Anatomy of a crash repository
Bibliographic record
Abstract
This work investigates the properties of crash reports collected from Ubuntu Linux users. Understanding crash reports is important to better store, categorize, prioritize, parse, triage, assign bugs to, and potentially synthesize them. Understanding what is in a crash report, and how the metadata and stack traces in crash reports vary will help solve, debug, and prevent the causes of crashes. 10 different aspects of 40,592 crash reports about 1,921 pieces of software submitted by users and developers to the Ubuntu project were analyzed, plotted, and statistical distributions were fitted to some of them. We investigated the structure and properties of crash reports. Crashes have many properties that seem to have distributions similar to standard statistical distributions, but with even longer tails than expected. These aspects of crash reports have not been analyzed statistically before. We found that many applications only had a single crash, while a few applications had a large number of crashes reported. Crash bucket size (clusters of similar crashes) also followed a Zipf-like distribution. The lifespan of buckets ranged from less than an hour to over four years. Some stack traces were short, and some were so long they were truncated by the tool that produced them. Many crash reports had no recursion, some contained recursion, and some displayed evidence of unbounded recursion. Linguistics literature hinted that sentence length follows a gamma distribution; this is not the case for function name length. Additionally, only two hardware architectures, and a few signals are reported for almost all of the crashes in the Ubuntu dataset. Many crashes were similar but there were also many unique crashes. This study of crashes from 1,921 projects will be valuable for anyone who wishes to: cluster or deduplicate crash reports, synthesize or simulate crash reports, store or triage crash reports, or data-mine crash reports.
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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".