Regression Testing in Agile: A Complete Guide for Enterprises

To scale-up the employee and customer satisfaction levels, enterprises frequently roll features to their software and applications. For instance, ING — the Dutch multinational financial services company releases features to its web and mobile sites every three weeks and has reported impressive improvement in its customer satisfaction scores.

New releases and enhancements are integral to agile businesses. But with these, comes the requirement to ensure a seamless experience for the user while using the application.

Whenever there is a change in code across multiple releases or multiple builds for the enhancement or bug fix and due to these changes there might be an Impact Area. Testing these Impact Areas is known as Regression Testing.

Regression Testing Cases

Regression testing is a combination of all the functional, integration and system test cases. Here, testers pick the test cases from the Test Case Repository. Organizations use regression testing in the following ways-

Executing the old test cases for the next release for any new feature addition.
Only after passing new test cases, the system executes the old test cases of the previous release.

Mainly, regression testing requires 3 things-

Addition of new test cases in the test case repository.
Deletion or retiring of the old test cases which have no relation to any module of an application.
Modification of the old test cases with respect to enhancement or changes in the existing features.

Types of Regression Testing

There are 3 main types of regression testing in agile:

1. Unit Regression Testing

This testing method tests the code as a single unit.

It tests the changed unit only.
If there’s a minor code change, testing is done on that particular module and all the components which have dependencies between them.
Here, testers need not find the impact area.
It is possible to modify or re-write existing test cases.

2. Regional Regression Testing

It involves testing the Impacted Areas of the software due to new feature releases or major enhancement to the existing features.

It involves testing the changing unit and the Impact Area.
Regional regression testing requires rewriting the entire test cases as it corresponds to a major change.
It requires deleting the old test case and adding a new test case to the repository.
It may affect other dependent features. Therefore, it requires identifying the Impact Areas and picking up old test cases from the test case repository and test the dependent modules referring to the old test cases.

3. Full Regression Testing

It is a comprehensive testing method that involves testing the changed unit as well as independent old features of the application.

Here, the changed unit, as well as the complete application (independent or dependent), is tested.
Full regression testing is mostly applicable for LIFE CRITICAL or MACHINE CRITICAL Applications.

Regression testing is also done at the product/application development stage.

4. Release Level Regression Testing

Regression testing at release level corresponds to testing during the second release of an application.

It always starts from the second release of an application.
Usually, when organizations seek to add new features or enhancing existing features of an application a new release needs to go live, for which, this type of regression testing is done.
Release level regression testing refers to testing on the Impact Area and involves finding out the regression test case accordingly.

5. Build Level Regression Testing

Regression testing at build level corresponds to testing during the second build of the upcoming release.

It takes place whenever there’s some code changes or bug fixes across the builds.
QA first retest the bug fixes and then the impact area.
This cycle of build continues until a final stable build.
The final stable build is given to the customer or when the product is live.
QA is usually aware of the product and utilizes their Product knowledge to identify the impact areas.

The Process of Regression Testing in Agile

After getting the requirements and understanding it completely, testers perform Impact Analysis to find the Impact Areas.
One should perform regression testing when the new features are stable.
To avoid major risks it is better to perform Impact Analysis in the beginning.
3 stakeholders can carry out Impact Analysis:
- Customers based on Customer Knowledge.
- Developer based on Coding Knowledge.
- And, most importantly by the QA based on the Product Knowledge.

All three stakeholders make their reports and the process continues till achieving the maximum impact area.
Then the Team Lead consolidates all the reports and picks test cases from the test case repository to prepare Regression Testing Suite for QA Engineers. Post this, the final execution process starts.

The main challenges of Regression Testing is to Identify the Impact Area.

Challenges of Manual Regression Testing

Time-Consuming as the test cases increase release by release.
The need for more manual QA Engineers.
Repetitive and monotonous tasks; therefore accuracy is always a question.

This is where Test Automation comes into place.

Advantages of Test Automation

Time-saving: Test Automation executes test cases in batches making it faster. I.e. it is possible to execute multiple test cases simultaneously.
Reusability: It allows reusing the test script in the next release when the impact areas are the same.
Cost-effective: There’s no need for additional resources for executing similar test cases again and again.
Accurate: Machine-based procedures are not prone to slip errors.

It may look like Test Automation might replace manual QA Engineers, but that’s not the case. Regression testing in agile still requires QA in the following instances.

Limitations of Test Automation

It is not possible to automate testing for new features. Test Automation Engineers still need to write test scripts.
Similarly, it’s not possible to automate testing in case of a feature update.
There is no technology support such as Captcha.
It requires human involvement; such as OTP.
At times, certain test cases require more time in test automation. During such instances, one can go for manual testing. For example, 5 Test Cases require 1 hour to execute it manually whereas Test Automation takes a complete 5 hours executing it.

In agile, enterprises need testing with each sprint. On the other hand, testers need to ensure that new changes do not affect existing functionalities of the product/application. Therefore, agile combines both regression testing and test automation to accelerate the product’s time-to-market.

If you’re looking for Testing Services for your Enterprises, please feel free to drop us a word at hello@mantralabsglobal.com. You can also check out our Testing Services.

Quality is never an accident; it is always the result of intelligent effort.
John Ruskin

About the author: Ankur Vishwakarma is a Software Engineer — QA at Mantra Labs Pvt Ltd. He is integral to the organization’s testing services. Apart from writing test scripts, you can find Ankur hauling on his Enfield!

Regression Testing FAQs

Why do you do regression testing?

Regression testing is done to ensure that any new feature or enhancement in the existing application runs smoothly and any change in code does not impact the functionality of the product.

Is regression testing part of UAT?

UAT corresponds to User Acceptance Testing. It is the last phase of the software testing process. Regression Testing is not a part of UAT as it is done on product/application features and updates.

What is Agile methodology in testing?

Agile implies an iterative development methodology. Agile testing corresponds to a continuous process rather than sequential. In this method, features are tested as they’re developed.

What is the difference between functional and regression testing?

Functional testing ensures that all the functionalities of an application are working fine. It is done before the product release. Regression testing ensures that new features or enhancements are working correctly after the build is released.

Related:

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

Regression Testing in Agile: A Complete Guide for Enterprises

Regression Testing Cases