Efficient mapping of software system traces to architectural views
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
Information about a software system’s execution can help a developer with many tasks, including software testing, performance tuning, and program understanding. In almost all cases, this dynamic information is reported in terms of source-level constructs, such as procedures and methods. For some software engineering tasks, source-level information is not optimal because there is a wide gap between the information presented (i.e., procedures) and the concepts of interest to the software developer (i.e., subsystems). One way to close this gap is to allow developers to investigate the execution information in terms of a higher-level, typically architectural, view. In this paper, we present a straightforward encoding technique for dynamic trace information that makes it tractable and efficient to manipulate a trace from a variety of different architecture-level viewpoints. We also describe how this encoding technique has been used to support the development of two tools: a visualization tool and a path query tool. We present this technique to enable the development of additional tools that manipulate dynamic information at a higherlevel than source.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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