Workday Azure Integration Testing

Name: SyntraFlow Workday Azure Integration Testing
Brand: SyntraFlow
Availability: InStock

A growing number of enterprises stitch Workday into their wider landscape using Microsoft Azure's integration platform — Azure Logic Apps for workflow orchestration, Azure Data Factory for bulk data movement, and Azure Service Bus for reliable, decoupled messaging. These iPaaS pipelines carry worker, payroll, financial and organisational data between Workday and downstream systems, and every layer of the pipeline — the Workday API call, the transformation, the message queue, the retry policy — is a place a defect can hide. Testing a Workday Azure integration means validating the whole path end to end, not just one connector. SyntraFlow's AI-powered platform is designed to test these iPaaS pipelines the way real production runs behave: asserting the data Workday emits and receives, exercising the failure and retry paths that Azure's messaging model introduces, and re-running the suite against every Workday feature release so a schema or configuration change cannot quietly break a Logic App or a Data Factory pipeline.

Many moving parts

A single flow spans a Workday API, a Logic App, mappings, Service Bus queues and a downstream endpoint — each an independent point of failure.

Asynchronous by design

Service Bus decouples sender from receiver, so ordering, duplication and dead-letter handling become testable behaviours, not assumptions.

Release fragility

Twice-yearly Workday releases and config edits can shift the payload a pipeline reads without touching a line of Azure code.

Silent partial failure

A throttled call or a message stuck in a dead-letter queue can leave a run "green" while records never arrive downstream.

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Business challenges Workday Azure integration testing addresses

Organisations standardised on Microsoft Azure often build their Workday integrations there rather than in Workday Studio or a third-party iPaaS. The Azure integration platform is attractive for good reasons: Logic Apps give a low-code way to orchestrate multi-step workflows, Data Factory moves large data sets on a schedule, Service Bus provides enterprise-grade queuing that survives outages, and it all runs inside the same subscription, identity model and monitoring stack the rest of the estate already uses. That convenience creates a specific testing challenge — the integration logic now lives outside Workday, so Workday's own tooling cannot see it, and Azure's tooling does not understand Workday's business meaning.

The first challenge is the length of the path. A Workday-to-Azure flow is rarely a single hop. A Logic App might authenticate to Workday via OAuth, call a REST resource or a SOAP web service, map the response, drop a message onto a Service Bus queue, and let a second Logic App or an Azure Function pick it up and deliver it onward. Every hand-off is a place where a field can be dropped, a type coerced wrongly, or a message lost. Validating only the Workday API call, or only the downstream arrival, leaves the middle untested — and the middle is where iPaaS integrations most often fail.

The second challenge is asynchrony. Service Bus deliberately decouples the producer from the consumer, which makes the pipeline resilient but also makes its correctness harder to reason about. Message ordering, duplicate delivery, at-least-once semantics, lock duration, and dead-letter routing are all behaviours that must be tested explicitly, because "the message was sent" is not the same as "the record arrived once, in order, and was processed." A pipeline that looks healthy in a demo can lose or double records under real load.

The third challenge is change. Workday ships two feature releases each year plus near-weekly service updates, and business configuration — new fields, new calculated values, new organisations — changes constantly. Any of these can alter the payload a Logic App or Data Factory pipeline reads, so an Azure integration that worked last month can silently produce wrong output this month with no change to the Azure side at all. Regression-testing every Workday-Azure pipeline against the preview tenant before each release is the only reliable defence, and doing it by hand across dozens of Logic Apps does not scale.

How Workday–Azure integrations are built

To test an Azure integration well you have to understand each Azure service it uses and how each one touches Workday. Unlike a self-contained Studio integration, an Azure pipeline is assembled from several distinct services, and a defect can live in any of them. Understanding the parts tells you exactly what a test must assert.

Azure Logic Apps — workflow orchestration

Logic Apps are the low-code orchestration layer: a trigger fires (a schedule, an HTTP request, a message arriving on a queue), and a sequence of actions runs — call Workday, transform the response, branch on a condition, call the next system. Because the logic is expressed as connectors, control-flow actions and inline expressions, testing must treat a Logic App as a flow with branches: a run that takes the wrong path, loops incorrectly, or mishandles an empty response can complete "successfully" and still be wrong. Workday is usually reached from a Logic App through the generic HTTP connector or a custom connector wrapping Workday Web Services, so the API contract and its auth are part of what a Logic App test covers.

