Add dataset card

README.md

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+---
+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.*