Difference between Exploratory testing and Ad-hoc testing not everyone knows about

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Exploratory vs Ad-hoc

We have all seen articles recommending explorative (exploratory) testing as a way to spot bugs faster and earlier in the development process. You may also have wondered if that is really the case. On the surface, ad-hoc testing offers many of the same benefits as exploratory testing. However, there is a small difference between exploratory and ad-hoc testing by definition. And knowing the difference would save you from potential miscommunication. Let’s compare.

Ad-hoc testing is an informal, free-form method of software testing, which you can perform without profound knowledge of test subject that offers the possibility of unearthing critical bugs missed by automated or regression testing.

In another words, it is far from structured. Therefore, it doesn’t have any rules, goals, documented plan or target, so the efficiency of such kind of testing really depends on the experience level of the tester. It can be missed that without any specifications, ad hoc testing is difficult to manage and often inherent lack of documentation means that any discovered bugs will be difficult to reproduce as well.

Exploratory testing, on the other hand, offers the freedom of ad hoc testing with more advantages from somewhat formal testing. While leaving the room for tester to think creatively and critically when executing their test, it is mandatory to create documentation of test cases and have a set of goals. It is a kind of testing in which the tester thoroughly asks the questions about what the product can do and how to sort out appropriate testing.

Because of that, it is structured enough to provide reliable results and it can effectively locate new difficulties in test cases.

Explorative testing is not that easy.

As previously mentioned, both ad-hoc and exploratory testing are experience based test methods that takes experience and intuition. If one has no testing expertise with the correct know how, he wouldn’t be able to test exploratively. In addition, what becomes crucial here is test case creation. Since the tester would learn as he goes about and explore, test case creation and documentation require skills ultimately to create proper and maybe better test cases that can be reused.

When do we use them?

Ad-hoc testing sounds impromptu, it is, but there is a use for it. For simple functions like registration and login, it is better to use ad-hoc testing as you don’t want to waste your time documenting and creating thought-through test cases.

Exploratory testing can be useful for more complex functions, as it is more reliable and it can be later assigned to junior testers as they are able to execute the same test cases after completed once.

#software-testing #qa #exploratory-testing #testing-techniques

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Difference between Exploratory testing and Ad-hoc testing not everyone knows about

Difference between Exploratory testing and Ad-hoc testing not everyone knows about

Image for post

Exploratory vs Ad-hoc

We have all seen articles recommending explorative (exploratory) testing as a way to spot bugs faster and earlier in the development process. You may also have wondered if that is really the case. On the surface, ad-hoc testing offers many of the same benefits as exploratory testing. However, there is a small difference between exploratory and ad-hoc testing by definition. And knowing the difference would save you from potential miscommunication. Let’s compare.

Ad-hoc testing is an informal, free-form method of software testing, which you can perform without profound knowledge of test subject that offers the possibility of unearthing critical bugs missed by automated or regression testing.

In another words, it is far from structured. Therefore, it doesn’t have any rules, goals, documented plan or target, so the efficiency of such kind of testing really depends on the experience level of the tester. It can be missed that without any specifications, ad hoc testing is difficult to manage and often inherent lack of documentation means that any discovered bugs will be difficult to reproduce as well.

Exploratory testing, on the other hand, offers the freedom of ad hoc testing with more advantages from somewhat formal testing. While leaving the room for tester to think creatively and critically when executing their test, it is mandatory to create documentation of test cases and have a set of goals. It is a kind of testing in which the tester thoroughly asks the questions about what the product can do and how to sort out appropriate testing.

Because of that, it is structured enough to provide reliable results and it can effectively locate new difficulties in test cases.

Explorative testing is not that easy.

As previously mentioned, both ad-hoc and exploratory testing are experience based test methods that takes experience and intuition. If one has no testing expertise with the correct know how, he wouldn’t be able to test exploratively. In addition, what becomes crucial here is test case creation. Since the tester would learn as he goes about and explore, test case creation and documentation require skills ultimately to create proper and maybe better test cases that can be reused.

When do we use them?

Ad-hoc testing sounds impromptu, it is, but there is a use for it. For simple functions like registration and login, it is better to use ad-hoc testing as you don’t want to waste your time documenting and creating thought-through test cases.

Exploratory testing can be useful for more complex functions, as it is more reliable and it can be later assigned to junior testers as they are able to execute the same test cases after completed once.

