An Empirical Study on Log Level Prediction for Multi-Component Systems
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
Logging statements are used to trace the execution of a software system. Practitioners leverage different logging information (e.g., the content of a log message) to decide for each logging statement an appropriate log level, which is leveraged to adjust the verbosity of logs so that only important log messages are traced. Deciding for the log level can be done differently from one to another component of a multi-component system, such as OpenStack and its 28 components. For example, a component might aim for increasing the verbosity of its log messages, while another component for the same multi-component system might aim at decreasing such a verbosity. Such different logging strategies can exist since each component can be developed and maintained by a different team. While a prior work leveraged an ordinal regression model to recommend the appropriate log level for a new logging statement, their evaluation did not consider the particularities that each component can have within a multi-component system. For instance, their model might not perform well at each component level of a multi-component system. The same model’s interpretability can mislead the developers of each component that has its unique logging strategy. In this paper, we quantify the impact of the particularities of each component of a multi-component system on the performance and interpretability of the log level prediction model of prior work. We observe that the performance of the log level prediction models that are trained at the whole project level (aka., global models) have lower performances (AUC) on 72% to 100% of the components of our five evaluated multi-component systems, compared to the same models when evaluated on the whole multi-component system. We observe that the models that are trained at the component level (aka., local models) statistically outperform the global model on 33% to 77% of the components of our evaluated multi-component systems. Furthermore, we observe that the rankings of the most important features that are obtained from the global models are statistically different from the feature importance rankings of 50% to 87% of the local models of our evaluated multi-component systems. Finally, we observe that 60% and 35% of the Spring and OpenStack components do not have enough data points to train their own local models (aka., data lacking components). Leveraging a peer-local model for such type of components is more promising than using the global model.
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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 | 0.001 | 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