Studying and Complementing the Use of Identifiers in Logs
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
Logs contain a large amount of curated run-time information about the process of a software. Modern software systems have become more complex and larger in scale. They are typically executed in parallel or distributively, resulting in interleaved software logs and making log analysis challenging. Despite extensive research on automated logging analysis, none to our knowledge focuses on the use of logs, and they rarely augment logs to help with simpler analysis. Software log IDs are unique identifiers that developers can use to group and filter log entries. However, we found that, on average, only 21% of logging statements produce IDs, which can lead to loss of information in the log file. We propose LTID, a static analysis approach on log IDs, to remediate the aforementioned issue by extracting a dependency relation between log statements from source code. We build a dependency graph using static analysis and compute the dominance relations of each logging statement. We then propagate IDs to logs that do not contain them based on the dependency graph. We studied 21 well-known Java open-source software subjects and were able to inject IDs on average into 12% of logs without IDs. Through an open coding process, we also establish a categorization, which has a Cohen’s Kappa agreement coefficient of 0.74, of the information gained to better understand the relations recovered by the ID propagation process.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 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