Azure Data Factory — bulk data movement

Data Factory is the batch and ELT workhorse: pipelines with copy activities, data flows and mapping logic that extract sizeable data sets from Workday — worker populations, financial extracts, reporting data — and land them in a data lake, a SQL database or a warehouse. Testing here centres on the mapping and the volume: field-level transformations must be asserted for correctness, and the pipeline must be validated at production-representative row counts, where pagination, throttling and timeout behaviour that never appears in a small sample suddenly matters.

Azure Service Bus — reliable messaging

Service Bus provides queues and publish-subscribe topics that decouple the systems either side of Workday. It is where the pipeline's resilience lives — and where its most subtle defects hide. Messages have sessions and ordering guarantees, delivery is typically at-least-once (so consumers must be idempotent), locks can expire and cause redelivery, and messages that repeatedly fail land in a dead-letter queue. Each of these is a behaviour a test must exercise deliberately: correct ordering, safe handling of a duplicate, and a defined path for a dead-lettered message rather than a silently lost record.

Identity, connectivity and monitoring

A Workday-Azure integration authenticates to Workday — usually OAuth 2.0 against a Workday API client, or an Integration System User (ISU) for SOAP — while Azure-side services authenticate to each other through Microsoft Entra ID managed identities and store secrets in Azure Key Vault. Connectivity may run over public endpoints, private endpoints or an on-prem data gateway. Runs are observed through Azure Monitor and Application Insights. These are among the most common points of production failure — an expired client secret, a rotated key, a firewall or private-endpoint change — and they must be tested deliberately rather than assumed. Note that this page addresses the integration data path; the separate concern of Workday sign-on through Entra ID is covered in Azure AD integration testing. Microsoft's own platform documentation at Microsoft Learn describes each of these services in depth.

Typical Workday–Azure integration scenarios

Azure integrations with Workday tend to cluster around a handful of high-value patterns. Knowing which pattern you are testing focuses effort on the failure modes that matter for that data.

▸Worker and organisation sync. A Logic App or Data Factory pipeline pulls new hires, job changes and terminations from Workday and provisions them into Active Directory, a service desk, or a line-of-business app — where effective dating and referential integrity must be preserved. Coordinate with Core HCM and hire-employee testing.
▸Payroll and financial extracts. Data Factory pipelines that extract validated payroll results or Financials postings into a warehouse or a downstream ERP, where totals must reconcile and mappings must be exact.
▸Event-driven messaging. Service Bus topics that fan a Workday business event out to several subscribers — a badge system, a provisioning flow, an analytics pipeline — where each subscriber must receive the event once and in order.
▸Reporting and analytics feeds. Scheduled extracts of Workday report or Prism data into a lakehouse or Power BI model, where completeness, freshness and row counts must be validated every run.
▸Inbound loads into Workday. Azure pipelines that assemble data from upstream systems and push it into Workday via the API or by staging a file for an EIB, where validation, error handling and idempotency on the Workday side are critical.

Common Workday–Azure integration failures

Effective testing is organised around the ways iPaaS pipelines actually break. The table below maps the most frequent failure modes to what a test should assert to catch them before production.

Failure mode	How it happens	What a test should assert
Mapping / transformation defect	A Logic App expression or Data Factory mapping drops a field, coerces a type, or formats a date or amount wrongly.	Payload received downstream matches an expected result for a known Workday input, across many data profiles.
Auth / token failure	An expired OAuth client secret, a rotated Key Vault secret, or a mis-scoped ISU blocks the Workday call.	Token acquisition succeeds, and an auth failure raises a clear alert rather than an empty or partial run.
API throttling / pagination	Workday rate limits or paginates a large response and the pipeline reads only the first page.	All pages are retrieved, throttling triggers back-off and retry, and the full population is delivered.
Message loss / dead-letter	A Service Bus message repeatedly fails processing and lands in the dead-letter queue unnoticed.	Dead-lettered messages are detected, surfaced and re-processable; no record is silently lost.
Duplicate / ordering	At-least-once delivery re-delivers a message, or sessions are missing and events arrive out of order.	Consumers are idempotent; a duplicate produces no double effect and ordering holds where required.
Volume / timeout	A Data Factory copy or Logic App exceeds a timeout or gateway limit at production row counts.	Runs complete within the window at production volume; no truncation or partial delivery under load.
Release regression	A Workday feature release or config change shifts the schema or values the pipeline reads.	Full suite re-runs green against the preview tenant before production cutover.

