An exploratory semantic analysis of logging questions
Bibliographic record
Abstract
Abstract Logging is an integral part of software development. Software practitioners often face issues in software logging, and they post these issues on Q&A websites to take suggestions from the experts. In this study, we perform a three‐level empirical analysis of logging questions posted on six popular technical Q&A websites, namely, Stack Overflow (SO), Serverfault (SF), Superuser (SU), Database Administrators (DB), Software Engineering (SE), and Android Enthusiasts (AE). The findings show that logging issues are prevalent across various domains, for example, database, networks, and mobile computing, and software practitioners from different domains face different logging issues. The semantic analysis of logging questions using Latent Dirichlet Allocation (LDA) reveals trends of several existing and new logging topics, such as logging conversion pattern , Android device logging , and database logging . In addition, we observe specific logging topics for each website: DB ( log shipping and log file growing/shrinking ), SU ( event log and syslog configuration ), SF ( log analysis and syslog configuration ), AE ( app install and usage tracking ), SE ( client server logging and exception logging ), and SO ( log file creation/deletion , Android emulator logging , and logger class of Log4j ). We obtain an increasing trend of logging topics on the SO, SU, and DB websites whereas a decreasing trend of logging topics on the SF website.
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.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".