Data library
Software engineering data for AI.
Quick answer
Software engineering data includes source code, commit histories, pull-request reviews, issues and CI results. It shows how professional teams write, review and fix code, which coding models and agents need.
What's included#
- Repositories with history
- Pull requests and review comments
- Linked issues and fixes
- CI/CD logs
Where it typically lives
How AI developers use it#
- Coding models
- Code-review assistants
- Software agents
Preparation and privacy#
- Scrub secrets and credentials
- Check open-source license obligations
- Exclude customer code
How SourceX approaches it#
Preparation requirements are agreed with you before any work begins, and the approved dataset is delivered only after an executed agreement and your explicit authorization of buyer access.
Industries with this data
Datasets built from this data
Dataset specs AI labs can request through SourceX, sourced to spec from businesses like yours.
- Software and ITSoftware engineering histories (issues, PRs, reviews)A software engineering history dataset is a linked record of how a team changed its code: each issue joined to the commits, pull request, review comments…CodeTextStructured records
- Software and ITProprietary codebases with full historyA proprietary codebase dataset is a company's private source code delivered as whole repositories with their version history: commits, branches, tags and…Code
- Software and ITIT service management and incident historiesAn ITSM ticket dataset is the record of how an IT organization handled its incidents, service requests, problems and changes, exported from its service…TextStructured records
See listings in the data catalog →
- AI lab? Browse Software engineering histories (issues, PRs, reviews)
- AI lab? Browse Proprietary codebases with full history
Typical fields#
- Record ID (pseudonymous)
- Created and closed timestamps
- Category or type
- Request or input text
- Ordered steps or actions
- Outcome or resolution
- Role of each actor (not identity)
Sample record
Illustrative · fictional
- record_id
- rec_8f21c0
- created / closed
- 2023-04-11 → 2023-04-12
- category
- Billing question
- request
- Customer [NAME] asks why invoice [ID] was charged twice.
- steps
- 1. Checked payment log 2. Found duplicate 3. Issued refund
- outcome
- Resolved · refund issued
- actors
- customer, agent_tier1
What buyers look for#
- Complete start-to-finish records
- Consistent fields over years
- Clear, labeled outcomes
- Low duplication
- Work done by skilled professionals
- Edge cases and failures, not only successes
Personal details removed#
- Names
- Email addresses
- Phone numbers
- Postal addresses
- Account, customer and order numbers
- Government IDs
- Payment details
- Health information
- IP addresses and device IDs
- Credentials, keys and hostnames
How buyers test it#
- Sample review by buyer researchers
- Residual personal-data scan
- Duplicate and leakage check against public data
- Label and outcome accuracy spot-check
- Rights and consent documentation review
Rules that may apply
Federal Wiretap Act 18 U.S.C. 2511
Applies to recorded calls and intercepted communications; some states require all-party consent. Assessed deal by deal.
California Consumer Privacy Act Cal. Civ. Code 1798.100 et seq.
May apply where records include personal information of California residents and the business meets the law's thresholds. Assessed deal by deal.
Reviewer note: applicability is confirmed with counsel for each deal. Not legal advice.
All data types · Accounting and finance data · Standard operating procedures · Operations data
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