How to Prove 8 Hours Billed Equals 8 Hours Worked
Here is a stat that should make every engineering manager uncomfortable: 88% of managers suspect at least some timesheet padding on their teams. Not because their developers are dishonest — but because there is no mechanism to verify that eight hours billed actually produced eight hours of deliverables. Timesheets, in most organizations, are trust exercises disguised as accountability tools.
And trust, without verification, is how expensive problems hide in plain sight.
The CrowdStrike Lesson: Effort Is Not the Same as Verified Output
In July 2024, CrowdStrike pushed a single bad update that brought cars, banks, airports, and hospitals to a halt. The engineers who worked on that update almost certainly logged their hours. They probably logged them accurately. The problem was not effort — it was unverified output. Nobody caught the gap between work performed and work validated before it shipped.
The same principle applies to every hour your team bills. A developer can honestly log eight hours on a Jira ticket, but if those eight hours produced three commits with minimal code changes and the ticket is still in progress two sprints later, something is off. Without proof that hours map to deliverables, you are managing by faith rather than data.
This is not about catching bad actors. It is about building a system where good work is visible, underperformance is detectable, and billing disputes become impossible.
What Proof-Based Work Tracking Actually Looks Like
Traditional timesheet verification means a manager eyeballs a spreadsheet and asks, "Does this look right?" That approach fails for three reasons:
- It does not scale. A manager with 12 direct reports cannot meaningfully review 480 hours of logged time each week.
- It lacks context. A timesheet entry that says "Backend development — 6 hours" tells you nothing about what was actually produced.
- It is always retrospective. By the time you spot a problem, the invoice has already gone out.
Proof-based tracking flips this model. Instead of asking developers to describe their work, the system cross-references their logged hours against artifacts that already exist: GitHub commits, pull requests, Jira ticket transitions, and code review activity. The proof is generated automatically as a byproduct of normal work.
How to Cross-Reference Timesheets with GitHub Commits
GitHub is a goldmine for timesheet verification because commits carry timestamps, diffs, and author metadata. Here is how to build a verification layer:
- Map commit timestamps to timesheet blocks. If a developer logged 9 AM to 5 PM on a project, there should be commit activity (or at minimum, branch activity) within that window.
- Measure commit volume against logged hours. This is not about lines of code — it is about consistency. An eight-hour day with zero commits and no PR activity is a flag worth investigating.
- Track PR review cycles. Code reviews are legitimate work. A developer who spent three hours reviewing a complex PR has verifiable activity even without their own commits.
- Flag time gaps. Two hours logged with no corresponding Git activity does not automatically mean fraud — the developer might have been in meetings or doing research. But it does mean the system should prompt for a note or context.
The key is that verification should be automatic and non-invasive. Developers should not need to change their workflow. The system reads the activity they already generate.
How to Verify Billable Hours Against Jira Tickets
Jira (or any issue tracker) provides a second verification axis. While GitHub shows what was built, Jira shows why it was built and how long it was expected to take:
- Compare logged hours to ticket estimates. If a ticket was estimated at four hours but the developer logged twelve, the system should flag this — not as a problem, but as a conversation starter. Maybe the estimate was wrong. Maybe scope crept. Either way, you want to know.
- Track ticket status transitions. Hours logged against a ticket that has not moved from "In Progress" in two weeks tells a different story than hours logged against a ticket that moved through code review and QA.
- Correlate tickets to commits. Modern Git workflows use branch naming conventions (e.g., feature/PROJ-123) that link commits to Jira tickets. This creates a three-way verification: timesheet entry → Jira ticket → Git commits.
- Spot orphaned time entries. Hours logged without an associated ticket are not necessarily wrong, but they should be the exception, not the norm.
When you combine GitHub and Jira verification, you get a complete audit trail from timesheet entry to delivered feature. This is not surveillance — it is documentation.
Time Recovery: Finding the Hours You Already Worked but Did Not Bill
Here is the part most timesheet discussions miss: verification works in both directions. Teams that implement proof-based tracking consistently discover that developers are under-billing, not over-billing. Common examples:
- Code reviews that take 45 minutes but never get logged
- Hotfix deployments at 7 PM that nobody tracks
- Slack-based debugging sessions that consume an hour but feel like "not real work"
- CI/CD pipeline investigations after a failed build
AI-powered time recovery scans your GitHub and Jira activity for work that was performed but never logged. For agencies and consultancies billing hourly, recovering even two to three hours per developer per week can represent tens of thousands in annual revenue. More importantly, it means your team gets credit for the work they actually do.
