Datasets:
license: cc-by-4.0
task_categories:
- text-classification
- question-answering
language:
- en
tags:
- software-engineering
- bug-reports
- performance-regression
- visualization
- trace-differencing
- empirical-study
size_categories:
- n<1K
VisualTraceBench v1.0 — Dataset Card
Benchmark for Visual Execution-Trace Differencing in Software Performance Regression Diagnosis
Following the Datasheet for Datasets framework (Gebru et al., 2021).
Motivation
Purpose: VisualTraceBench is a curated dataset of 192 real-world software performance regression bug reports from a large enterprise database system (2016–2025), annotated with trace artifact type (visual vs. text-only) and resolution timing. It enables empirical measurement of the Visualization Performance Enhancement Rate (VPER) — the efficiency gain from visual execution-trace differencing over text-only trace inspection — and supports benchmarking of AI systems on multimodal root-cause analysis tasks.
Who created it: The dataset was created by a researcher at a large enterprise software company as part of an internal retrospective study on developer productivity in performance regression workflows.
Funding: Internal research. No external funding.
Composition
Instances: 192 bug reports (performance regression incidents), each representing one resolved bug from the production CI/CD pipeline of a large-scale column-store database system.
Groups:
| Group | N | Description |
|---|---|---|
| A | 79 | Bugs where a visual execution-trace artifact (flame graph diff / PlanViz) was attached during diagnosis |
| B | 103 | Bugs where only text-format execution traces were available |
| C | 2 | Bugs with both visual and text trace artifacts |
| Unclassified | 8 | Incomplete annotation |
Features per instance:
| Column | Type | Description |
|---|---|---|
record_id |
int | Sequential identifier (1–192) |
bug_id_anon |
string | Anonymized stable bug ID (BUG-N) |
group |
string | A=visual, B=text-only, C=both, U=unclassified |
priority |
string | Bug priority (Showstopper / Very High / High / Medium / Low) |
severity |
string | Bug severity category |
visualized |
string | Raw annotation: O=visual trace present, X=absent |
text_traced |
string | Raw annotation: O=text trace present, X=absent |
reporting_date |
date | Date bug was reported (YYYY-MM-DD) |
bisected_date |
date | Date root cause was bisected / trace artifact attached |
planviz_date |
date | Date visual plan/trace artifact was generated (Group A only) |
fix_pushed_date |
date | Date fix was pushed to repository |
total_fix_hours |
float | Total hours from report to fix push |
post_attach_gap_hours |
float | Hours from trace attachment to fix push (core VPER metric) |
dup_of_bug_anon |
string | Anonymized ID of duplicate bug, if any |
additional_tag |
string | Researcher-applied classification tag |
customer_scenario |
string | Anonymized customer scenario tag (CUSTOMER_SCENARIO_X) |
product |
string | Product area (NewDB / LCM / QA Test Infrastructure) |
component |
string | Database component (56 categories, e.g. SQL Optimizer, Column Store) |
assignee_anon |
string | Pseudonymized developer ID (dev_001 … dev_094) |
reporter_anon |
string | Pseudonymized reporter ID (reporter_001 … reporter_011) |
root_cause_type |
string | Root cause category |
status |
string | Bug status at snapshot time |
resolution |
string | Resolution type (FIXED / WONTFIX / etc.) |
summary_scrubbed |
string | Bug title with emails and internal paths removed |
shipped_releases |
string | Release versions where fix was shipped |
planned_for |
string | Release milestone |
keywords |
string | QA/triage keywords |
flags |
string | QA delivery flags |
cvss_score |
float | CVSS score if applicable |
reported_release |
string | Release version where regression was observed |
reported_revision |
string | Revision string of affected build |
opened_date |
date | Date bug was opened in tracker |
num_comments |
int | Number of comments on the bug report |
Label / target for ML tasks: group (A vs B) and timing columns (post_attach_gap_hours, total_fix_hours) are the primary labels. The summary_scrubbed field supports NLP tasks.
Missing data: total_fix_hours and post_attach_gap_hours contain blank values where timing data was unavailable (formulas in original tracker). Subsample sizes used in analysis: metric1 A_n=41, B_n=87; metric2 A_n=75, B_n=49; metric3 A_n=37, B_n=33.
Sensitive data: All personally identifiable information has been removed. Email addresses replaced with pseudonyms (dev_NNN, reporter_NNN). Real customer names replaced with scenario tags. Bug IDs remapped to sequential anonymous IDs. Internal file paths and Gerrit links removed from free-text fields.
Collection Process
How collected: Retrospective extraction from an internal bug tracking system. Bugs were selected based on keyword search for performance regression indicators and manual annotation of trace artifact type by the original researcher.
Time span: 2016–2025 (approximately 9 years of production incidents).
Sampling: Consecutive incidents matching selection criteria — not a random sample. Represents the full population of qualifying bugs in the studied system during the period.
Direct collection: No crowdsourcing. All bugs represent real production incidents resolved by software engineers.
Preprocessing / Cleaning / Labeling
Preprocessing applied for this release:
- All SAP employee email addresses replaced with stable pseudonyms
- All real customer/company names replaced with anonymized scenario tags
- Original bug IDs remapped to sequential anonymous IDs (BUG-1 … BUG-192)
- Internal file system paths and Gerrit review links removed from free-text fields
- Free-text columns with high PII risk (steps to reproduce, solution text, delivery remarks) excluded entirely
groupcolumn derived from raw annotation columns (F, G) using rule: A=(F=O,G=O), B=(F=X,G≠X), C=(F=X,G=X)
Raw data: The original dataset with internal identifiers is retained by the authors and is not released.
Who performed labeling: The visualized/text_traced annotation was performed by the researcher conducting the retrospective study, based on inspection of bug report attachments.
Uses
Intended uses:
- Empirical software engineering research on visualization effectiveness in debugging
- Benchmarking AI systems on multimodal root-cause analysis (AI-VPER benchmark)
- Fault localization and performance regression detection research
- Training/evaluation of NLP models on bug report classification
Out-of-scope uses:
- Identifying individual developers or customers from the anonymized data
- Drawing conclusions about the performance of specific individuals
- Use as a representative sample of all software systems (single-system study)
Potential misuse: The pseudonymization is stable (dev_001 always refers to the same person) to enable longitudinal analysis, but should not be used to attempt re-identification.
Distribution
License: CC BY 4.0 (Creative Commons Attribution 4.0 International)
Citation:
@dataset{visualtracebench2026,
title = {VisualTraceBench: A Dataset for Visual Execution-Trace Differencing in Performance Regression Diagnosis},
author = {Anonymous},
year = {2026},
note = {Under double-blind review. Author details withheld.},
url = {[dataset URL]}
}
Version: v1.0 (2026-06-04)
Maintenance
Maintainer: Authors (identity withheld for double-blind review). Contact via paper submission portal.
Updates: Bug fixes to anonymization or annotation errors will be versioned (v1.1, v1.2). Major additions will increment the minor version.
Errata: None known at v1.0.
Key Statistics (from analysis_results.json)
| Metric | Group A (visual) | Group B (text) |
|---|---|---|
| N (total) | 79 | 103 |
| N (efficiency ratio subsample) | 37 | 33 |
| Median efficiency ratio | 0.464 | 0.643 |
| Median post-attach gap (hours) | 169 | 192 |
| Median acceleration multiplier | 2.16x | 1.56x |
| VPER | 27.9% | — |
| Mann-Whitney p (efficiency ratio) | 0.689 | — |
Note: p=0.689 reflects underpowered subsample (n=37+33). Effect size r=0.049. Acknowledged limitation — see paper.