Workday Azure integration testing strategy

A sound strategy tests an Azure integration at three levels: the individual component in isolation, the pipeline end to end, and the integration in the context of a Workday release. Each level uses different scenario types, and together they cover the failure modes above. The layering matters — a mapping bug is cheapest to catch on a single component, while an ordering or dead-letter defect only shows up when the whole pipeline runs under realistic conditions.

Component-level: the Workday call and the mapping

Start where the data originates. Assert that the Workday REST or SOAP call returns the expected payload for a controlled input, that OAuth token acquisition works and handles expiry, and that pagination retrieves every page. Then assert the mapping deterministically: a given Workday response should always produce a specific downstream shape, so data-driven testing pays off — dozens of worker or transaction profiles, single and multi-currency, with and without optional fields, run through the same Logic App or Data Factory mapping and each expected output is checked.

Pipeline-level: end to end through Service Bus

Run the deployed pipeline against a test tenant with real (masked) data, let a message travel from the Workday call through the queue or topic to the downstream endpoint, and validate what actually arrives — record counts, control totals, ordering and content. This is the level that catches routing, duplication, dead-letter and connectivity defects no component test can see, and it is where the asynchronous nature of Service Bus is exercised rather than assumed.

Release-level: regression against preview

Ahead of each of Workday's two annual feature releases, run the entire Azure integration suite against the preview tenant and compare output to a trusted baseline. Any difference is triaged as an intended change or a regression before production. This discipline turns a twice-yearly scramble into a routine, evidence-backed sign-off, and it ties directly into your broader release testing and integration impact program.

Across those levels, the scenario types below give a Workday-Azure pipeline meaningful coverage.

Scenario type	What it covers	Example
Positive / happy path	Valid data flows end to end and arrives correct.	A new hire in Workday provisions correctly downstream via the pipeline.
Negative / invalid	Bad or malformed data is rejected or handled per spec.	A worker with a missing required field is reported, not silently skipped.
Boundary	Edges of length, value, date, precision and page size.	Maximum field length, zero-amount row, page-boundary record, period-end date.
Exception / fault	Endpoint, auth or dependency failure is handled and surfaced.	Workday throttles the call; the pipeline backs off, retries and alerts.
Messaging semantics	Ordering, duplication, sessions and dead-letter routing.	A redelivered message is idempotent; a poison message dead-letters and alerts.
Integration	End-to-end flow across Workday, Azure services and endpoint.	Downstream record set reconciles to the source Workday population.
Security	OAuth/ISU scope, Key Vault secrets and transport security.	Integration reads exactly its authorised data; secrets are current and encrypted.
Performance	Throughput and stability at production volume.	Full-population Data Factory copy completes within the window, no truncation.
Regression	Behaviour unchanged after a release or config edit.	Preview-tenant output matches the pre-release baseline.

Security testing considerations

Azure pipelines concentrate both sensitive Workday data and the credentials that unlock it, so security is a first-class test dimension, not an afterthought. Confirm the Workday API client or ISU runs under least privilege — scoped to exactly the domains the integration needs and no more — and that a scope change is caught by an expected-population assertion. Validate that secrets live in Azure Key Vault rather than pipeline definitions, that managed identities are used between Azure services where possible, and that transport is encrypted end to end. Treat token expiry, secret rotation and private-endpoint or firewall changes as test cases with owners. These are testing considerations to align with your security and audit functions, drawing on your own policies and public guidance such as the OWASP project; they are not compliance guarantees a testing platform can make. Coordinate this work with your security testing program.

Performance testing considerations

A pipeline that passes at ten records can fail at ten thousand. Test at production-representative volume to expose throttling, pagination limits, timeouts and truncation, and confirm the pipeline handles Workday's rate limits gracefully with back-off rather than dropping records. Measure the run against the window it must fit inside — an extract that misses a downstream cut-off is a business failure even if every value is correct — and validate concurrent-run behaviour and Service Bus throughput where multiple pipelines touch the same data.

