How Much Logs Does My Source Code File Need? Learning to Predict the Density of Logs
Bibliographic record
Abstract
Software logging is the practice of recording different events that occur within a software system, which are useful for several analysis activities. However, striking the right balance between logging and system overhead is challenging. Prior work has conducted various machine learning-based solutions to suggest where to insert logging statements. But most importantly, before answering the question “where to log?’’, practitioners first need to determine whether a file needs logging at the first place. To do so, we conduct in this paper an empirical study to characterize the log density (i.e., ratio of log lines over the total lines of code) in seven open-source software projects. Then, we propose a deep learning based approach to predict the log density based on syntactic and semantic features of the source code. We find that the percentage of files with at least one log line ranges from 5% to 33% across the studied projects. Additionally, the median log density in the files with at least one log line ranges from 0.95% to 1.85% across the seven projects and can go up to 18%. Our findings resonate with the hypothesis that not all source code files require logging. On the other hand, our log density models achieve an average accuracy of 84%. Whereas our cross-project log density prediction results show a promising performance with an average accuracy of 72%, which represents over 86% (ratio of cross/within) of the corresponding within-project predictions using syntactic features. Our results show that we can accurately predict whether a file needs logging and such predictions may be generalized across projects.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".