How to Automate Your Legacy Application Code Testing For Smooth Modernization and Migration

By: Team CodeHunter™


There is a common adage that is said in family and small business, corporations and government: “If it ain’t broke, don’t fix it.” Unfortunately, that’s just not how progress works. This mindset has created a substantial problem for many businesses today; new or updated software implementations often create associated tasks that drain an exponential amount of resources. This applies to legacy systems on legacy operating systems, where the company functions with archaic technology to support operation critical software. With the modernization and the inception of newer systems, it becomes difficult to use legacy applications, developed over two or three decades ago, long before their present-day counterparts. As more industries have to migrate legacy code and dedicate time, money, and other resources to manual testing methods, it is important that they understand the opportunity for testing automation and resource conservation.

Table of Contents


What are Legacy Applications?

Modernizing and Migrating to New Operating Environments

Automated Testing Versus Manual Testing

The Evolution of Testing Automation Tools

Automated Testing Frameworks and Types

Five Automation Tool Best Practices

Six Types of Automated Software Testing




The rapid transformation of economies into a global digital ecosystem has posed many challenges to both industry and government. The growing requirements for operational productivity and stronger cybersecurity have led to investments in the porting and modernization of legacy systems and software applications. As the embracement of digitization grows, automated testing for assimilation of legacy applications is a competitive differentiator and a technology enabler for adapting in the new information technology landscape.

A recent study by Tata Consultancy Services discovered that 70% of corporate business systems today are a compilation of legacy applications. Over 60% of the average budget spent for Information Technology (IT) departments focuses on maintaining these Legacy systems, that includes costs for labor and overhead. Conducting business with outdated technology may seem to be the path of least resistance, however, it creates a slew of costly problems and long-term stagnation in development. Legacy systems hold a higher risk for malware attacks and increased vulnerabilities in security. They also lack resolution documentation for developers, and cause backlog deficits in resource allocation. The solution to combat rising costs of operation, in this scenario, begins with automation testing prior to migrating the legacy code.

What are Legacy Applications?

To understand the need for automated testing of legacy application code, it is important to define what makes up legacy applications. There are a variety of similar interpretations, depending on the industry and use of the application. Wikipedia describes a legacy system “as an old method, technology, computer system, or application program that continues to be used, typically because it still functions for the users' needs, even though newer technology or more efficient methods of performing a task are now available.” A more succinct definition from the research firm Gartner defines a legacy application as “an information system that may be based on outdated technologies but is critical to day-to-day operations.”

Deviation from the norm has created an emergence of advancements in how people do business on a global scale. The sheer impact of new technology has contributed to explosive growth, improved quality control, and enhanced cyber security. Despite the often eager nature of the community to embrace newer software, there are still many organizations that stick to their investment in old applications. These sometimes function as key components, such as mainframe applications or operating systems. As time and technology move forward, the cost of maintenance for these legacy applications can become a burden on both time and cost efficiency.

Modernizing and Migrating to New Operating Environments

Migrating IT legacy applications from one operating environment to another has become a priority for many organizations seeking better functionality. The process can include the conversion, rewriting, or porting of legacy code, code inherited from past applications, into the new applications. The end goal is to maintain the value of the legacy application in a new environment. Legacy code can still be meaningful to future operations, given that it can contain critical data points. The legacy modernization process preserves legacy code integrity by customizing and redesigning migration tools to facilitate operations in new databases.

Migration tools are part of the enablement equation for the transition of legacy applications to new operating environments. When migrating to the cloud or to a more physical location, migration tools need to be tested for efficacy. Testing limits potential risk in future failures or vulnerabilities, a critical step in the migration process.

Not every legacy application will need modernization and migration, this depends on the priorities and strategies of the organization. As more industries migrate toward cloud or hybrid cloud environments, the re-architecting of legacy code for the cloud resolves many technical limitations. This way, developers can address operational issues easily and accurately.

The readiness of legacy applications needs to be tested for optimization in older designs and with older features. There are additional challenges of data interfaces & dependencies, batch schedulers, custom programs, and cybersecurity integration that need to be tested to ensure proper function in the future. Automated software testing tools enable DevOps and continuous delivery of software for the legacy modernization process.

Automated Testing Versus Manual Testing

For modernization and migration of legacy applications, the choice is whether to test manually or to use automation. For most organizations, the guiding dynamics on how to test correspond to productivity and the ability to remain competitive, which include speed, efficiency, quality, and costs.

