# Epicor Upgrade Regression Test Effort Calculator

> Source: https://netray.co/tools/epicor-upgrade-regression-test-effort-calculator
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Size the regression testing for an Epicor upgrade from test cases, failure rate, fix time and tester count.

## Category

epicor

## Erp

Epicor Kinetic

## Audience

- Epicor project manager
- Epicor developer
- QA lead

## Tool

**Kind:** calculator

**Inputs:**

#### Regression test cases

**Key:** cases

**Type:** number

**Default:** 220

**Min:** 1

**Step:** 10

**Unit:** cases

**Help:** Business scenarios plus custom BPMs, reports, dashboards and integrations.

#### Average minutes per case

**Key:** min_case

**Type:** number

**Default:** 12

**Min:** 1

**Step:** 1

**Unit:** min

#### Failure rate on first run

**Key:** fail_pct

**Type:** slider

**Default:** 8

**Min:** 0

**Max:** 40

**Step:** 1

**Unit:** %

#### Fix effort per failure

**Key:** fix_hours

**Type:** number

**Default:** 3

**Min:** 0

**Step:** 0.5

**Unit:** hours

**Help:** Diagnosis, change and unit test by a developer.

#### Retest effort relative to first run

**Key:** retest_factor

**Type:** number

**Default:** 1.5

**Min:** 1

**Step:** 0.1

**Unit:** x

**Help:** Retests include setup and checking related areas.

#### Testers

**Key:** testers

**Type:** slider

**Default:** 4

**Min:** 1

**Max:** 20

**Step:** 1

**Unit:** people

#### Productive test hours per tester per day

**Key:** hrs_day

**Type:** number

**Default:** 5

**Min:** 1

**Max:** 8

**Step:** 0.5

**Unit:** hours

**Outputs:**

#### First pass test hours

**Key:** first_hours

**Expr:** cases * min_case / 60

**Format:** hours

#### Expected failures

**Key:** fails

**Expr:** cases * fail_pct / 100

**Format:** decimal

#### Retest hours

**Key:** retest_hours

**Expr:** fails * min_case / 60 * retest_factor

**Format:** hours

#### Developer fix hours

**Key:** dev_hours

**Expr:** fails * fix_hours

**Format:** hours

**Primary:** true

#### Total test hours

**Key:** test_hours

**Expr:** first_hours + retest_hours

**Format:** hours

#### Calendar days of testing

**Key:** days

**Expr:** test_hours / (testers * hrs_day)

**Format:** days

**Primary:** true

**Chart:**

#### Testing days vs number of testers

**Type:** line

**X:** testers

**From:** 1

**To:** 20

**Steps:** 20

**Series:**

- days

**Disclaimer:** Planning estimate for one regression cycle. Real upgrades may need several cycles, environment builds and sign-off time that are not included.

## Content

**Intro:** Upgrade plans often budget for the installer and forget the testing. This calculator estimates the test hours, developer fixes and calendar days for a regression cycle from your test case count, failure rate and team size.

**Sections:**

#### How to use it

**Body:** Count the test cases you actually need: core transactions, then every customization, BPM, report, dashboard and integration. Estimate average minutes per case and a first pass failure rate from past upgrades. Add the effort to fix a failure, a retest multiplier and the number of testers with their productive hours. The tool returns total hours, developer effort and elapsed days.

**Bullets:**

- Weight cases toward customizations, which fail most often.
- Use failure rates from your own previous upgrade or patch cycles.
- Run the estimate for pessimistic and optimistic failure rates.

#### The formulas

**Body:** First pass hours are cases times minutes divided by 60. Failures are cases times the failure rate. Retest hours are failures times minutes per case, scaled by a retest factor to reflect setup and surrounding checks. Developer hours are failures times fix effort. Calendar days are total test hours divided by testers times productive hours per day.

#### Reading the chart

**Body:** Adding testers shortens elapsed days with diminishing returns in practice, because environment access, data setup and defect triage do not scale. The model is linear, so treat the chart as a lower bound. If developer hours dwarf test hours, the failure rate is the real risk, and simplifying customizations before the upgrade pays back.

#### Common mistakes

**Body:** Teams test only happy paths, skip month-end and period-close scenarios, and forget that custom reports and Excel-linked dashboards also break. Using a stale copy of data makes results meaningless. Keep a written test script per case so results can be repeated for the next cycle, and plan a second cycle for fixes.

**Cta:** Netray can help generate and run regression tests against your Epicor customizations. The calculator is free either way.

**Faq:**

#### How many test cases are typical?

**Answer:** It depends on the modules and customizations in use. Many mid-size manufacturers end up with a few hundred scenarios once they include reports and integrations. Build from your actual process list, not a generic number.

#### What failure rate should I assume?

**Answer:** Use your own history if you have it. If not, test the rate sensitivity by running a low and a high case, since heavily customized systems usually see more failures than lightly customized ones.

#### Do I need more than one cycle?

**Answer:** Usually yes. After fixing failures you retest, and a final confirmation pass before go-live is common. Multiply the estimate by the number of cycles you plan to run.

#### Can automation reduce this?

**Answer:** For stable, repeated scenarios, automated API or UI tests can cut first-pass hours in later cycles. The setup effort is real, so it pays off mainly when you upgrade or patch often.

## Search terms

epicor upgrade testing, epicor upgrade regression test plan, epicor kinetic upgrade effort, epicor customization retest, epicor upgrade project estimate, epicor uat effort, erp upgrade test cycle calculator

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