Topic Modeling-based Logging Suggestions for Java Software Systems
Bibliographic record
Abstract
Log statements help software developers and end users get informed about different valuable run-time information while log levels categorize the severity of that information. Researchers have been working extensively on log-related problems for the last two decades. As a result, a good amount of research has been conducted on logging and its practices. However, determining which topics can be logged from a system has a potential to work on. To implement our study, first, we examined the code snippets from some renowned open-source Java language-based projects. We collected the logged methods from nine applications and after preprocessing the methods and extracting our required data, we applied some renowned topic models: Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Non-negative Matrix Factorization (NMF). In the first part of the results, we showed how the topics are related to logging to investigate the alignment between topic modeling and logging. Our dataset, enriched with meaningful words related to method functionality, is subjected to LDA analysis. Results indicate that topics with the highest sum of word probabilities are more likely to be logged. In the second section, we listed the popular topics with their associated words from different systems generated by LDA. In the last part of the results, a comprehensive result was shown by evaluating the performance of the models using coherence scores. We believe that our research will not only be useful for its result and evaluation but also be helpful for future researchers by providing a unique dataset.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".