Have you ever thought about how your phone’s apps and software stay perfect even after countless updates? Imagine this: developers are coding furiously, testers are putting in their efforts, and new features are added to the apps. But some new changes come with great responsibility – make sure that everything works smoothly and no sneaky bugs have crept in! This is where regression testing comes in. Here, we will explain what regression testing is, its types, how to conduct regression testing, and many more. Keep reading to know!

What is Regression Testing?

It is a type of testing where you can check the changes made in the codebase that do not affect the existing software functionality. For instance, such code changes might involve adding new features, bug fixes, updating current features, etc.

In simple words, regression testing means re-executing previously passed test cases on the updated version of the apps to confirm that all features are still functioning properly. Moreover, regression testing is a series of tests that are conducted each time a new version is added to the codebase.

Is It Possible To Perform Regression Testing Manually?

Yes, such type of testing can be performed manually. Generally, it includes retesting the changed parts of the software application to ensure that the changes haven’t impacted the current functionalities.

Though manual software regression testing is possible, it can be time-consuming and error-prone, especially for big and complex systems. This is why automated regression testing tools are advisable to enhance efficiency and accuracy.

Examples of Regression Testing

Let’s take a web-based e-commerce platform as an example. Suppose the development team adds an enhancement to the search functionality, enabling users to filter the product by its color. Here’s how regression testing may be applied in such case:

Product Browsing: Apart from the changes made to the search functionality, users should still be able to browse through product categories, and check our product details and items in their cart without having any issues.
Cart management: After adding a new search filter feature, regression testing ensures that people can still easily add, remove, or update items in the shopping carts.
Checkout process: Confirmed that consumers can proceed via the checkout process smoothly, from entering shipping and billing information to completing the payment is pivotal. Regression testing ensures that this important functionality remains intact.
User accounts: We need to test the user’s account management system to verify that they can still log in, update their profile, and check order history without having any issues with changes made.
Mobile responsiveness: This testing may also involve checking the responsiveness of the platform across several devices and screen sizes to ensure the new search filter has not caused layout or usability problems on mobile devices.

When to Perform Regression Testing?

This testing in software testing is performed when the changes are made or the code is modified including adding new features, fixing bugs, and updating the current software. It is suitable in below cases:

A New Feature Or Functionality Is Introduced To The Application

For example, you have made a website with login functionality enabling users to first login only via email. And now you want to add login via Facebook or Instagram.

There is a Requirement to Change

For instance, you delete the remember password functionality on the login page which was applicable easily. Regression testing is conducted after every such change.

When Defects Or Patches Are Fixed In The Codebase

For example, when the tester finds a broken login button. Once the developers fix the bug, they test the login button for expected results, while simultaneously performing tests for other functionalities related to the login button.

When Performance Issues Are Fixed

For instance, when a page takes 5-7 seconds to load, the loading time is reduced to 2 seconds.

When There Are Environment Or Configuration Changes

For example, update the database from MySQL to Oracle.

Advantages and Disadvantages of Regression Testing

Advantages:

Regression testing makes sure that any change in code does not negatively impact other functionality.
It ensures that already solved issues don’t occur again.
This software regression testing serves as a risk mitigation strategy during testing.
Easy to learn, understand, and determine.

Disadvantages:

Without automation, this type of testing takes more time.
Testing is required for all small changes of code.
A repetitive process of testing can affect agile sprint.
Needs you to create complex test cases.

How to Conduct Regression Testing?

Normally, there are no fixed patterns to perform this testing. But, there are several methods that quality analysts should use while conducting testing:

Step 1: Regression Test Selection

First, you need to choose the test cases requiring re-testing. Keep in mind that you would not be able to test the entire test suite, and the selection of test cases relies on the module where there is a change in the source code.

Then, you divide the test cases into:

(i) Reusable Test Cases

(ii) Obsolete Test Cases.

Reusable test cases will be used for future regression cycles, while you won’t consider Obsolete ones for the upcoming testing cycles.

Step 2: Know the Time for Executing Test Cases

The next thing you need to do is determine the time it will take to execute the chosen test cases.

Several factors that impact the execution time are test data creation, regression test planning by the quality analyst team, and checking of all the test cases.

Step 3: Identify the Test Cases that can be Automated

Here, as per the results of exploring testing, the QA team can decide the test cases that they can automate. Automated test cases are faster as compared to manual testing and enable you to reuse the same script again. So, divide the test cases into two groups –

(i) manual test cases

(ii) automated test cases

Step 4: Test Cases Prioritization

Now, you collect all the test cases and prioritize them such as high, medium, and low. By this evaluation, you will execute the high-priority cases first, followed by medium and low-priority test cases. The priority will depend on the product’s functionality and user involvement.

Step 5: Executing Test Cases

Finally, it’s time to execute all the test cases and test whether the product is working as it should or not. You can go for manual testing or automation as per the requirement. For automated regression testing, using functional tools like Selenium, QTP, Watir, etc., allows you to execute the test cases faster.

Conclusion

Regression testing is a crucial aspect of software development that ensures code changes do not impact existing functionality. By re-executing previously passed test cases, developers can maintain software quality and reliability. While it can be time-consuming, especially when done manually, the benefits of catching potential issues early far outweigh the costs. With proper test case selection, prioritization, and execution strategies—including automation where appropriate—regression testing helps deliver stable, high-quality software products that meet user expectations and business needs.