#software-testing #qa #exploratory-testing #testing-techniques

Tamia  Walter

Tamia Walter

1596754901

Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

  • Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
  • Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
  • While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

Ways to Test Microservices

It is easy to fall into the trap of making testing microservices complicated, but there are ways to avoid this problem. Testing microservices doesn’t have to be complicated at all when you have the right strategy in place.

There are several ways to test microservices too, including:

  • Unit testing: Which allows developers to test microservices in a granular way. It doesn’t limit testing to individual microservices, but rather allows developers to take a more granular approach such as testing individual features or runtimes.
  • Integration testing: Which handles the testing of microservices in an interactive way. Microservices still need to work with each other when they are deployed, and integration testing is a key process in making sure that they do.
  • End-to-end testing: Which⁠—as the name suggests⁠—tests microservices as a complete app. This type of testing enables the testing of features, UI, communications, and other components that construct the app.

What’s important to note is the fact that these testing approaches allow for asynchronous testing. After all, asynchronous development is what makes developing microservices very appealing in the first place. By allowing for asynchronous testing, you can also make sure that components or microservices can be updated independently to one another.

#blog #microservices #testing #caylent #contract testing #end-to-end testing #hoverfly #integration testing #microservices #microservices architecture #pact #testing #unit testing #vagrant #vcr

Software Testing 101: Regression Tests, Unit Tests, Integration Tests

Automation and segregation can help you build better software
If you write automated tests and deliver them to the customer, he can make sure the software is working properly. And, at the end of the day, he paid for it.

Ok. We can segregate or separate the tests according to some criteria. For example, “white box” tests are used to measure the internal quality of the software, in addition to the expected results. They are very useful to know the percentage of lines of code executed, the cyclomatic complexity and several other software metrics. Unit tests are white box tests.

#testing #software testing #regression tests #unit tests #integration tests

Dejah  Reinger

Dejah Reinger

1599859380

How to Do API Testing?

Nowadays API testing is an integral part of testing. There are a lot of tools like postman, insomnia, etc. There are many articles that ask what is API, What is API testing, but the problem is How to do API testing? What I need to validate.

Note: In this article, I am going to use postman assertions for all the examples since it is the most popular tool. But this article is not intended only for the postman tool.

Let’s directly jump to the topic.

Let’s consider you have an API endpoint example http://dzone.com/getuserDetails/{{username}} when you send the get request to that URL it returns the JSON response.

My API endpoint is http://dzone.com/getuserDetails/{{username}}

The response is in JSON format like below

JSON

{
  "jobTitle": "string",
  "userid": "string",
  "phoneNumber": "string",
  "password": "string",
  "email": "user@example.com",
  "firstName": "string",
  "lastName": "string",
  "userName": "string",
  "country": "string",
  "region": "string",
  "city": "string",
  "department": "string",
  "userType": 0
}

In the JSON we can see there are properties and associated values.

Now, For example, if we need details of the user with the username ‘ganeshhegde’ we need to send a **GET **request to **http://dzone.com/getuserDetails/ganeshhegde **

dzone.com

Now there are two scenarios.

1. Valid Usecase: User is available in the database and it returns user details with status code 200

2. Invalid Usecase: User is Unavailable/Invalid user in this case it returns status with code 404 with not found message.

#tutorial #performance #api #test automation #api testing #testing and qa #application programming interface #testing as a service #testing tutorial #api test

Aurelie  Block

Aurelie Block

1598916060

Top 10 Automation Testing Tools: 2020 Edition

The demand for delivering quality software faster — or “Quality at Speed” — requires organizations to search for solutions in Agile, continuous integration (CI), and DevOps methodologies. Test automation is an essential part of these aspects. The latest World Quality Report 2018–2019 suggests that test automation is the biggest bottleneck to deliver “Quality at Speed,” as it is an enabler of successful Agile and DevOps adoption.

Test automation cannot be realized without good tools; as they determine how automation is performed and whether the benefits of automation can be delivered. Test automation tools is a crucial component in the DevOps toolchain. The current test automation trends have increased in applying artificial intelligence and machine learning (AI/ML) to offer advanced capabilities for test optimization, intelligent test generation, execution, and reporting. It will be worthwhile to understand which tools are best poised to take advantage of these trends.****

#automation-testing #automation-testing-tools #testing #testing-tools #selenium #open-source #test-automation #automated-testing