Error handling and monitoring

The most dangerous iPaaS defect is a failure that looks like a success. Testing must prove the pipeline's fault paths work: a throttled Workday call triggers retry with back-off, a poison message dead-letters and raises an alert rather than vanishing, and a partial run does not present as complete. Validate the retry policies on Logic App actions and Data Factory activities, the dead-letter handling on every queue and subscription, and the telemetry surfaced through Azure Monitor and Application Insights. Confirm operators can tell, from monitoring alone, whether a run truly succeeded — and that reconciliation controls (record counts, control totals) accompany every run so a downstream team can independently verify completeness.

Enterprise best practices for Workday Azure integration testing

These recommendations turn ad-hoc pipeline validation into a repeatable engineering discipline.

Test end to end, not connector by connector. A green Workday call and a green downstream write do not prove the middle worked. Assert what actually arrives at the destination against what left Workday.
Assert the payload, not just the run status. A Logic App that finishes is not a Logic App that is correct. Compare the delivered data against an expected result, not a success flag.
Drive breadth with data. Run many Workday data profiles — salaried, hourly, multi-currency, edge cases — through the same pipeline rather than one convenient record.
Test messaging semantics explicitly. Prove ordering where it matters, prove consumers are idempotent under duplicate delivery, and prove dead-lettered messages are surfaced and recoverable.
Reconcile with control totals. Every run should carry record counts and control totals that reconcile to the Workday source, making silent record loss detectable.
Exercise the failure paths. Simulate a throttled API, an expired secret and a poison message, and assert the pipeline backs off, retries, alerts and never delivers bad or partial data.
Test at production volume. Validate pagination, throughput, timeouts and the run window with a realistic population, not a handful of records.
Pin security scope with tests. Assert expected population counts so an API-client or ISU scope change is caught, and confirm secrets are in Key Vault and current on every run.
Regression-test every release. Run the full Azure suite against the preview tenant ahead of each feature release and after relevant weekly service updates.
Use masked, realistic data. Test with production-shaped data that is masked or synthetic, preserving referential integrity while protecting sensitive information — see test data management.
Version pipelines and tests together. Keep Logic App, Data Factory and Service Bus definitions and their tests under change control so a change and its validation move as one unit.
Make tests the documentation. Well-named tests become the executable specification of a pipeline that an engineer who may have moved on originally built.
Coordinate with API and EIB coverage. Azure pipelines rarely stand alone; align them with API and EIB testing for a complete integration picture.
Own the monitoring assertions. Confirm the alerts operators rely on in Azure Monitor and Application Insights actually fire, so a real failure is never mistaken for a success.

Put your Workday–Azure pipelines under continuous test

See how SyntraFlow is designed to assert end-to-end payloads, exercise Service Bus failure paths, and regression-test every Azure integration against each Workday release.

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AI automation for Workday Azure integration testing

Manual iPaaS testing does not keep pace with the number of Logic Apps and Data Factory pipelines an enterprise runs, the breadth of Workday data profiles they must handle, or the twice-yearly release cadence. AI-assisted test automation is designed to close that gap, and it applies to Azure pipelines in several concrete ways.

▸Test generation. AI is designed to propose scenario coverage from a pipeline's Workday inputs, mappings and messaging model — positive, negative, boundary, exception and messaging-semantics cases — so gaps such as an untested dead-letter path are less likely to be missed.
▸Self-healing. When a Workday release or configuration change shifts a field or schema, self-healing is designed to adapt the affected assertions instead of failing the whole suite, cutting the maintenance that usually kills integration test coverage.
▸Impact analysis. By reading Workday release notes and tenant configuration, impact analysis is designed to point testers at the specific Logic Apps and pipelines a change is likely to affect, focusing regression where the risk actually is.
▸Risk-based execution. Rather than run everything blindly, risk-based execution is designed to prioritise the highest-consequence pipelines — payroll and financial extracts — so the most important coverage runs first under deadline.
▸Reusable assets. Workday API assertions, mapping checks, control-total reconciliations and messaging-semantics scenarios become reusable building blocks shared across pipelines, so a new Azure integration inherits proven coverage.

These capabilities are available for demonstration and proof-of-concept validation against your own Azure integrations; some deeper Azure-specific behaviours remain on the active roadmap, so confirm current scope during an assessment.

