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API Testing Services

Your team changes one service. Its tests pass. Another service that depends on it breaks anyway.

Products now run on services that change on their own schedules, some owned by other teams and some by partners. The break shows up after release, and a partner or a customer often finds it first.

QASource finds what a change can break before it ships. Our engineers work out which services depend on the one that changed, test those connections, and tell you in writing what is still at risk. The QASource Intelligent Platform (QASIP) generates the contract tests, and they run in your pipeline on every change.

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Tell us what's going wrong

The Contract Nobody Owns

Most products now run on services that call each other. Some belong to other teams in your company, and some belong to partners outside it. Each team tests its own service, and each suite passes.

What no one tests is the connection between them: the fields, formats, and responses one service expects from another. That expectation is the contract, and it belongs to no single team. A change that breaks it doesn't fail a build. It shows up after release, in a service that wasn't part of the change.

What our API testing covers

Engineers who know your services work out which services and partners a change can reach, test those connections before release, and write down what was tested, what was not, and the risk that remains.

Generated, Then Checked.
QASIP generates the contract tests for the changed surface of each release. An engineer checks each one before it enters your suite.

In Your Pipeline. The tests run on every change in Jenkins, GitLab CI, CircleCI, or Azure DevOps, and every test is committed to your repository.

Including What You Don't Own. REST, SOAP, and GraphQL, and the partner and third-party services your integrations depend on.

Case Study · A conversation analytics platform

Found in the Connections, Not in the Code That Changed

Each of these would have reached a partner or a customer. The service that changed had passed its own tests. Our engineers found them in the connections.

Defect 01

Valid Requests Rejected by a Category Rule

Where the engineer looked, and why

Category application through tags was not working as expected.
 The engineer submitted a chat request using existing tags and compared the failure against the category definition. The API returned HTTP 400, reporting matching, active categories as not found.

What it would have reached

Any customer or partner integration using tag-based categorization would have had its requests rejected at submission, and nothing downstream would have received the results.

Defect 02

A Sync Job Quietly Deleting Live Configuration

Where the engineer looked, and why

A configuration-sync fix was deployed for validation. The engineer ran the full plan for the sync path rather than the fix alone. One case failed: active categories were being marked as deleted in the configuration database after the sync ran.

What it would have reached

Every account with active categories would have lost its categorization data the next time the sync job executed.

Defect 03

Strict Validation Blocking Valid Uploads

Where the engineer looked, and why

A new feature added a configuration node. The engineer tested with the feature switched off, the state most users would be in. The frontend sent an empty block for the node, and the ingress layer rejected otherwise valid media submissions. The fix was verified across 5,100+ API regression test cases and 426 frontend automation cases.

What it would have reached

Dependent frontend workflows broken for anyone submitting with the new feature disabled.

The changed service passed every time. The break was in what depended on it.

How We Test the Connections Between Your Services

Before a change ships, our engineers work out which services it can reach and which of those connections to test first. Three parts, in this order.

01

Decide the Order

Five signals decide which connections get tested first. What we don't reach is written down as untested. Each signal is shown with what it meant on a real release.

  1. Signal 1

    Who Calls It

    The services, app versions, and partners that depend on the one that changed, including the ones your team doesn't own.

    In Practice

    A query-handling change that preserved trailing whitespace was traced to the one enterprise customer whose indexed media documents it would affect, before anything was tested.

  2. Signal 2

    What Changed in the Contract

    The fields, formats, and responses that callers rely on.

    In Practice

    A new configuration node was checked in its absent, empty, and populated states, because the callers would send all three.

  3. Signal 3

    Where It Broke Before

    The connections with a history of breaks and the fixes that didn't hold.

    In Practice

    A sync path that had misbehaved before got the full test plan again, not just the fix. One case failed.

  4. Signal 4

    What a Break Would Cost You

    Payments, permissions, personal data, and anything a partner or customer would see first.

