Enhancing Log Sentiments: An Exploratory Study of Sentiments and Emotions with Software Logs
Bibliographic record
Abstract
Software logs serve as valuable resources for understanding system running and are extensively used in diverse software maintenance tasks. Logs are generated by logging statements in the code, which are written by developers. Therefore, logs may reflect developers’ sentiments about the described situations. Consequently, when developers and system administrators read logs, the sentiments embedded in logs may influence their understanding. Although the sentiments associated with logs can convey valuable information, such information is not leveraged in research and practice. Previous research has primarily relied on verbosity levels of logs to gauge sentiments, which does not really capture the sentiments and emotions perceived by humans. To bridge this gap, in this article, we first conduct an exploratory study to investigate sentiments and emotions that are communicated within logs. Our study encompasses five anomaly log datasets from LogHub and a dataset involving eight open-source Apache Java projects. We find that 8% of the logs express sentiments and emotions though developers are suggested to write them in an objective way. While most log messages might not explicitly express sentiments and emotions, they can still implicitly evoke sentiments and emotions in those who read them. Therefore, we exploit issue reports referencing logs to capture such sentiments and emotions. In these issue reports, 47.5% exhibit emotions, with 54.7% of those emotions being related to logs and 8.1% directly addressing logs. Furthermore, we demonstrate the potential of leveraging sentiment analysis to complement verbosity levels in logs, showcasing how sentiment information can offer novel insights and enhance log analysis. Specifically, by applying automatic tools, we identify 41 issue reports (9.8% on average) with negative sentiment and 55 reports (13.2% on average) with negative emotions, all referencing INFO or DEBUG logs (i.e., low severity). After manually verifying and filtering exception logs, we uncover three main concerns from 22 critical instances.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".