Oobeya · Software Engineering Intelligence Platform

Software Engineering Benchmark Report

2026 Edition

Oobeya helps organizations gather and analyze engineering data from across their toolchain, turning it into insight that supports better decisions about how software gets built and delivered. This report draws on patterns observed across 12 countries and 5 industries: Finance & Banks, Insurance, Telecom & Vendors, Retail & E-commerce, and Software & IT. The figures in this report are illustrative benchmark values intended to give you a frame of reference, not a precise ranking of any single organization.

Section 1

Engineering Performance Profile

A directional snapshot across six dimensions. Each axis is scored 0–100 — Elite 85+, Strong 70–84, Average 50–69, At risk below 50.

Strongest Dimension

DORA

78 · Strong

Watch

Engineering Health

61 · Average

This profile is a directional signal across dimensions, not a single ranking score. A strong result in one dimension cannot fully offset a weaker result in another.

Section 2

Five metrics that capture how reliably work moves from request to delivery

Five metrics that capture how reliably work moves from request to delivery, and how planning decisions show up in practice.

Reaction Time, Cycle Time, and Lead Time

Lead Time = Reaction Time + Cycle Time. Reaction time measures how long a task sits in a not-started state (to do, backlog) before work begins. Cycle time measures how long work takes once started, until it is ready for delivery. Lead time measures the full span from task creation to completion.

11.6days

Strong

16d12d8d4d0d

Finance

Insurance

Telecom

Retail

Software

Reaction Time Cycle Time

What it means

A high reaction time usually points to friction in triage or prioritization before work even starts. A high cycle time points to bottlenecks once work is underway — code review queues, unclear requirements, or dependency waits.

What to consider

Check which component is driving the total. If reaction time dominates, the fix is typically in triage and backlog process, not developer speed. If cycle time dominates, look at review turnaround and in-progress work-in-progress limits.

Predictability & Productivity

Predictability = (Planned Completed Items / Planned Items) × 100%. Productivity evaluates total output relative to plan — including extra work — so it can exceed 100%.

84%

Average

120%90%60%30%0%

Finance

Insurance

Telecom

Retail

Software

Predictability Productivity

What it means

Low predictability usually signals overcommitment or unclear scoping at planning time, rather than poor execution by the team. Very high productivity alongside low predictability often means a team is absorbing unplanned work at the cost of its original commitments.

What to consider

If predictability is low, look first at sprint scoping and estimation practices before adding process overhead elsewhere. If productivity is consistently above 100% while predictability is low, check whether high output is masking a planning problem.

Innovation Rate

The percentage of time, story points, or work items allocated to innovation work (such as new features) relative to total effort, which also includes maintenance and bug fixes.

75%

Elite

1007550250

Finance

Insurance

Telecom

Retail

Software

What it means

A low innovation rate sustained over multiple periods usually signals that maintenance burden is crowding out growth-oriented work.

What to consider

If this rate is trending down over time, it is worth quantifying how much of the maintenance load is avoidable technical debt versus genuinely necessary upkeep.

Section 3

How coding time is actually allocated

Five metrics describing how a team's coding time is actually allocated. Coding Efficiency is the sum of New Work, Refactor, and Help Others, so it is best read alongside its three components rather than in isolation.

Coding Efficiency — Allocation Wheel

The percentage of productive work, calculated as the sum of New Work, Refactor, and Help Others rates.

85.6%

Elite

New Work Refactor Help Others Rework

New Work69.0%

Refactor14.2%

Help Others2.4%

Rework16.3%

What it means

Because this metric is a composite, a mid-range score can hide an imbalance — for example, strong new-work output offsetting very low collaboration.

What to consider

Look at the three component rates individually before drawing conclusions from this number alone.

New Work / Refactor / Help Others / Rework

The four allocation components shown side-by-side across industries for direct comparison.

16.3% rework

Average

80%60%40%20%0%

Finance

Insurance

Telecom

Retail

Software & IT

New Work Refactor Help Others Rework

What it means

Very high new-work output alongside low refactor and help-others rates can mean technical debt is accumulating without being addressed. A very low Help Others rate across a team can indicate siloed ownership. Persistent high churn usually points to unclear requirements or rushed initial implementation.

What to consider

Track refactor rate alongside Reliability and Maintainability metrics. If churn is high, check whether it concentrates around specific work types before treating it as an individual or team skill issue.

Section 4

DORA

Four metrics from the DevOps Research and Assessment program, covering delivery speed and stability. Research from the DORA program has consistently found that speed and stability move together rather than trading off against each other.

