ConfigMiner: Identifying the Appropriate Configuration Options for Config-Related User Questions by Mining Online Forums
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
While the behavior of a software system can be easily changed by modifying the values of a couple of configuration options, finding one out of hundreds or thousands of available options is, unfortunately, a challenging task. Therefore, users often spend a considerable amount of time asking and searching around for the appropriate configuration options in online forums such as StackOverflow. In this paper, we propose ConfigMiner, an approach to automatically identify the appropriate option(s) to config-related user questions by mining already-answered config-related questions in online forums. Our evaluation on 2,062 config-related user questions for seven software systems shows that ConfigMiner can identify the appropriate option(s) for a median of 83 percent (up to 91 percent) of user questions within the top-20 recommended options, improving over state-of-the-art approaches by a median of 130 percent. Besides, ConfigMiner reports the relevant options at a median rank of 4, compared to a median of 16-20.5 as reported by the state-of-the-art approaches.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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