    In Practice

    Role-based access to a new feature was tested across every role, because the wrong role seeing the wrong data is the first thing a customer reports.

  5. Signal 5

    What Counts as Safe to Release

    The definition we agree on with you before the first release we test.

    In Practice

    Performance testing was recorded as not required, with the reason: the change didn't touch the engine it would have measured. Written down, not assumed.

02

Test Both Sides of Each Contract

When the other side of an API belongs to another team or a partner, we test the agreement between the two without testing the whole system end to end.

The Consumer Side

  • Request format and required fields
  • Headers and authentication
  • Expected response structure
  • How errors are handled
the contract

The Provider Side

  • Accepts valid consumer requests
  • Responses match the agreed contract
  • Status codes, schemas, and error responses
  • Backward compatibility and version changes

Contract verification runs in CI/CD on every change, and contract changes are coordinated with the team or partner that owns the other side.

03

Run the Checks on Every Change

Across REST, SOAP, and GraphQL APIs, in your pipeline.

Contracts and Schemas Each change is checked against the contracts and schemas that the services calling it rely on.

Services Your Team Doesn't Own Partner and third-party services, validated against the contracts and schemas your integrations depend on.

Services That Change Independently Contract tests for services that change on their own schedules, triggered by your pipeline.

Drift Between Releases When an API drifts, the tests that cover it are updated and verified again.

Performance and Load Your APIs under real release traffic profiles, in environments close enough to production for the result to mean something.

Security in the Release Gate Security testing on your APIs, built into the release gate rather than run as an annual event.

Tools: Postman for on-demand checks, to run a query, inspect headers, and verify a response immediately. Java with Jersey and Jackson for automation, built into your framework and run from Jenkins, GitLab CI, CircleCI, or Azure DevOps.

Why Clients Stay With Us

Founded in 2002 and still founder-owned. We grew by referral, and most of the engineers who test our clients’ products have been with us for years. So the engineer testing your next release is usually the one who tested the last one.

"QASource has quickly found issues within four weeks of starting the project, proving their reliability."
Executive at a software company,on Clutch
"Reduced bugs in production."
Senior software engineering manager at a search company,on Clutch

Rated 4.8 out of 5 on Clutch (18 reviews) and 4.7 out of 5 on G2.

94%Customer retention
200+Clients
40%Of new clients come by referral
100+Engineers past their tenth year with us
1,100+People, all employees

API Testing for AI-Generated Code

A feature can pass every direct test and still fail the moment real data arrives through an integration. That is where our engineers test it.

Case Study · The conversation analytics platform

The Feature That Worked by API and Failed Through the Connector

A new feature used a large language model to categorize data for enterprise customers who monitor and evaluate agent quality. The developers and our engineers both used AI assistants in building and testing it.

Tested Directly Through the API: Passed

Called at its endpoints, the categorization handled complex edge cases and classified data with high accuracy. On that evidence it was ready.

Tested Through the Integration: Failed

Our engineers tested it the way customers would use it: real data pulled from external sources through integration connectors, with categorization requested in the connector setup. The data was never categorized.

The cause

The connector layer was stripping the categorization setting from the configuration before posting the data to the platform. No error was thrown. The pipeline looked healthy.

What it would have reached

The ingestion pipelines of the largest enterprise customers and partners who rely on automated categorization for agent quality evaluation. Found before release.

When developers and testers both use AI assistants, where a defect came from stops mattering. Whether the customer's path works end to end is what matters.

AI coding assistants let your developers ship more change in every release, and more of those changes reach an API that other services depend on. Our engineers check the connections a change can reach, starting with the ones that carry payments, permissions, and personal data. Knowing where a change can reach is the part that takes years on the same product.

Engineers Who Know Where a Change Travels

Case Study · The conversation analytics platform

Much of what an engineer knows about your services isn't written down. It builds up release after release: which service calls which, which app versions are still in use, and which partner relies on a field nobody documented.