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Data analytics assists in understanding complex information more easily. In this digital age, Natural Language Processing (NLP) changes everything making data analysis simple. With the use of NLP, we can go through large volumes of text data and pick out useful information in addition to identifying patterns. Ranging from examining sentiments to recognizing entities, NLP techniques increase the efficiency and precision of data analysis. Again, NLP enables computers to take data from, interpret it in as well as generate human language and this is useful for its administration in data analysis.

For instance, when computers use NLP, they can handle large amounts of text easily, even if it’s not organized neatly. This helps find information quickly, drawing from fields like AI and machine learning. It’s a cool area where computers get smarter at understanding language.

Understand Natural Language Processing (NLP)

First, we will talk about things at a basic level. In other words, Natural Language processing refers basically to making machines capable of understanding human language. It is indeed correct! This involves understanding the meanings of words as they appear in different contexts, such as emails, social media posts, blogs, or articles among others. In data science, NLP methodologies use various patterns for analyzing large numbers of texts that have not been well organized; these include emails, and articles, among others. These are diverse operations including sentiment analysis; entity extraction; text summarization; and text categorization.

Let me clarify this with an example: NLP can decide on the feelings expressed through words or look out for people’s names as well as towns, for example. Its use alongside AI and machine learning cannot be overemphasized as it is practiced in sectors including healthcare.

Limitations of Traditional Analytics

How Natural Language Processing (NLP) Revolutionizes Data Analysis and Insights

It is difficult for many potential users because most data analytics tools are too complicated for those without programming knowledge. Nonetheless, Natural Language Processing (NLP) is the remedy by enabling individuals to interact with data in ordinary languages. This implies that even if you are not computer literate; you can now ask queries and provide responses using your own words. It is for this reason that data analytics has become more user-friendly and within reach for any person irrespective of the level of expertise in technology.

Can NLP Transform And Enhance The Field Of Data Analytics?

Here’s everything on how Natural Language Processing can transform and enhance data analytics.

Keep scrolling!

Improves accessibility through conversational interfaces

The role of NLP is to make data interaction methods more conversational. For instance, NLP allows us to talk to computers the way we talk to our friends. Through such interactions, users can easily retrieve data without necessarily having to know syntax or commands that relate to it. As such, there is less fear attached to retrieving data; meaning that both central and other teams can harness the power of analysis even if they lack some particular expertise.

User Insight with Sentiment Analysis

It is very important for any business that wants to get better customer satisfaction, to learn how customers feel about different things. Sentiment analysis is provided by NLP which is a method of interpreting expressed feelings from written text. Using customer reviews, comments, or support tickets companies can understand what users think of them and thus improve them effectively so they meet their needs exactly.

Efficient Data Extraction and Summarization

When it comes to unstructured data, NLP is the best algorithm for discovering essential details from emails, articles, text documents, social media posts, etc. This use helps businesses that use written content. With NLP, text summarization is now automatic – it saves a lot of time when you have masses of information to read through.

Role of NLP in Data Analytics

By combining Natural Language Processing (NLP) with data analytics, agencies can get valuable insights that were previously tough to obtain. Nowadays, NLP has become a powerful tool that helps shift towards a more data-driven approach. With the help of NLP, it’s easier to access and understand data which can further lead to better decisions. This simple integration helps businesses to stay updated with the latest data analytics trends and motivates innovation. Below, we will mention the role of NLP in detail:

Making Language Differences Easier To Understand

NLP acts as a bridge. It enables people to engage with data analytics even if they have non- non-technical background. This breaks down language barriers and makes sure that the insights are available to everyone promoting a culture of collaboration.

Encouraging Innovation Through Making Things Accessible To More People

Entities can foster creativity by simplifying information accessibility with Natural Language Processing (NLP.) This can trigger mixed-skilled workers from varied departments to creatively solve problems that enhance data-driven decisions at all levels within organizations. Applicability of NLP in this scenario means that it converts data from individuals into a collective asset that allows teamwork among employees leading to innovative practices alongside the attainment of mutual objectives of such teams.

Streamlining Business Processes With Automation

Why is one stuck with abstract processes when we have automation? So far so good. But beyond routine inquiries, NLP enables computer systems to understand and communicate with humans in their natural language. This has also found applications in several businesses including customer support and data entry where it is used to automate tasks.

Using NLP, computers can do this kind of job on their own without engaging human beings in it. And it seems we have not been working hard enough or something still needs clarification sometimes.

Conclusion

Typically, NLP is a powerful tool that can process and analyze data easily, especially for dynamic industries. With NLP, decision-makers have real-time insights that are based on the latest information available, which enables them to make better decisions. Apart from this, they can assess even minute data, enabling them to respond promptly to the change scenarios. All of these make NLP a strategic asset that drives informed and timely decisions throughout the companies.

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Tag: Automation

What is Regression Testing? All You Need To Know

What is Regression Testing?

Is It Possible To Perform Regression Testing Manually?

Examples of Regression Testing

When to Perform Regression Testing?