Manual testing efforts contribute to higher risk, largely because of the human factor of introducing unexpected errors. Humans are more prone to mistakes than algorithms. Manual testing can include thousands of extra hours of labor because of millions of lines of complex code in applications, and requirements to navigate interdependencies between data and systems.

Using automation to auto-convert legacy applications increases speed, reduces errors, and ultimately saves hours of labor time that amount to lower costs overall. Automated testing offers significant benefits for migrating legacy applications to the cloud by adapting to complexity, reducing risk, incorporating compliance, and algorithmically configuring applications using the new architecture. The automated testing legacy transformation process is typically the most secure way to preserve legacy applications and avoid the operational costs and time delays associated with migration to an entirely new software.

In a nutshell, automation testing for legacy applications is more scalable, transparent, and economical. The DevOps Enterprise Forum provides a good summary of the value of automation:

The primary goal with test automation is to drive down cycle time to understand quality and working software. Test automation unlocks the ability to greatly reduce cycle time and make quality visible and tangible. With an automated test suite, stakeholders no longer must wait for elongated regression cycles to understand the quality in the system. The test automation becomes the rapid and visible indicator of this quality. Successful test automation also depends on: Continuous Integration and Deployment, Environment Setup & Congruency, Test Data Setup/Management and System Coupling. These areas will require dedicated focus if you want test automation to provide large scale benefit.

Testing automation for legacy applications is valuable for repetitive tasks, therefore, it should be an integral part of the budget for code deployment. Implementing testing automation is an economical way to limit human error and increase the speed of deployment. Quality control provided by testing automation benefits the developer life cycle in both cost and speed, especially when used in areas of agile development, continuous testing, CI/CD implementation, and DevOps.

As industries continue to migrate away from legacy systems, they should be mindful of the different testing automation tools so they can choose the right one to meet their needs.

The Evolution of Testing Automation Tools

Currently, the most advanced tool for testing automation is codeless. These tools use learning AI and mathematical algorithms to recognize proper code behavior as they scan for dysfunction in migration. PerfectIO describes this evolution as:

Codeless automation tools came about as a response to the challenges that testers had with other existing solutions. Within the world of software testing, no-code automation (also known as codeless test automation) has grown in popularity in recent years. It addresses many challenges that come with test automation, providing solutions to some of the most pressing QA bottlenecks.

The circumstances around the needs and goals of the business determine which automation tool they should use. Several options are still in use today, regardless of their age and relevance. There are four categories in the evolution of test automation: legacy code-based tools, open-source, code-based tools, standalone record-and-playback tools, and codeless test automation tools (pictured below).

In the future, the methods and capabilities of automating testing in legacy application code will develop with the digital era. The next evolution of automated testing for legacy applications will no doubt use artificial intelligence (AI) as it will speed up processes, analytics, and reduce maintenance costs. AI is not a mature technology tool yet, it is simply a component. That being said, machine learning (ML) is already being used for test creation and execution. AI and ML will be valuable tools for testing, interpreting, and adapting legacy code and applications for years to come.

No-code automation tools address many of the issues posed by other testing solutions. They use the power of code-based platforms while functioning as a record-and-playback tool. Some codeless automation tools also incorporate AI and machine learning into their platforms as a self-healing mechanism to improve test stability and prevent tests from breaking. Having these safety mechanisms in place when choosing the tool creates a high probability of success in implementation and migration.

Automated Testing Frameworks and Types

Every business model should have a framework strategy for operations, and that includes testing automation. A framework can help visualize design and planning based on customized organizational priorities. It also serves as a guiding element to determine what methods of automated testing are best suited for legacy applications. “Software testing undoubtedly forms an integral part of the application’s software development lifecycle (SDLC) and earlier manual testing was more popular. One particular approach shows an example of what this framework might look like”:

Five Automation Tool Best Practices

In any framework, recommended best practices for implementing new systems provide the requirements of incorporating legacy code and/or building new products. Having a concrete idea of what capabilities the tool needs to work properly is an integral first step to building that framework. Below are five automation tool best practices to consider while building this framework:

  1. The scope of Automation needs to be determined in detail before the start of the project. This sets expectations from Automation right.

  2. Select the right automation tool: A tool must not be selected based on its popularity, but it is fit to the automation requirements.