An engineer new to a product tests the service that changed. On this product a platform change reaches the UI, then the database, then the BI dashboards. An engineer who doesn't know that chain can't test the impact on the services downstream.

Our lead on the product can anticipate what the end user needs from a feature and plan the test strategy around the upstream and downstream services it touches. That takes years on the same product, and we keep the people, so the knowledge stays. The three defects earlier on this page were found that way, in connections the changed service’s own tests never reached.

The value is in their judgment, and it grows with every change they trace through your services.

Case Study · How a service change moves through that team

  1. 01Engineering defines the scope: service versions, impact assessment, test evidence, deployment steps, monitoring, and rollback.
  2. 02The development team reviews and approves the technical documentation, deployment configuration, and rollback plan.
  3. 03Developers and our engineers map the service dependencies, including third-party integrations and partner configurations. Our engineers check that map and test those connections.
  4. 04Our engineers validate the integrations, dependencies, and core components and give formal sign-off.
  5. 05Release management aligns stakeholders and customer care, and confirms deployment readiness.

Contract Tests in Your Pipeline

QASIP generates contract tests for the changed surface of each release. Where documentation doesn't exist, it can build a first suite from the application itself. QASIP comes with the team, and your data is never used to train or prompt external models.

Step 1

QASIP Generates

Contract tests for what the release changed, from the application, the stories, and the existing documents.

Step 2

An Engineer Reviews

Every generated test, against the six checks below. Nothing enters your suite until it passes them.

Step 3

Committed to Your Repository

Every test is yours, in your framework, with your naming and patterns.

Step 4

Runs on Every Change

In Jenkins, GitLab CI, CircleCI, or Azure DevOps, on your branching model.

What the review in step 2 decides

  • Every Step Implemented: The endpoint, method, headers, path and query parameters, and request body the test case calls for, not an approximation of them.
  • Real Assertions: Status codes, response fields and values, schema, headers, and business rules, rather than a check that a response came back.
  • Negative and Boundary Cases: Invalid inputs, missing mandatory fields, unauthorized requests, invalid identifiers, and unsupported operations.
  • Auth and Sequencing: Valid tokens, required setup, and dependent calls in the right order.
  • Data and Secrets: Payloads and configuration sourced from the right files, with no undefined keys and no hard-coded secrets.
  • Framework and Diagnostics: The existing framework's patterns and naming, meaningful logs and assertion messages, sensible timeouts, and retries only where they can't mask a real defect.

In AI-assisted API testing, QASIP makes the tests fast to write. Our engineers answer for whether they are right.

How we generate tests and verify them →

What You Get Before Each Release

Every release candidate gets a written finding, measured against the definition of "safe to release" we agree on with you before the first release we test. For an API change, it covers every service and partner the change can reach. Here is what it covers, and a real one.

The Finding Covers

  • Every Service It Reaches: Not only the code that changed, but the services and partners the change can affect.
  • What Was Validated: Functional, regression, and integration testing by module, automation results, logs checked, rollback tested.
  • What Was Not Validated: And the reason, written down rather than assumed.
  • Risk That Remains: The open issues, their impact, and the mitigation.
  • Against the Agreed Criteria: Whether the definition of "safe to release" was met.
  • The Recommendation: Go, no-go, or go with documented conditions, with the reasons.

A real release finding

Release Finding

Conversation analytics platform · Three-service release

Go

Release

Three services released together.

What the Change Reaches

Access to a new LLM categorization feature by user role and ingestion of that feature's data into the analytics database.

What Was Validated

Unit tests on all three services, 100% passing. Functional and regression testing on every module, with a test plan written for the new feature's access and behavior. A feature checklist covering login and logout, password reset, email activation, PCI compliance, search, encryption, and the warehouse sync jobs. The full automated regression suite on staging, 5,100+ test cases passed, with service logs checked for unknown exceptions. Rollback tested by deploying the versions currently in production.

