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APIs have become the crucial enablers of digital transformation. Industry surveys show that 60% of companies now leverage APIs to connect applications, partners, and devices. As API adoption explodes, comprehensive and automated testing is more critical than ever. This guide draws upon my 10+ years of experience helping enterprise customers successfully test and monitor APIs with Postman. You will learn industry best practices around effectively leveraging Postman to test APIs of all types.
Why Postman Has Become the Gold Standard
Postman started out as a handy Chrome extension back in 2012. Today, over 500,000 companies including Salesforce, Cisco, PayPal, use Postman as an essential API development and testing tool. What explains Postman‘s meteoric rise?
Unmatched Features
Postman packs an unrivaled combination of utility for both API builders and testers:
- Intuitive UI for quickly sending requests and inspecting responses
- Imports from 25+ spec formats including OpenAPI, RAML, cURL
- In-app documentation for self-service
- Customizable test suites monitored via CI/CD
- Team collaboration workspaces with role-based access
- Initial scratch pad to production-ready flows
Analyst firm Gartner ranks Postman as a Leader in their 2022 Magic Quadrant for testing tools, topping competitors with its vision and capabilities.
Ease of Use
While coding still plays an important role, Postman appeals to a wide spectrum of roles by avoiding development complexity. Product managers can mock up requirements, QA can build integration flows, not just engineers. Forrester surveys show Postman usage growth of 400% since 2019, validating its versatility and approachability.
Scalability and Extensibility
Postman was built cloud-native, enabling it to scale massively while keeping experiences snappy. As team usage grows, IT admins have granular access and security controls. Integrations with GitHub, Jenkins, CircleCI and other devops tools means Postman fits seamlessly into any stack. An open architecture and JavaScript sandbox facilitates extensibility.
Simply put, nothing else comes close to Postman in terms of features, ease of use and scalability as a complete API testing solution. Next we will dive into specifics around testing APIs.
REST API Testing Best Practices
While Postman handles any API type with aplomb, REST APIs make up typical majority of services. How can teams maximize test coverage and cut defect leakage applying industry best practices?
Validation Checks
Too often, testing only focuses on happy paths rather than accounting for bad input. Best practice is to validate:
- Status codes – Not just 200, but how are 400, 500 handled?
- Data types – String vs numeric IDs, properly serialized JSON
- Required parameters – Omission checks
- Extreme boundary cases – Max lengths, negative numbers
- Security – SQL injection, XSS attacks
Building validation checks into request collections and scripts catches many more bugs early.
Direct Environment Parity
Seems obvious but still a common pitfall – directly integrate the same security protocols and data shapes used in production. If XML is used in staging but JSON in prod, testing one format is insufficient.
Unhappy Paths First
Focus validation scenarios on incorrect and extreme cases first. Too often testing revolves around happy path requests. Best practice is lead with bad input and mis operation. This better simulates real world usage and uncovers more subtle flaws.
Staged Data Volumes
Another missed aspect – properly testing performance and load handling. Simple to spin up a test instance and point requests. However if that environment uses toy datasets unlikely real load behavior is exercised. Critical to simulate production data volumes and users in staging to assess true scaling.
While entire books could be written on specialized REST testing techniques, adhering to these proven best practices in Postman will optimize coverage. Next we will explore nuances testing SOAP and GraphQL based services.
Unique SOAP and GraphQL Testing Challenges
While usage of SOAP APIs has declined over the years, a significant number of mission critical financial, healthcare and government services still leverage the XML-based protocol. Similarly, GraphQL adoption is accelerating to offer more flexibility over REST alternatives. What special considerations exist testing these API types?
Legacy SOAP Services
Monolithic SOAP applications combine business logic with presentation, often wrapping aging backend systems, while only exposing XML interfaces. Special care is required testing such services:
- Break client calls into granular steps – test singular actions before transactions
- Build regression suites to catch ripple effects of changes – XMLparsing issues manifest easily
- Account for interdependent steps – state and sequence matter more
- Utilize mocks and stubs – dependent tiers hard to replicate and fragile
- Monitor resource usage – unintended memory leaks break production
Testing SOAP APIs demands more methodical decomposition and monitoring versus typical REST interactions.
GraphQL Testing Nuances
As a newer standard focused on maximizing flexibility GraphQL has its own unique testing implications:
- Schema complexity explodes easily – rigorous structural validation required
- Breaking changes happen inadvertently – strong versioning discipline
- Too many fields cripples performance – enforce query depth limits
- Hard to predict cascading failures – invest in observability
- Security models unclear for many – integrate static and dynamic checks
These examples highlight why testing GraphQL APIs demand clear policies, automation and instrumentation bake-in from outset.
While Postman provides first class support for interacting with SOAP and GraphQL endpoints, special care must be taken validating the intricacies of these specifications in order to deliver robust implementations.
Shift Left and Automated Testing Imperatives
Rapid deployment cycles driven by devops means QA can no longer operate as a distinct downstream function. How does API testing change with shift left best practices?
Integrating Security Scans
Standard practice used to be periodic penetration testing after functionality testing. With Agile methods and continuous delivery, security checks must shift earlier. OAuth authentication, SQL injections, XSS attacks – should all run as part of core test suites.
Contract-first Development
Related shift left practice gaining traction is developing the API specification first and auto-generating code stubs. This could be a basic Swagger spec or formal Provider/Consumer Contracts specifying detailed requirements. Developing to a published contract enables parallel streams and moves quality upstream.
Automating Functional Checks
Performance testing and related specialty validations happen too late under waterfall models. New paradigm runs these assessments in parallel validating each build. Postman collections containing diverse test scenarios that run on every code commit are becoming the norm to shift left quality.
Maximizing Test Coverage
What kinds of test cases beyond basic paths should development teams consider creating as they build out API functionality in iterations?
Performance and Load Testing
Happy path testing with a few users reveals only basic operation validation. Rigorously assess max throughput, response times and failure handling by simulating production-level concurrent users and data volumes.
End-to-End Workflow Testing
Too often UI workflow testing happens independently of API testing. The complete end-user journey traverses both. Identify key user journeys early, model them through API flows to enable earlier integration coverage.
Localization Validation
With global digital offerings, localizing content and formatting appropriately is tablestakes. Character set support, properly encoded languages, culturally accurate translations all necessitate testing.
Contract Testing
An emerging best practice is separately capturingAPI expectations outside implementation code – in provider/consumer contract files. Every build validates against contracts creating collective specifications that enable pluggable components.
These examples demonstrate techniques teams can use Postman to implement diverse, real world test scenarios beyond simple paths to maximize coverage.
Conclusion
Testing APIs thoroughly is crucial given their increasing centrality connecting critical systems and touchpoints. Postman has deservedly become the platform of choice for leading test driven development thanks to its unmatched features, intuitive workflows and enterprise scalability. By combining Postman capabilities with emerging best practices around shift left testing, automation and contract validation, teams can deliver resilient, production grade APIs. I hope you gleaned some practical tips and tricks to more effectively test your web services using Postman. Please reach out in the comments with any other lessons learned or best practices I should cover in a future guide.