A Study on the Use of Runtime Files in Handling Crash Reports in a Large Telecom Company
Bibliographic record
Abstract
To minimize service downtime and ensure high system availability, Telecom companies must react quickly to failures and crashes of network services and applications. In this study, the focus is on the crash reporting process that normally goes through different levels of service support in a telecom company. To speed up this this repairing or recovering process, service support engineers and product developers rely on the analysis of runtime files (logs, traces, performance metrics, etc.) that are attached to crash reports submitted when an incident occurs. However, there is no clear understanding how these files are used and what their impact on the crash fixing time is. In this paper, we conduct an empirical study at Ericsson to study the use of runtime files in the crash resolution process. We tackle various research questions that revolve around the proportion of runtime files in a selected set of crash reports, the relationship between the severity of crashes and the type of files they contain, and the impact of different file types on the time to fix the crashes. We also study the prediction of the attachment of runtime files to crash reports during the creation of the reports. Our ultimate goal is to figure out how to collect enough information in the crash report system for support engineers and product developers to resolve the failures occurring in telecom network quickly and efficiently.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".