What Was Not Validated

Performance. The change didn't touch the speech engine the performance tests measure, so they were recorded as not required for that reason.

Risk That Remains

None open. The integration blockers found during testing, including database ingestion, schema query errors, and categorization defects, were resolved and reverified.

Against "Safe to Release"

Met. All exit criteria for staging were met, and the new feature was validated end to end.

Go/No-Go Call

Go, recommended by our QA lead. Release management finalized it, with all stakeholders signing off. Deployment approved for staging and production.

When a Finding Held a Release

During final regression on staging, every core use of a new categorization feature worked except one: categories applied through tags were suggested correctly and never written. Our finding named the cause, a schema mismatch between the gateway's payload and the tagging service's expectations, with reproducible steps.

Release management delayed the release. The fix was built, deployed to staging, and verified against edge-case payloads, and the feature shipped with no customer-facing issues.

Where API Testing Fits in the Wider Engagement

Dedicated QA Teams

The whole quality function: a team we hire, ramp, and run, with API and integration testers as one role on it.

The team, built for you → Test Automation Services

The wider suite, built in your framework and committed to your repository.

The wider suite → Mobile App Testing

The app versions that call your APIs, tested on real devices, including the older versions still in use.

The apps that call your APIs →

Tell Us What Your Services Look Like

Which services you run, who owns each one, how often each one changes, and who calls it. We reply within one business day. Or start with a scoped assessment: two to three weeks of our engineers in your codebase, a verified sample suite for one of your services, and a written finding you keep either way.

What Buyers Ask Us

Why do our services pass their own tests and still break each other in production?

Each suite checks its own service. The break sits in the contract between services, and that contract changes when either side changes. We test those contracts on every change, including services your own team doesn't own.

How do we know an API change is safe to release?

We agree with you on what "safe to release" means before the first release we test. Every release candidate then gets a written finding: what was tested, what was not, and the risk that remains, across every service the change can reach. Our QA lead on your product gives the recommendation, and you make the call.

How can we test integrations with APIs our team does not own?

We validate the contracts and schemas your integrations depend on, including partner and third-party services. On the consumer side we check the request format, required fields, headers, authentication, and error handling. On the provider side we check that valid requests get responses matching the agreed contract. Those checks run in your pipeline on every change.

How do you use AI in API testing, and who makes sure the tests are right?

The QASource Intelligent Platform (QASIP) generates the tests, and one of our engineers reviews each one before it enters your suite: every step of the test case implemented, real assertions on status, body, schema, and business rules, negative and boundary cases, correct authentication and sequencing, and no hard-coded secrets. QASIP comes with the team.

How do we keep API tests current as AI-assisted development speeds up our changes?

QASIP generates tests for the changed surface of each release. When an API drifts, the tests are updated and verified again. We size the team to the amount and risk of change you produce, not to a fixed headcount.

Will the tests work with our APIs and run in our own CI/CD pipeline?

We test REST, SOAP, and GraphQL APIs. The tests run in Jenkins, GitLab CI, CircleCI, or Azure DevOps, triggered by your pipeline on your branching model. Every test is committed to your repository and stays yours.

Which tools do you use for API testing?

For on-demand checks, Postman, to run a query, inspect headers, and verify a response immediately. For automation, Java with Jersey for request handling and Jackson for JSON serialization, built into your framework. Other stacks on request.

What should we ask an API testing company before we hire one?

Ask for comparable engagements, a sample release finding, who answers for the release, and who owns the tests. At QASource, we show comparable engagements, and where we haven't done something like it, we say so.

How can we see an API testing service provider's work before we commit?

Start with a scoped assessment. In two to three weeks, our engineers work in your codebase and produce a generated-and-verified sample suite for one of your services, with a coverage-against-change baseline. You keep the written finding whether or not you hire us.

How is API testing priced?

API testing usually runs as part of a dedicated team, billed monthly. See what a team costs →

Ask us the one that isn't here →

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