PASD: A Performance Analysis Approach Through the Statistical Debugging of Kernel Events
Bibliographic record
Abstract
Dynamic performance analysis plays a crucial role in optimizing systems and identifying performance bottlenecks. Traditional software debugging methods frequently encounter difficulties when trying to pinpoint performance problems in complex software settings. This is often because performance issues remain hidden during the code execution within debugging tools or under certain run-time circumstances, making them challenging to identify and address. This paper introduces PASD (Performance Analysis through Statistical Debugging), a dynamic performance analysis approach based on statistical debugging of kernel-level trace events. Importantly, this approach requires no application code instrumentation and purely utilizes operating system kernel trace events for analysis. PASD collects kernel trace events generated during software execution and utilizes heuristics to analyze their performance issues and the root-causes. Through statistical debugging techniques, PASD identifies the most important functions correlated with performance problems. It notably does so without disrupting the software’s normal functions and ensuring that any issues are detected in the software’s typical operating conditions, thus avoiding additional complexity in the debugging process. We have conducted two empirical studies to assess the effectiveness of PASD on performance issues in the Firefox web browser as well as the ‘ls’ tool (a common utility in Unix-like systems). Our experiments demonstrate that PASD successfully identifies performance issues and their causes in software without prior knowledge of the architecture or source code instrumentation. By providing an overview of software behavior through the kernel-level, our proposed method can aid developers and testers in quickly pinpointing performance problems in the source code. This, in turn, can result in improved software quality, increased user satisfaction, and the prevention of critical system failures.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".