How SyntraFlow helps with Workday Azure integration testing

SyntraFlow is an AI-powered enterprise testing platform, Oracle-native and expanding to Workday, Salesforce and SAP. Its architecture is designed to test Azure integrations the way this page describes: assert the Workday REST or SOAP payload, validate Logic App and Data Factory mappings across many data profiles, run the pipeline end to end through Service Bus and reconcile what arrives downstream against the source, and exercise the retry, duplicate and dead-letter paths that manual review skips. It is designed to complement the Azure integration platform and Workday's own tooling, never to replace them — Logic Apps, Data Factory and Service Bus remain where the integration is built and run; SyntraFlow is designed to provide the automated, repeatable validation layer around them.

A distinctive strength is cross-application coverage. Azure pipelines rarely end at Workday's boundary — they feed data warehouses, ERP systems, provisioning targets and analytics platforms. SyntraFlow's ability to assert both the Workday side and the destination side of an integration, and to span Workday and systems such as Oracle or SAP in a single scenario, is a genuine differentiator for the enterprises whose most critical data crosses those lines.

The comparison below contrasts a typical manual approach with SyntraFlow's designed AI-assisted approach.

Dimension	Manual Azure testing	SyntraFlow (designed)
Scope of validation	Check the Workday call and the downstream write separately.	Assert the whole path end to end, including the Service Bus middle.
Payload validation	Eyeball a sample once, then trust it.	Assert delivered payload against a baseline, every run.
Data breadth	One or two convenient records.	Data-driven across many worker and transaction profiles.
Messaging semantics	Ordering, duplicates and dead-letters rarely tested.	Idempotency, ordering and dead-letter paths deliberately exercised.
Release regression	Manual re-check under deadline, often sampled.	Full suite re-run against preview with impact analysis.
Maintenance	Tests break and get abandoned after a schema shift.	Self-healing adapts assertions to shifting payloads.
Cross-application	Workday and destination tested separately, if at all.	Single scenario asserts both sides, spanning Oracle/SAP where relevant.

Compliance dimensions — data privacy in test payloads, segregation of duties around who can change a pipeline, audit evidence for each run — are considerations to confirm with your compliance, security and audit functions; SyntraFlow is designed to support those functions with repeatable evidence, not to substitute for their judgement. The most reliable way to assess fit is a proof-of-concept against your own Azure integrations. You can explore the wider Workday testing program, the integration testing hub, and the practitioner community at Workday Community.

Frequently asked questions

What is Workday Azure integration testing?

Workday Azure integration testing is the practice of validating iPaaS pipelines that connect Workday to other systems using Microsoft Azure services — Logic Apps for orchestration, Data Factory for bulk data movement, and Service Bus for reliable messaging. It asserts that the Workday API call, the mappings, the message queue and the downstream delivery all behave correctly end to end, that failure and retry paths are safe, and that a Workday release does not silently break the pipeline.

Which Azure services are used to integrate with Workday?

The three most common are Azure Logic Apps for low-code workflow orchestration, Azure Data Factory for scheduled bulk extraction and loading, and Azure Service Bus for queue- and topic-based messaging that decouples the systems either side of Workday. Supporting services include Microsoft Entra ID for identity, Azure Key Vault for secrets, Azure Functions for custom logic, and Azure Monitor with Application Insights for observability. Each participates in the data path and needs test coverage.

How is this different from Azure AD integration testing?

Azure AD (Microsoft Entra ID) integration testing focuses on identity — single sign-on and user provisioning between Workday and Entra ID. This page covers the integration data path: Logic Apps, Data Factory and Service Bus moving business data such as workers, payroll and financial records between Workday and other systems. They are related but distinct concerns; the identity side is covered separately in Azure AD integration testing, and the two should be coordinated in a complete program.

How do you test a Workday Logic App?

Treat the Logic App as a flow with branches. Assert that the Workday call authenticates and returns the expected payload, that each action and condition behaves correctly for valid, invalid and boundary inputs, and that the mapping produces the exact downstream shape for a controlled Workday input. Then run it end to end and validate what arrives at the destination. Data-driven testing across many Workday profiles catches mapping defects that a single sample would miss.

How do you test Azure Data Factory pipelines with Workday?

Assert the copy and mapping logic deterministically — a given Workday extract should always land in a specific downstream shape — and validate the pipeline at production-representative volume. Volume is where Data Factory defects surface: pagination of large Workday responses, API throttling, timeouts and gateway limits. Confirm all pages are retrieved, throttling triggers back-off and retry, row counts and control totals reconcile to the source, and the run completes within its scheduled window.

