Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning
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 code is written by developers to capture system runtime behavior and plays a vital role in debugging, performance analysis, and system monitoring. However, defects in logging code can undermine the usefulness of logs and lead to misinterpretations. Although prior work has identified several logging defect patterns and provided valuable insights into logging practices, these studies often focus on a narrow range of defect patterns derived from limited sources (e.g., commit histories) and lack a systematic and comprehensive analysis. Moreover, large language models (LLMs) have demonstrated promising generalization and reasoning capabilities across a variety of code-related tasks, yet their potential for detecting logging code defects remains largely unexploredIn this paper, we derive a comprehensive taxonomy of logging code defects, which encompasses seven logging code defect patterns with 14 detailed scenarios. We further construct a benchmark dataset, Defects4Log, consisting of 164 developer-verified real-world logging defects. Then we propose an automated framework that leverages various prompting strategies and contextual information to evaluate LLMs’ capability in detecting and reasoning logging code defects. Experimental results reveal that LLMs generally struggle to accurately detect and reason logging code defects based on the source code only. However, incorporating proper knowledge (e.g., detailed scenarios of defect patterns) can lead to 10.9% improvement in detection accuracy. Overall, our findings provide actionable guidance for practitioners to avoid common defect patterns and establish a foundation for improving LLM-based reasoning in logging code defect detection.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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