  3. Choose an appropriate framework.

  4. Scripting Standards- Standards must be followed while writing the scripts for Automation. Some of them are-

  5. Create uniform scripts, comments, and indentation of the code.

  6. Adequate Exception handling - How error is handled on system failure or unexpected behavior of the application.

  7. User-defined messages should be coded or standardized for Error Logging for testers to understand.

  8. Measure metrics- Success of automation cannot be determined by comparing the manual effort with the automation effort but by also capturing the following metrics.

  9. Percent of defects found.

  10. The time required for automation testing for every release cycle.

  11. Minimal Time is taken for release.

  12. Customer Satisfaction Index

  13. Productivity improvement

The testing automation tool works best when aligned with the significant organizational needs and goals. Having a stable Test Automation Maintenance Approach allows the users to test whether the new functionalities added to the software are working properly, saving ample time in the SDLC. With these best practices in mind, determining the testing process, and team, are important players for the full migration to be successful.

The next, and final step, in using a test automation tool, is to determine the type/approach that suits business needs.

Six Types of Automated Software Testing

Testing is an essential part of guaranteeing any software product development, legacy or non-legacy, and helps ensure quality and capability. Testing can show if legacy applications are still being developed and supported. Automated testing efficiently optimizes resources and helps detect bugs, coding, or data interoperability problems. You can also use logs of automated tests to track processes, data tables, file contents, and share other information with IT teams. It is also possible to adapt and replicate automated testing for new projects.

A variety of types of tests exist for testing legacy applications. Unit and functional tests are the basic forms for most products. Regression testing is a type of test to measure the level of product quality.

An example test in an automated testing setup can include:

  1. Unit Testing:

  2. A unit is the smallest piece of code that can be logically isolated in the system. For most programming languages, that is a function. A unit test should not touch other code or make any interactions with a database, config file, or network. If the function holds such interactions, these interactions should be stubbed (faking the interaction and returning data).

  3. Functional Testing:

  4. Whereas unit testing focuses on testing isolated logic, functional testing aims at testing the function with all its integrations. In short, a functional test takes input and converts it to output. This output is used to compare with the expected results.

  5. Regression Testing:

  6. Regression testing is an important task for detecting if the quality of an application has decreased (or increased).

  7. Smoke Testing:

  8. A smoke test helps a QA engineer determine whether the deployed build is stable or not. Often, this is seen as the confirmation step before proceeding with further testing.

  9. Black Box Testing:

  10. Black box testing does not consider the internal code structure. With black box testing, only the inputs and outputs matter. A tester should not have prior knowledge about the application and should not be given any form of documentation. The goal is to see how useable the application is when somebody tries to use the application for the first time.

  11. Integration Testing:

  12. A testing engineer who performs an integration test wants to know if two or more systems can work together (integrate). A system can be anything ranging from a database to an API.


The modernization for many organizations from legacy systems to emerging software has increased the expectations for high-quality productivity and robust cybersecurity protocols, operations need faster solutions with small margins of error. Embracing automated testing tools for DevOps, particularly codeless ones, improves functionality reporting and addresses issues that might arise in manual testing and other testing tools. Automation algorithms are paving the way for the future of technology, industry, and governments; by embracing these new standards, we pave the way for rapid, safe progress for people everywhere.


DevOps Enterprise Forum. (2015). Tactics for Implementing Test Automation for Legacy Code. Retrieved from

Gartner. (2021, July). Information Technology Glossary. Retrieved from Gartner:

Guru99. (2021). Automation Testing Tutorial: What is Automated Testing? Retrieved from

PerfectIO. (2019, October 24). The Evolution of No-Code Automation Tools. Retrieved from PerfectIO:

Tata Consultancy Services . (2021, February 15). 70% of Global CXOs See Mainframe and Legacy Modernization as Top Business Priority: TCS Survey. Retrieved from

Testim. (2019, November 06). Test Automation Benefits: 12 Reasons to Automate in 2020. Retrieved from Testim:

TestingXperts. (2021, June 24). Test Automation Frameworks – Why, Types, Benefits, Approach. Retrieved from DevOps Enterprise Forum. (2015). Tactics for Implementing Test Automation for Legacy Code. Retrieved from

Wikipedia. (2021, June 10). Legacy system. Retrieved from Wikipedia:

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