Negative Testing Guide - Explained with Real-Time Examples

What is Negative Testing with Examples?

From the software testers’ point of view, it is very important to verify that the software performs its basic functions as per the requirements but it is equally important to verify that the software is able to gracefully handle any abnormal situations or invalid input which helps to determine the stability of the software. Negative testing is performed to find a situation where there is the possibility of software to crash.

It is a negative approach, where testers try to design test cases to find the negative aspects of the application and validate against invalid input. Negative testing is also known as Failure testing or error path testing. The application’s functional reliability can be measured only with designed negative scenarios.

Examples of Negative Testing (Negative Testing Scenarios):

Example #1. Testing of a phone number field.

Test cases for negative testing could be input non-numeric values or alphabets in the phone number field, test less than 10 characters, or greater than 10 characters in the phone number field.

Example #2. Testing of age field.

The test case for negative testing could be to input age as the alphabet or negative integer.

Example #3. Testing of the zip code field.

Zipcode format varies in different countries. Negative test cases in such scenarios could be input alphanumeric value for the USA, India, and numeric for Canada, UK. Exceeding the number of characters in the zip code field is also a negative test case.

Example #4. For mandatory fields

Test by skipping the required data entry and try to proceed further.

Example #5. For data bounds and limits

Enter large values to test the size of the fields.

When to perform Negative Testing?

Negative testing is performed while doing functional testing of the build where there are chances of unexpected conditions. It can be performed by professionals.

Techniques Used in Positive Testing

Techniques used for negative testing are:

Boundary Value Analysis:

It is related to the invalid partition in your input test data range. The system should reject the values for invalid inputs. Invalid partition is going to have 2 boundaries – lower and upper boundary. If the input test data range is A-B, negative test cases should be designed for A-1 and B+1.

Example #1: For date field (1-31), invalid partition lower boundary(input 0 in date field) and invalid upper boundary(input 32 in date field) are considered for negative test cases.

Example #2: For username fields having specifications of 6-10 characters, invalid partition lower boundary(5 characters) and invalid partition upper boundary(11 characters) are considered for negative test cases.

Example #3: For floating-point values, let the system accept values from 0.2 to 0.8 with one decimal place. Invalid partition lower boundary(input 0.1) and invalid upper boundary(input 0.9) are considered for negative test cases.

Read more about Boundary Value Analysis Test Case Design Technique here

Equivalence Partitioning:

In this technique, input test data is divided into partitions. For negative testing, if you pick a value from an invalid partition, the system should reject that value.

Example #1: For date fields (1-31), input any invalid value such as 0 and negative integer values, the system should reject the values.

Example #2: For username fields having specifications of 6-10 characters, input any value from invalid partition i.e. 0-5 or 11,12,13… and test the behavior of the system. In this case, the system should reject the values and display an error.

Example #3: For Age fields between 18-80 years except for 60-65 yrs, input any value from invalid partition i.e. 0-17 or 60-65 or 81,82,83… and test the behavior of the system.

#manual testing #testing #coding #negative

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Negative Testing Guide - Explained with Real-Time Examples

What is Negative Testing with Examples?

From the software testers’ point of view, it is very important to verify that the software performs its basic functions as per the requirements but it is equally important to verify that the software is able to gracefully handle any abnormal situations or invalid input which helps to determine the stability of the software. Negative testing is performed to find a situation where there is the possibility of software to crash.

It is a negative approach, where testers try to design test cases to find the negative aspects of the application and validate against invalid input. Negative testing is also known as Failure testing or error path testing. The application’s functional reliability can be measured only with designed negative scenarios.

Examples of Negative Testing (Negative Testing Scenarios):

Example #1. Testing of a phone number field.

Test cases for negative testing could be input non-numeric values or alphabets in the phone number field, test less than 10 characters, or greater than 10 characters in the phone number field.

Example #2. Testing of age field.

The test case for negative testing could be to input age as the alphabet or negative integer.

Example #3. Testing of the zip code field.

Zipcode format varies in different countries. Negative test cases in such scenarios could be input alphanumeric value for the USA, India, and numeric for Canada, UK. Exceeding the number of characters in the zip code field is also a negative test case.

Example #4. For mandatory fields

Test by skipping the required data entry and try to proceed further.

Example #5. For data bounds and limits

Enter large values to test the size of the fields.

When to perform Negative Testing?

Negative testing is performed while doing functional testing of the build where there are chances of unexpected conditions. It can be performed by professionals.

Techniques Used in Positive Testing

Techniques used for negative testing are:

Boundary Value Analysis:

It is related to the invalid partition in your input test data range. The system should reject the values for invalid inputs. Invalid partition is going to have 2 boundaries – lower and upper boundary. If the input test data range is A-B, negative test cases should be designed for A-1 and B+1.

Example #1: For date field (1-31), invalid partition lower boundary(input 0 in date field) and invalid upper boundary(input 32 in date field) are considered for negative test cases.

Example #2: For username fields having specifications of 6-10 characters, invalid partition lower boundary(5 characters) and invalid partition upper boundary(11 characters) are considered for negative test cases.

Example #3: For floating-point values, let the system accept values from 0.2 to 0.8 with one decimal place. Invalid partition lower boundary(input 0.1) and invalid upper boundary(input 0.9) are considered for negative test cases.

Read more about Boundary Value Analysis Test Case Design Technique here

Equivalence Partitioning:

In this technique, input test data is divided into partitions. For negative testing, if you pick a value from an invalid partition, the system should reject that value.

Example #1: For date fields (1-31), input any invalid value such as 0 and negative integer values, the system should reject the values.

Example #2: For username fields having specifications of 6-10 characters, input any value from invalid partition i.e. 0-5 or 11,12,13… and test the behavior of the system. In this case, the system should reject the values and display an error.

Example #3: For Age fields between 18-80 years except for 60-65 yrs, input any value from invalid partition i.e. 0-17 or 60-65 or 81,82,83… and test the behavior of the system.

#manual testing #testing #coding #negative

4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

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Real-Time Data Analytics: Guiding and Improving Business Decisions

Real-time data analytics help in improving business operations by analyzing and processing data chunks to provide instant insights.

Data, also known as the digital currency, is the fuel for modern businesses. The present-day enterprises are constantly bombarded with a humongous amount of data, which needs to be collected, processed, and analyzed. Hence, it is difficult to deliver useful business outcomes instantly. Real-time data analytics resolves the time lag between data collection and processing.

Gartner defines real-time analytics as, “the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real-time simply means the analytics is completed within a few seconds or minutes after the arrival of new data.”

Accuracy and speed are crucial in data analytics. The modern business world needs real-time data analytics to efficiently deliver information, minimize costs and downtimes, and improve business decisions.

Benefits of Real-Time Data Analytics

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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.

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