Skip to main content

Machine Learning Models: Adapting Data Patterns With Copado For AI Test Automation

a web of shapes to illustrate machine learning models


Machine learning models are a hot topic today, but they’ve been around in some form or another for a long time – all the way back to the 1950s. Of course, the applications then weren’t nearly as sophisticated as today's options. Machine learning today has evolved from simply spotting software defects, to actively predicting were new defects will emerge, even to recent developments in which is able to develop software based on verbal descriptions.

Machine learning models are particularly attractive in test automation, where we build in the checks and balances to ensure top-performing software. In fact, it's estimated that by 2024, about 75% of large organizations will be using some AI in their continuous testing. These tools have the potential to revolutionize software across industries, provided we use them right.

The Importance (and Challenges) of Continuous Testing

Continuous testing is a staple of lean, so you're probably familiar with the term. When it comes to software, it's far more than that. Consider coding errors. The further along one is found, the more expensive it is to fix. According to a report published by the Consortium for Information & Software Quality (CISQ), operational software failure cost organizations about $1.56 trillion in 2020. As digital transformation accelerates, that number will only increase. Adding testing into the process at the earliest possible point and ensuring it continues through the whole lifecycle helps you overcome those issues.


continuous testing


Continuous testing enables organizations to capture quality metrics about their digital products without human intervention, ensuring everything performs as expected at the earliest stages of development. This lets you catch defects at a stage where they’re easy to resolve. 
Unfortunately, it's not a magic bullet. Poorly implemented, it creates just as many problems as it solves. The big holdup is manual testing. It's not scalable, takes forever, and requires ongoing maintenance. Every time a new release is prepared, the developer has to rewrite the test to fit the new framework.  

Automated testing – driven by machine learning – is the solution. In this, the initial test is extremely easy to create and attach to the software. When software is updated, the test leverages AI to heal itself and help it meet the new standard. This is made possible by machine learning, which teaches AI-enabled tests how to make logical decisions based on available data. 

Standard Machine Learning Models

Machine learning teaches a computer to analyze data and make inferences based on available information. A human user doesn’t have to make these decisions, because the computer is mimicking the way that person would think. There are three basic machine learning models:





Supervised ML uses training data to teach the program to think on its own. These data sets will contain a series of inputs, along with desired outputs. We can use a simple example based on the colors yellow, blue, and green. The input data would include the color yellow and the color blue. The desired output would be set to green. The machine would use this data to understand similar datasets – like light yellow, and light blue, make light green. 

Unsupervised learning is essentially a data dump. Only inputs are added to the system, and it is up to the program to find the patterns. It will locate common threads in the pieces of data and make assumptions. So, in our color wheel example, we would enter yellow, blue, and green as unlabeled inputs. The algorithm would then notice common attributes of both blue and yellow in the color green and determine on its own that the combination of those two colors leads to green.   

Reinforcement learning is different from the other two models as it's about training a machine to make a sequence of decisions. The machine is faced with a problem that it has to solve and will use trial and error in its solution. It’s a common model for games, though it can work for just about anything. In the color example, the machine would be told to create the color green. It would then go through all the spectrums of available colors, combining them until it created green. While other methods are based on inputs, reinforcement models know the desired output and work backward from there. 


These three base models of machine learning are the jumping-off point for many other methods of teaching computers to think. Other options like semi-supervised learning or similarity learning may combine several methods. 

There are also different ways a machine learning mode can organize data as it moves through the decision-making process. Some standard formats include: 

decision tree

Decision trees: Data is organized into various nodes (or branches) where connections can be made based on yes or no questions. This is a common regression-based strategy used in supervised learning.

Neural networks: This is a complex process designed to mimic the connections in the human brain. These networks use non-linear statistical models and decision-making to find connections in large quantities of data

neural networks

Clustering: Data clusters are used in unsupervised learning models. Data points are grouped to find connections and commonalities where inferences may be made.

Dimensionality reduction: This is a process of elimination. Key variables are established and then used to reduce the number of random variables or extract the desired ones. 


These are just a few methods of helping machine learning models make decisions. There are hundreds of hybrid approaches out there, and that number will grow with the field.

Applying ML Models To Continuous Testing Processes 

A thorough testing process is vital to ensure the quality and success of the final product. While scripts may be seen as a necessary step in the process, they ultimately serve as a tool to gather evidence and make more informed decisions. Streamlining or automating script management can greatly improve and modernize the testing process. 

Test scripts are critical to automation as they define the expected outcome of the program. They act as a benchmark for automation to return results and are essential for the supervised form of machine learning where the development code is compared to the test script to determine the desired outcome. Without test scripts, automation cannot run, and thus, clear, consistent, and reusable test scripts are crucial.  

However, there is a reinforcement component here as well. Consider an instance where a new release changes the effect of the underlying test. At this point, the program is confronted with a problem it must solve. It must adapt the test for the new environment. 

In a good automated testing platform, all of these complications get smoothed out by a strong user experience. It will use keywords and clear language to allow individuals to create better scripts. The user-friendly scripting method of creating scripts reduces the risk of errors. 

When Not To Automate Tests With AI

While we've touted the benefits of automating testing in software development, there are some instances where it isn't feasible. Machine learning, while exceptional, isn't a catch-all solution and shouldn’t be used in all scenarios. Five scenarios where automation of testing may not be succeeded are:

  • Limited test cases: If an organization doesn't have many programs to test, then automation would be cost-prohibitive. Test automation is best for situations where there are many tests and manual entry is not feasible. 
  • Immature development: Automation is for mature processes. If a company is still defining its goals, there are no metrics around which to structure the tests. For solid testing, you need a good way to understand the expected output.  
  • Unpredictable steps: This ties into immature development. Successful automation needs a benchmark of predictable, repeatable steps as a measuring tool. 
  • No environment control: Automation is like an experiment lab, if you cannot control the conditions, you cannot control the outcome of the test
  • No data control: If you have automated cases, and bad data, most likely machine learning will not payout

In summary, the key components for a successful testing process are data, environment, and intelligent test cases. Without these, the testing process will not yield accurate results. It's important to have a good combination of these three elements in order to ensure that the final product is of high quality and meets the requirements.

However, for organizations with many tests to run, mature products, and repeatable steps, AI test automation based on standard machine learning models is a game-changer. It's a low-code, simplified way of introducing testing across systems and platforms. AI components ensure tests are always up-to-date and easy to track, regardless of how frequently new releases are rolled out. 

Get More Durable, Predictive Testing Solutions Powered By ML

Artificial intelligence and machine learning are crucial elements of test automation success. AI provides the foundation you need for scalability. When your testing solution is AI-powered, like Copado Robotic Testing, it can adapt to changes and self-heal broken tests. Copado’s machine learning algorithms don’t just look backward, though — they can also predict the quality of your next release. With machine learning, your testing doesn’t just keep up with changes, it lets you get out ahead of them.


Copado Robotic Testing is a low code, AI-driven solution to automating testing to support stronger software development. To learn more, request a demo