AI and Machine Learning Software Testing Tools in Continuous Delivery

In this post, I’ll be going over what I have learned about testing in QA and continuous delivery over my professional career. We’ll also discuss some of the most popular solutions on the market and the benefits of understanding and applying AI to CI/CD pipeline.

Let’s dive into it!

What Is Continuous Delivery?

Continuous delivery starts with the idea of continuous integration (CI). For the software to be released, it must first be built and tested by a team, whose size depends on the scope of the project. Continuous integration is the practice of automating the integration of the work of each of the contributors into a single repository.

This DevOps practice allows developers to frequently merge code changes into a central repository where builds and tests are then run. Automated tools are used to assert the new code’s reliability before integration. And that’s where machine learning algorithms kick in, allowing the team to test their commits in real time and detecting bugs, misbehaviors, potential exploits, and even confusing code that could be refactored.

What follows is continuous delivery (CD), the idea that we are constantly updating our product in a production environment. Instead of a major release, or slow and big updates, continuous delivery means pushing out your product as fast as possible and delivering new content in small chunks. It doesn’t have to be agile in the strictest sense, but it’s certainly a step away from traditional waterfall release schedules.

CI/CD pipelines are not just new software tools; it’s a paradigm, a way of looking at software development. The software has to be built, tested, and finally deployed — that’s true for any methodology you apply. What makes CI/CD stand out is that we see this process as an iteration, with each cycle increasing the quality and scope of our product. Put all of this together and we have the notion of a continuous release process.

The team that builds the code has a release plan in the form of a schedule, usually based on the requirement of the product owner or the investor. Under a standard waterfall model, if you have a product that needs to go into production, that is going to take a while from conceptualization to release.

But in the current market, speed is everything. You have to release that product as fast as possible. And one way to increase productivity is to automate the development process. Code reviews can be a bottleneck to any team due to a little thing I like to call the pyramid funnel.

In essence, teams are usually structured as a hierarchy. With each increasing rank we have fewer and fewer team members whose job tends to involve working with the lower strata (ergo the pyramid). What tends to happen is that you have a code reviewer who has to answer to several different developers, while also handling their own part of the project.

The result? Unless your code reviewer is a machine (pun intended), there is a limit to what a person can feasibly do. So, approvals are delayed. If a developer is sticking to continuous delivery, then they will probably make pull requests faster than what the code reviewer can handle, and that’s how we end up with a funnel.

And that’s just one aspect of the whole process. Also consider that writing your own tests is extremely time-consuming, and as your product changes, so do your tests. Manually building and adapting every test as the project scales is necessary, but it’s one key area that stands to gain a lot from automation.

What Is AI & ML Automation Testing?

AI and machine learning automation testing is the process of building a test automation framework that will automate the testing of a software system. This is an essential feature for testing the quality of the software or its systems, and therefore it requires high testing standards.

Automation testing with AI is performed by software engineers with very little to no input from humans. In an automation test environment, several test cases are executed by the automation tester to verify the functionality of software systems. These test scenarios are also referred to as tests. The test suite can be executed using a tool such as Selenium, JUnit, RSpec, etc.

Test cases can be written in any number of languages, depending on the scope and scale of the project. Automated testing requires the interaction of automation testers with the application and with each other. A failed test can be handled in several ways, from outright rejecting the request, to even proposing new solutions.

Say for example that a function returns an object from a list, but the test finds that when you pass a number higher than the list length you get an error. The automated tester finds that this is because the developer forgot to add an exception handler (a rookie mistake, but a common one). So it automatically messages the author explaining the error and how they can fix it.

During the test procedure, the testers must interact with test tools and test scripts. As the number and complexity of tests increase, automated testing becomes more complicated, which is a problem in itself. Forethought and carefully planning are fundamental when building your automated test framework.

“Wait a minute,” I hear you say. “Isn’t this what automated testing tools already do?” Well, yes, in fact, it is. In many ways, automated testing tools are already AI, but they come with limitations.

The trick here is that with machine learning we can broaden the scope of our testing environments. The truth is that no two development processes are equal; each team will have different needs, and no algorithm can cover every single use case. As such, by training an AI with carefully selected data we can tailor an agent that aligns with the idiosyncrasies of our project.

Now, it’s very important to understand that automated testing is not here to replace code reviewers, QA, and testers, but to work in tandem. AI, no matter how advanced, isn’t yet on par with human ingenuity and the ability to think creatively. AIs are limited to their training, and the more we deviate from their training patterns, the more likely our digital helpers are going to go off the rails.

To summarize, we can integrate AI and machine learning in three key areas of our CI/CD pipeline:

Automatically generating tests for your application. This can help ensure that your application is always up to date and compliant with the latest changes.
Analyzing your application’s codebase and identifying areas that are most likely to break in the future. This information can be used to prioritize which areas of the codebase should be tested more thoroughly before each release.
Monitoring your application’s performance in production and identifying potential issues early on. This can help you avoid potential outages or degraded performance for your users.

How to Train an AI as a Software Testing Tool

The methods of training an AI vary depending on the type and complexity of the system being trained. The most basic method is to provide it with a set of data points it can then use to learn about and identify patterns within that data. This process can be repeated with different data sets until the AI can generalize its understanding to new data sets.

More complex methods may involve using reinforcement learning, in which the AI is given rewards or punishments based on its performance in achieving certain tasks. This allows the AI to learn through trial and error, gradually improving its performance over time.

In both cases, you will need a data set of software testing examples that includes input values and expected outputs. This data set can be created manually or sourced from an existing database. It can be based on your previous work or taken from open sources. The bigger the data and the more variability, the more powerful the model.

Once we have found the data, what comes next? Most commonly, the data is separated in a 75/25 split. We call the bigger group “training data” and the second group “test data.” As the name implies, we use the training data to actually train our model.

Then you can check if your AI is working correctly by examining the accuracy of your prediction on training data. If you get good accuracy, it means that we are on the right track.

But, is the machine really predicting an outcome, or did it just learn the training data set? To answer this question we check the model with our test data. If you get good accuracy on test data, it means that your AI is working well and has not memorized the training data.

It’s quite normal to not get a good model the first time around. Depending on the underlying algorithm you may have to change the parameters until you find the best possible model. For example, with neural networks, we play around with both layers and nodes per layer until we are satisfied with the result.

Ultimately, whichever method or combination of methods is used will depend on what kind of AI is being trained and what applications it will be used for. Creating an AI requires time, resources, and processing power, and while the prospect might seem enticing, ask yourself, “Is my project struggling with testing?” Small projects have very little to gain from all this effort.

On the plus side, once you have an automated tool, it can be repurposed for future projects.

Best Tools for AI and ML Software Testing

There are many AI and machine learning software testing tools available in the market. However, it is difficult to select the most appropriate tool for continuous delivery pipelines. Some of the popular AI and machine learning software testing tools are:

TensorFlow: an open-source platform for machine learning. It offers various features such as data flow programming, automatic differentiation, and deep neural networks.
Keras: a high-level API for deep learning that can be used with TensorFlow or Theano. It offers various features such as model construction, training, and prediction-making.
Scikit-learn: a free software Machine Learning library for Python programming language. It offers various features such as classification, regression, and clustering algorithms.
Microsoft Azure ML Studio: a cloud-based service that allows developers to build, deploy, and share predictive analytics solutions. It offers various features such as a drag-and-drop interface, pre-built models, and sample data sets

Clever readers will probably realize that these tools are mainly for machine learning, but they can be integrated with CI/CD solutions like:

Jenkins: an open-source automation server that can be used to automate various tasks related to software development, such as building, testing, and deploying code changes. It has a large community of users and plugins that make it easy to extend its functionality. Jenkins can also be used to trigger other processes, such as sending notifications or triggering deployments in other systems.
Bamboo: a commercial continuous integration and delivery tool from Atlassian (the company behind Jira and Confluence). It offers many features that are similar to those found in Jenkins, such as the ability to build, test, and deploy code changes automatically. Bamboo also has many plugins available that allow it to integrate with other Atlassian products (such as Bitbucket) or third-party tools (such as Slack).
GoCD: an open-source continuous delivery tool from Thoughtworks (the company behind the popular Agile project management tool Rally). It offers similar features to Jenkins and Bamboo but is designed specifically for use with the Go programming language (hence its name). GoCD has many plugins available that allow it to integrate with other tools (such as Jira), but its main focus is on supporting builds and deployments for Go applications.

And that’s just a small sample. There are several different commercial and open-source testing tools on the market that offer the aforementioned capabilities. Selecting the right tool for your needs will depend on some factors, such as your budget, the size and complexity of your application, and your team’s skill set.

Benefits of Implementing AI and ML Tools in Your Business

There are many benefits of implementing AI and machine learning software testing tools in your business. Some of these benefits include:

Increased accuracy: AI and ML software testing tools can help to increase the accuracy of your test results. This is because these tools can help to identify errors and issues that would otherwise be missed by human testers.
Increased efficiency: AI and ML software testing tools can help to automate repetitive tasks. This can free up time for your testers to focus on more important tasks.
Improved quality: ArtIficial inteligence and ML software testing tools can help to improve the quality of your software products. This is because these tools can identify errors and issues that would otherwise be missed by human testers.
Reduced costs: AI and ML software testing tools can help to reduce the costs associated with your software development projects. This is because these tools can automate repetitive tasks, which can save you money on labor costs.
Faster testing: AI and machine learning software testing tools can also help to speed up the testing process. This is because these tools can automate repetitive tasks, such as test case execution and data collection.
Improved coverage: Artificial intelligence and machine learning software testing tools can also help to improve the coverage of your tests. This is because these tools can generate new test cases based on past data and experiences.
Increased flexibility: AI and ML software testing tools can also help to increase the flexibility of your testing process. This is because these tools can be used to test a variety of different applications and systems.
Easier integration: AI and ML tools can also help to make the integration of your testing process easier. This is because these tools can be used to test a variety of different applications and systems across projects.
Improved scalability: AI and ML testing tools can also help to improve the scalability of your testing process. This is because these tools can be used to test a large number of applications and systems with very little overhead.

What’s the Next Step?

Continuous delivery is a process of delivering new content in small chunks instead of one big release. It starts with continuous integration, which is automating the process of integrating code changes into a central repository. Automated testing with AI is used to verify the functionality of the software system.

This AI has to be trained with previous data, which requires a lot of work, but once that’s done, it provides a very powerful and flexible model that can be repurposed for different projects or different areas of the same project.

AI and Machine learning software testing tools are here to stay. To build the AI, you are probably going to need both software engineers and data scientists to gather the data, build the model, test it, and find it. This might mean expanding your team or outsourcing your work. Whichever may be the case, AI and machine learning are powerful options for companies looking to up their game and optimize their pipelines.

If you enjoyed this, be sure to check out our other AI articles.