README.md

---
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
- `group` column 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:**
1. Empirical software engineering research on visualization effectiveness in debugging
2. Benchmarking AI systems on multimodal root-cause analysis (AI-VPER benchmark)
3. Fault localization and performance regression detection research
4. 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.*