How BetterFlow Automates Proof-Based Verification
BetterFlow was built to solve exactly this problem. Rather than adding more manual steps to timesheet management, it automates verification using AI that cross-references multiple data sources:
- AI Verification Engine: Automatically compares timesheet entries against GitHub commit history, Jira ticket activity, and PR timelines. Flags discrepancies for review without requiring manager intervention on every entry.
- GitHub Integration: Connects to your repositories and maps commit timestamps, branch activity, and PR reviews to logged hours. No changes to your Git workflow required.
- Jira Integration: Pulls ticket data including estimates, status transitions, and assignments to verify that logged hours align with project work.
- Time Recovery Dashboard: Surfaces unlogged work detected from development activity — code reviews, after-hours commits, and debugging sessions that your team performed but forgot to bill.
- Verification Dashboards: Give managers a single view of verification status across teams, with drill-down capability into any flagged entry. No more spreadsheet reviews.
Full transparency: BetterFlow is built by BetterQA, a QA company with 50+ engineers. BetterQA built BetterFlow to solve their own billable hours verification problem and uses it daily to verify every hour billed to clients. The platform also integrates with BugBoard for teams that need end-to-end QA workflow management alongside time verification.
Can AI Verification Replace Manual Timesheet Review?
For the majority of entries, yes. AI verification handles the routine work of checking that logged hours have corresponding activity in GitHub and Jira. This eliminates the need for managers to manually review every line item. However, AI flags edge cases — like legitimate research time or meeting-heavy days — for human review. The goal is to reduce manual review by 80-90%, not eliminate human judgment entirely. Think of it as an automated first pass that only escalates what actually needs attention.
Does Timesheet Verification Feel Like Surveillance to Developers?
Implementation matters more than the tool itself. Teams that frame verification as "we want to make sure you get credit for all your work" see higher adoption than teams that frame it as "we are checking up on you." In practice, most developers prefer proof-based systems because they eliminate subjective performance debates. When your commits, PRs, and ticket completions speak for themselves, you do not need to justify your time in standups. The data does it for you.
What Happens When Timesheets and Git Activity Do Not Match?
A mismatch is not automatically a problem — it is a signal. Common legitimate reasons include: the developer was in meetings or design sessions, they were pair programming on a colleague's machine, they were doing research or documentation work, or they were onboarding and reading code without making changes. The verification system flags mismatches and provides context so managers can have informed conversations rather than accusatory ones. Over time, teams develop patterns that the AI learns to recognize, reducing false flags significantly.
How Quickly Can a Team Implement Proof-Based Time Tracking?
If your team already uses GitHub and Jira, the integration setup takes under an hour. BetterFlow connects via API tokens — no repository access changes, no Jira admin permissions, no workflow modifications. Most teams see their first verification reports within 24 hours of connecting their tools. The AI calibration period — where the system learns your team's normal patterns — takes about two weeks. After that, false flag rates typically drop below 5%.
Is AI Timesheet Verification Worth It for Small Teams?
Small teams (under 10 developers) often benefit the most because the cost of unverified hours is proportionally higher. A five-person consultancy losing three unbilled hours per developer per week at $150/hour is leaving $117,000 on the table annually. For small teams, the time recovery feature alone typically pays for the platform within the first month. Additionally, small teams billing enterprise clients often face stricter audit requirements — proof-based verification gives you documentation that survives client scrutiny.
Stop trusting timesheets. Start verifying them.
Try BetterFlow free for 30 days — AI verification cross-references timesheets with GitHub commits and Jira tickets automatically. Built by BetterQA, a QA company with 50+ engineers that built and uses BetterFlow daily to verify every hour billed to clients.
Published by BetterQA, an ISO 27001 and ISO 9001 certified company with 8+ years of experience in software quality assurance. According to research by McKinsey, data-driven project management improves team productivity by up to 25%. Last updated on .
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