Characterizing and Detecting Anti-Patterns in the Logging Code
Bibliographic record
Abstract
Snippets of logging code are output statements (e.g., LOG.info or System.out.println) that developers insert into a software system. Although more logging code can provide more execution context of the system's behavior during runtime, it is undesirable to instrument the system with too much logging code due to maintenance overhead. Furthermore, excessive logging may cause unexpected side-effects like performance slow-down or high disk I/O bandwidth. Recent studies show that there are no well-defined coding guidelines for performing effective logging. Previous research on the logging code mainly tackles the problems of where-to-log and what-to-log. There are very few works trying to address the problem of how-to-log (developing and maintaining high-quality logging code). In this paper, we study the problem of how-to-log by characterizing and detecting the anti-patterns in the logging code. As the majority of the logging code is evolved together with the feature code, the remaining set of logging code changes usually contains the fixes to the anti-patterns. We have manually examined 352 pairs of independently changed logging code snippets from three well-maintenance open source systems: ActiveMQ, Hadoop and Maven. Our analysis has resulted in six different anti-patterns in the logging code. To demonstrate the value of our findings, we have encoded these anti-patterns into a static code analysis tool, LCAnalyzer. Case studies show that LCAnalyzer has an average recall of 95% and precision of 60% and can be used to automatically detect previously unknown anti-patterns in the source code. To gather feedback, we have filed 64 representative instances of the logging code anti-patterns from the most recent releases of ten open source software systems. Among them, 46 instances (72%) have already been accepted by their developers.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".