Lead Time for Changes

7.6

days

Strong

Deployment Frequency

7.2

/month

Strong

Change Failure Rate

13.1

Average

Time to Restore Service

hours

Strong

Lead Time for Changes

The amount of time it takes a commit to get into production.

7.6 days

Strong

129630

Finance

Insurance

Telecom

Retail

Software & IT

What it means

Shorter lead time generally means work is broken into smaller, well-scoped chunks that move through review and deployment quickly.

What to consider

If lead time is high, check whether the bottleneck sits in code review, CI pipeline duration, or batching of changes before release.

Deployment Frequency

How often an organization successfully releases to production.

7.2/month

Strong

129630

Finance

Insurance

Telecom

Retail

Software & IT

What it means

Higher deployment frequency is generally associated with smaller, lower-risk changes and faster feedback loops.

What to consider

If frequency is low, weigh whether it reflects deliberate batching for a regulated release process or an avoidable bottleneck.

Change Failure Rate

The percentage of deployments causing a failure in production.

13.1%

Average

24181260

Finance

Insurance

Telecom

Retail

Software & IT

What it means

A rising change failure rate alongside rising deployment frequency suggests speed is being gained at the expense of stability.

What to consider

If failure rate is high and deployment frequency is also high, this is the clearest signal that release validation needs attention.

Time to Restore Service

How long it takes an organization to recover from a failure in production.

42 hours

Strong

725436180

Finance

Insurance

Telecom

Retail

Software & IT

What it means

Faster restoration generally reflects strong incident detection and a clear rollback or hotfix process.

What to consider

If restoration time is high, check whether incidents are being detected quickly before assuming the fix itself is slow.

Section 5

Quality & Security

Three metrics describing the health of the codebase itself, distinct from how quickly or how often it is changed.

Total Quality Index by industry

Reliability + Maintainability + Security stacked together.

129630

Finance

Insurance

Telecom

Retail

Software

Reliability (Bugs) Maintainability (Code Smells) Security (Vulnerabilities)

Reliability (Bugs)

2.41

Rising reliability issues alongside high rework are frequently the same underlying problem surfacing in two places.

If both rework and reliability issues are trending up together, prioritize root-cause investigation over treating them as separate problems.

Maintainability (Code Smells)

3.52

±0.5 pts

Early data suggests comparable rates when AI-assisted code goes through the same review process as other code.

Section 7

Symptoms by Industry

Beyond individual metrics, Oobeya's Symptoms module identifies recurring patterns — anti-patterns, bottlenecks, and risk indicators — drawn directly from the same underlying data already discussed in this report. Symptom prevalence and severity vary meaningfully by industry, reflecting differences in regulatory context, release cadence, and system age.

Unreviewed pull requests

High severity

What this typically signals

Review-gate friction under compliance pressure. Code is merged without a completed review cycle.

Improvement area

Enforce mandatory review policies in the VCS/CI pipeline rather than relying on convention.

High lead time for changes

High severity

What this typically signals

Changes take longer than benchmark to reach production, often due to layered approval steps.

Improvement area

Map the approval chain and identify which steps add the most elapsed time without adding proportional risk reduction.

High code review time

Moderate severity

What this typically signals

Reviews sit open longer than benchmark before being completed.

Improvement area

Set review SLAs and monitor queue age, not just review count.

Low deployment frequency

Moderate severity

What this typically signals

Releases are batched and infrequent, often a function of regulatory caution.

Improvement area

Evaluate whether smaller, more frequent releases could reduce per-release risk rather than concentrate it.

Section 8

What We Learned

Two cross-cutting findings from the patterns above.

Rework and lead time move together

Organizations with lower rework rates tend to show shorter lead time for changes. Rather than trading off against each other, these two move in the same direction — suggesting that reducing rework is one of the more reliable ways to improve delivery speed, rather than speed and quality being opposing goals.

Review time and deployment frequency are linked

Faster code review turnaround tends to correlate with higher deployment frequency. This is consistent with the idea that review queues, not coding speed itself, are often the binding constraint on how quickly an organization can ship.

Section 9

Conclusion

Engineering Performance Profile — All Industries

This report set out to do more than present metrics — the goal was to give engineering leaders a way to interpret their own numbers against industry patterns, and to understand what action, if any, a given result might warrant.

Revisiting the engineering performance profile from the start of this report: across the six dimensions covered, DORA metrics showed the strongest illustrative performance, while Engineering Health is the dimension most worth monitoring closely.

The clearest theme across this report is that speed, quality, and stability are not naturally opposing forces — they consistently move together. We recommend treating this report as a periodic reference rather than a one-time snapshot.