Towards a framework for software navigation techniques
Bibliographic record
Abstract
Making a change to a large software system requires investing time in understanding the system first. In the context of programming, navigation refers to the process of finding one's way through a complex network of programming constructs and other software artifacts. The process consists of answering questions such as "What am I looking at?" and "What do I want to look at next?", along with the question of "How do I get there?". This paper looks at the range of techniques used to aid navigation, and categorizes them using three perspectives: perceptual techniques, which use graphical representations and exploit spatial memory to aid navigation; filtering techniques, which operate by automatically reducing the amount of information provided so that the appropriate pieces of information are easy to find; and enrichment techniques, which involve augmenting the view of the software with peripheral information, so that the relative information can be more easily identified.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".