techniques neglect to use a central and vital piece of data available - the historical modification records stored in source control systems. These records offer a rich and detailed account of the evolution of the software system to its current state. In this paper, we present an approach which recovers valuable information from source control systems and attaches this information to the static dependency graph of a software system. We call this recovered information - source sticky notes. We show how to use these notes along with the software reflexion framework to assist in understanding the architecture of large software systems. To demonstrate the viability of our approach, we apply it to understand the architecture of NetBSD - a large open source operating system.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it