Bibliographic record
Abstract
Log messages have been used widely in many software systems for a variety of purposes during software development and field operation. There are two phases in software logging: log instrumentation and log management. Log instrumentation refers to the practice that developers insert logging code into source code to record runtime information. Log management refers to the practice that operators collect the generated log messages and conduct data analysis techniques to provide valuable insights of runtime behavior. There are many open source and commercial log management tools available. However, their effectiveness highly depends on the quality of the instrumented logging code, as log messages generated by high-quality logging code can greatly ease the process of various log analysis tasks (e.g., monitoring, failure diagnosis, and auditing). Hence, in this article, we conducted a systematic survey on state-of-the-art research on log instrumentation by studying 69 papers between 1997 and 2019. In particular, we have focused on the challenges and proposed solutions used in the three steps of log instrumentation: (1) logging approach; (2) logging utility integration; and (3) logging code composition. This survey will be useful to DevOps practitioners and researchers who are interested in software logging.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".