Why does Azure Service Bus need special test attention?

Service Bus deliberately decouples the sender from the receiver, which makes a pipeline resilient but changes what "correct" means. Delivery is typically at-least-once, so a message can be redelivered and consumers must be idempotent; ordering only holds when sessions are used; and messages that repeatedly fail land in a dead-letter queue. Each of these must be tested explicitly, because a message being sent does not prove it arrived once, in order, and was processed.

What are the most common Workday–Azure integration failures?

Mapping and transformation defects, auth failures from an expired OAuth secret or rotated Key Vault key, API throttling and pagination that reads only part of a population, messages lost to a dead-letter queue, duplicate or out-of-order delivery, volume timeouts, and release regressions where a Workday change shifts the payload. The most dangerous class is a failure that looks like a success — a partial run or a dead-lettered record that never surfaces — which is why control totals and reconciliation are essential.

Why do Azure integrations break after a Workday release?

Workday ships two feature releases a year plus near-weekly service updates, and business configuration changes constantly. Any of these can change the schema or values a Logic App or Data Factory pipeline reads, so an integration can produce subtly wrong output with no change to the Azure side at all. Running the full Azure suite against the preview tenant before each release, and comparing output to a trusted baseline, is the reliable way to catch these regressions early.

How do you test authentication and security in a Workday–Azure pipeline?

Confirm the Workday API client or Integration System User runs under least privilege — scoped to exactly the domains needed — and assert an expected population count so a scope change is caught. Validate that secrets live in Azure Key Vault rather than pipeline definitions, that managed identities are used between Azure services, that transport is encrypted, and that token expiry and secret rotation are handled. These are testing considerations to align with your security and audit functions using your own policies; they are not compliance guarantees a testing platform can make.

How do you performance-test a Workday–Azure integration?

Run it at production-representative volume, because a pipeline that passes at ten records can fail at ten thousand through throttling, pagination limits, timeouts or truncation. Confirm the pipeline handles Workday rate limits with back-off rather than dropping records, measure the run against the window it must fit inside — an extract that misses a downstream cut-off is a business failure even if every value is correct — and validate concurrent-run behaviour and Service Bus throughput where relevant.

Can Workday Azure integration testing be automated?

Yes. The Workday API payload, mapping output, control-total reconciliation, connectivity checks and messaging semantics are all deterministic and highly automatable. SyntraFlow's architecture is designed to generate scenario coverage, run data-driven assertions, self-heal when payloads shift, and prioritise the highest-risk pipelines under deadline. These capabilities are available for demonstration and proof-of-concept validation; some deeper Azure-specific behaviours remain on the active roadmap, so confirm scope for your pipelines during an assessment.

Does SyntraFlow replace Azure Logic Apps or Data Factory?

No. Azure Logic Apps, Data Factory and Service Bus remain where the integration is built and run, and SyntraFlow is designed to complement them, never replace them. SyntraFlow provides the automated, repeatable validation layer around Azure pipelines — asserting end-to-end payloads, exercising failure and dead-letter paths, and regression-testing every Workday release. It works alongside your Azure platform, Workday's delivered tooling and preview tenant, and your existing integration practices rather than substituting for any of them.

Can SyntraFlow test integrations that span Workday and other systems?

Yes, and cross-application coverage is a genuine differentiator. Azure pipelines rarely end at Workday's boundary — they feed data warehouses, ERP systems, provisioning targets and analytics platforms. SyntraFlow's architecture is designed to assert both the Workday side and the destination side of an integration in a single scenario, and to span Workday and systems such as Oracle or SAP, so the enterprises whose most critical data crosses those lines can validate the whole flow, not just one end.

How do we evaluate SyntraFlow for Workday Azure integration testing?

The most reliable approach is a proof-of-concept against your own Azure integrations. Assess end-to-end payload assertion through Service Bus, data-driven breadth, mapping validation, failure- and dead-letter-path simulation, security-scope checks, self-healing accuracy, and preview-tenant regression with impact analysis — across your highest-consequence payroll, financial and provisioning pipelines. You can schedule a Workday testing assessment or talk to an expert, and review the wider integration testing program before deciding.