Friday, 3 April 2020

What are the challenges you see with third party inbound and outbound testing when using IBM MQ queues?

"What are the challenges you see with third party inbound and outbound testing when using IBM MQ queues?" - Software tester working for a multinational enterprise.

Sometimes third-party systems can cause issues when testing your enterprise systems, for example:
  • The third-party system is not available 24/7 for testing, you need to schedule time on test environments as it is shared between many teams which result in the lower time to market
  • The third-party system does not allow for simulating error responses
  • The third-party test environment might not support the load you require for running your performance tests

In this case, you can use service virtualization or mocking tool to simulate the third party system.

Here is a sample tutorial for Traffic Parrot if you are using IBM MQ via JMS APIs.

Monday, 30 March 2020

Bestow has used Traffic Parrot gRPC mocks to deliver features faster to customers

After a thorough evaluation, Bestow Inc. selected Traffic Parrot's service virtualization and API mocking tool in April 2019 for their application development needs. In this case study, we will look at the details of their infrastructure, how they applied Traffic Parrot, and what issues they have come across.
  • Traffic Parrot is specifically designed to maximize the productivity of developers writing automated tests and to enable them to mock out microservices for local development. Their lightweight platform with gRPC support was a good fit for our Docker and Go-based development environment. They provided strong support during the POC and continue to track the rapid evolution of gRPC, acting as an effective extension to our team.
    Brian Romanko, VP Engineering at Bestow


Bestow has challenged industry assumptions with a new underwriting framework that provides affordable term life insurance in minutes instead of weeks. They use Traffic Parrot to unblock teams and allow them to work independently. Bestow uses Traffic Parrot gRPC mocks in their microservice CI regression testing suites to detect breaking changes in their microservice APIs.

Technology stack: Docker, GoLang and gRPC

The core technology they rely on includes:
  • Container-based infrastructure, running Docker in Kubernetes on GCP
  • Microservices in a variety of languages, including GoLang and Python
  • Microservices communicate using gRPC APIs, with API contracts defined in Proto files
Bestow colocated teams developing a microservice to encourage close communication. gRPC APIs connect microservices, which are sometimes owned by different teams. Bestow designs gRPC APIs using Proto files, which form the contract between microservices.

Problem: teams are blocked waiting for APIs

Starting more than a year ago, Bestow developed multiple microservices in parallel. For example, the Policy Administration team provided gRPC APIs for the Enrollment team to consume. This meant that developers on the Enrollment team were sometimes waiting for the Policy Administration team to deliver their microservice APIs before they could start working.
This led to blocked timelines between teams, which meant Bestow could not deliver at the fast pace required for their customers. It was urgent for Bestow to find a solution to allow the teams to work independently.

Solution: decouple teams by using gRPC mocks

Traffic Parrot was identified as a candidate for a gRPC API mocking solution that could help unblock the timelines between the teams at Bestow. After a two week technical evaluation by VP of Engineering Brian Romanko, it was clear that the open-source alternatives did not provide adequate capabilities and Traffic Parrot was chosen to fulfil Bestow development needs.
Teams at Bestow use Traffic Parrot to develop both sides of their gRPC APIs in parallel, without having to wait for the server code to be written before a client can be tested. They run automated test suites on their CI build agents, with Traffic Parrot running in a Docker container on the agent.

Wednesday, 25 March 2020

How to choose a service virtualization tool?

Most companies like to evaluate several tools before they commit to a purchase.

Typically they evaluate the service virtualization tools based on many factors such as:
  • Cost
  • Protocols and technologies supported
  • Features
  • Performance benchmarks
  • Support level

Here are a few additional technical questions might help decide which of the tools you are looking at is best:
  • Would you like to have a central team of administrators managing the new tool?
  • What kind of footprint would you like (RAM, disk usage, ...)?
  • What kind of licensing model would work best for your use case?
  • Do you need to source control of the virtual services and deployment scripts?
  • Are you looking for a tool that fits more the microservices architecture or a monolithic architecture?
These questions are based on:

Monday, 27 January 2020

How to combine microservices and BigData?

"I've just joined a company and the architects love microservices but the developers love Big Data solutions. Do they mix? Can you point me in the direction of where I can read more about marrying the two together?" - Big Data Engineer working for a UK financial startup.

Microservice architectures are a tool to solve a specific problems an organisation might have. If you have problems that can be solved by using microservice architectures, generate 2-3 options and if the microservice route looks most promising, go for it.

Our general recommendation would be to focus on the problems you have to solve and the constraints you are working with, generate a few options and possible solutions and choose the one that seems most promising.

There will be certain scenarios where BigData and microservices will work well together, and others where they will not make sense. We would have to know more details to be of further help, please email contact us to schedule a call to discuss your specific requirements.

We recommend reading Sequoia's Guide to Microservices and Martin Fowler's blog on microservices as a good starting point to problems microservice architectures help solving.

Friday, 17 January 2020

Traffic Parrot 5.10.0 released, whats new?

We have just released version 5.10.0. Here is a list of the changes that came with the release:


  • Added support for IBM®MQ message delays in replay mode
  • Added support for specifying multiple IBM®MQ queues, using queue manager names from ibm-mq-connections.json to record using syntax in script tab in the UI:
    # This is a sample comment
    QueueManager:'Local Docker MQ 9'
    # This is a sample comment
    QueueManager:'Local Docker MQ 9'


  • Ensure IBM®MQ channel connections are always released after use

Monday, 13 January 2020

How can I distinguish mapping A (which belongs to service A) and mapping B (which belongs to service B)?

"How can I distinguish mapping A (which belongs to service A) and mapping B (which belongs to service B)?" - Intern at a global software development consultancy.

Good question!

You can use the URL or the service. For example, typically, the Users service will have a /users URL.Is this the case in your company as well? If you need more details please contact and we will be more than happy to help!

Sunday, 12 January 2020

How to use Mockito to mock grpc ServiceBlockingStub?

"How do I use Mockito to mock grpc ServiceBlockingStub to throw StatusRuntimeException with a specific status?" - Java developer

You have a few options:
Note why mocking final, in this case, might be a bad idea: Mocking final classes or methods might be a bad idea, depending on the case. The devil is in the details. In your situation, you are creating a mock of the generated code, so you are assuming how that generated code will behave in the future. gRPC and Protobuf are still rapidly evolving, so it might be risky to make those assumptions, as they might change and you won't notice because you do not check your mocks against the generated code. Hence, it's not a good idea to mock the generated code unless you really have to.

Friday, 3 January 2020

QAs testing microservices can use third party and backend mocks to speed up testing

While testing microservices you will often need to mock or stub the third-party and legacy backend systems.

Third-party and backend system mocks and stubs will help you resolve issues with:
* Setting up test data in third party and backend systems
* Simulating API and backend system error messages (for negative or sad path testing)
* Simulating protocol-specific errors (for negative or sad path testing)
* Simulating slow responses (for performance testing)

You can create HTTPgRPCJMS IBM MQ and other types of mocks with Traffic Parrot.
You can run Traffic Parrot in Docker and OpenShift and Kubernetes, which is what you need when working with microservice architectures.

For more details how to use mocks when testing microservices watch "How software testers can test microservices".

See the diagram below for example usage of Traffic Parrot when testing microservices: