DEVOPSdigest

The emergence of artificial intelligence (AI) continues to transform the technological landscape. Its application in several facets of software development continues to grow. One of the areas of software development where the adoption of AI can advance is software testing.

Software testing is crucial in ensuring the release of software products that meet both compliance standards and user demands for quality. However, with many permutations surrounding the use of artificial intelligence, we'll dive deep into uncovering what AI is in software testing.

■ How does AI in the context of software test automation differ from its broader definition?

■ What do we mean when we talk about AI and its sister term, machine learning?

■ What are the benefits of using AI and machine learning to advance state-of-the-art API testing?

Let's find out.

What Is AI & How Is It Changing the Dynamics of Software Testing?

Artificial intelligence is one of the most overloaded buzzwords in the digital marketplace. "AI" conjures up images of things like all-powerful supercomputers hell bent on human destruction, voice-control assistance in the way of Alexa or Siri, computer chess opponents, and self-driving cars.

Wikipedia defines AI research as "…the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals." But that's a little too abstract.

I like to think of AI as the ability of a computer program or machine to think (reason for itself) and learn (collect data and modify future behavior in a beneficial way).

It's in this definition that we start to see something more meaningful in the context of what AI means for software development tools and technology.

More Software Releases Means More Software Testing

As the number of developers worldwide continues to surge, more software releases are expected to hit the software market. A recent report by Statista corroborates this expectation with a projection that suggests that the global developer population is expected to increase from 24.5 million in 2020 to 28.7 million people by 2024.

This portends that we'll continue to see more software launches in the coming years. With this expected growth in the number of software releases comes the need to automate software testing.

Automation Reduces the Complexity of Continuous Delivery/Deployment

DevOps is a mature concept at this point. Continuous integration seems well supported and integrated into development processes. The continuous delivery and deployment aspect has been more challenging and complex and is an area in which leaders in digital transformation are more than 60% ahead of laggards in terms of process maturity. This is an area where software test automation plays a role.

Software testing is the process of subjecting a software infrastructure to a series of functional and nonfunctional testing scenarios. It's a process of evaluating software to ensure that it can do what it's designed to do efficiently. When teams test software, they can discover and resolve runtime defects, scalability issues, security vulnerabilities, and more.

The software testing process is usually rigorous, hence the need for automation. However, for software automation to be more efficient and seamless, there is a need to incorporate AI.

AI in Software Test Automation

The use of AI in software development is still evolving, but the level at which it's currently used in software automated testing is lower compared to more advanced areas of work such as self-driving systems or voice-assisted control, machine translation, and robotics.

The application of AI in software testing tools is focused on making the software development life cycle (SDLC) easier. Through the application of reasoning, problem solving, and, in some cases, machine learning, AI can be used to help automate and reduce the amount of mundane and tedious tasks in development and testing.

You may wonder, "Don't test automation tools do this already?" Of course, test automation tools already have AI in effect, but they have limitations.

Where AI shines in software development is when it's applied to remove those limitations, enabling software test automation tools to provide even more value to developers and testers. The value of AI comes from reducing the direct involvement of the developer or tester in the most mundane tasks. We still have a great need for human intelligence in applying business logic, strategic thinking, creative ideas, and the like.

For example, consider that most, if not all, test automation tools run tests for you and deliver results. Most don't know which tests to run, so they run all of them or some predetermined set.

What if an AI-enabled bot could review the current state of test statuses, recent code changes, code coverage, and other metrics, and then decide which tests to run and run them for you?

Bringing in decision-making that's based on changing data is an example of applying AI. Good news!

How Machine Learning Enhances AI

So, what about machine learning?

Machine learning (ML) can augment AI by applying algorithms that allow the tool to improve automatically by collecting copious amounts of data produced by testing.

ML research is a subset of overall AI research with a focus on decision-making management based on previously observed data. This is an important aspect of AI overall, as intelligence requires modifying decision-making as learning improves. In software testing tools, though, machine learning isn't always necessary. Sometimes an AI-enabled tool is best manually fine-tuned to suit the organization using the tool, and then the same logic and reasoning can be applied every time, regardless of the outcome.

In other cases, data collection is key to the decision-making process, and machine learning can be extremely valuable, requiring some data initially and then improving or adapting as more data is collected. For example, code coverage, static analysis results, test results, or other software metrics, over time, can inform the AI about the state of the software project.

Deliver Quality With Smarter Software Testing

Software testing needs smarter tools in order to increase the level of automation they provide and enable DevOps teams to deliver high-quality applications. Tools need to go beyond being just a framework for test creation and execution.

There's an important role for AI and machine learning in software testing as it increases the usefulness of the tools to developers by further reducing the manual and tedious steps they have normally needed to do for testing.

Parasoft has created many innovations for applying AI across these areas and much more. Learn more about the applications of AI and ML in software testing in this whitepaper, Guide for AI in Software Testing.

Learn more about how companies leverage AI as part of their software testing strategies at the Automated Software Testing & Quality Summit, hosted by Parasoft on May 16.