Artificial intelligence is the fuel for what we know as the fifth industrial revolution. Its capacities to impact and disrupt numerous industries are virtually unparalleled. It is no less true regarding software development and testing.

While self-writing code is not yet a broadly commercialized technology, automation is more prevalent in writing and testing software. This approach becomes even stronger with the increasing adoption of AI.

We can train AI processes to monitor software, perform tasks autonomously, and make automated adjustments basing on the learned patterns. Such applications influence multiple areas in software development and testing, from design to deployment.

Artificial intelligence and machine learning in software development take various forms. Popular uses involve augmenting automatic processes with the ability to make decisions based on specific inputs or outputs. For example, we can tie deployment scripts to error monitoring services. If an error occurs on a production branch, it can roll commit back or forward to ensure production servers’ stability. Manual intervention won’t be necessary. It alone represents the dramatic impact on SLAs, as it becomes no longer needed to keep staff on call to respond to minor server issues and downtimes.

Many areas in software development and testing have already benefited from AI introduction and usage. This article will present just a few examples of how AI is changing this area in various aspects.

Most software solutions start with the discovery phase. There are countless meetings with clients or customers to discuss requirements. Further, developers will interpret those demands when designing a new system or feature set. Clients’ demands can be delivered and interpreted in many ways. If we don’t organize the processes well, the work can quickly become a mess. e

Thanks to the Natural Language Processing evolution, AI can interpret documentation with requirements against standards, such as the INCOSE Guide for Writing Requirements. On detecting missed, ambiguous, or inconsistent elements in a document, it will flag them immediately.

This approach can potentially save lots of time and money. Misunderstood or conflicting requirements are the most common source of software design troubles. Such issues can affect all the development processes. Hence, it is crucial to get rid of them as soon as possible.

Developers spend plenty of time writing boilerplate codes. Even with time-saving tools like frameworks, build scripts, and pre-processors, they still have to write tons of repetitious code. Gmail’s AI-powered Smart Compose feature suggests the tests based on email contents. The same approach applies to IDEs and other developer tools. AI-powered code suggestions help the developers write new components for existing software quickly and easily, based on the structures of the previous solutions.

It does more than saving time writing the code. This way, it ensures code consistency across a project. Some projects include a linter or formatter to improve consistency in the build process. However, AI-based code-suggestion tools can go one step further. They enforce specific design patterns above and beyond code formatting. Then, the development process becomes faster, and the code is more consistent.

Software testing is a time-consuming process for the QA team. It can also load the developers themselves (something which itself can sometimes become a battle). Writing, running, and maintaining tests eats a lot of time. Of course, there are testing frameworks. They help by providing a solid structure to tests and removing the need to write lots of boilerplate codes. However, AI-enhanced testing can create self-generating tests, including the test data generation.

AI can flag potential new bugs as soon as they get committed. First, it can learn the code base of the project and all its bug and regression data. Then, it develops insights into where bugs are likely to occur. Next, if it discovers the code that is likely to cause errors, it flags the commit. Such an approach can cut the time-consuming testing processes, like regression testing. It can identify bags before they occur.

Another area enhanced by AI is software deployment. It is a unique stage in the development process that can sometimes be home to errors that you might miss during tests. AI-enhanced monitoring software can detect broken deployments and roll the code placed on the production branch back or forward automatically to prevent incorrect code from getting into the live environment.

It reduces the time to restore if things go wrong. Also, it helps to cut back on staff costs to keep people on-call.

AI-powered processes can also apply to the software runtime settings analysis and environment configurations optimization. Thus, you can lower the costs and get more efficient apps. The software will get optimized automatically to use as few resources as necessary and allocate the server resources much better.

Development in AI and ML areas moves quickly. Machine learning applies to more and more processes. Software testing, deployment processes, and monitoring tools deal with the deployed software continually. They gather and analyze the data usage on the fly and respond to errors.

The implementation of AI in software development and testing is in its relative infancy. But it is growing. All new technologies get adopted by the development teams quickly if they offer ways to maximize resources and make the development tasks easier. Git, the industry-standard in version control, first came into prominence in 2005. Node.js, a revolution in JavaScript operation, appeared in 2009. Such popular languages as Go and Rust are even newer.

All those tools became common in software development. The adoption of AI to existing processes is likely to follow this level of uptake. We consider conveniences, like code suggestions and deployment monitoring. Then, it applies to the development processes automating, such as generating tests. AI offers a unique trajectory for future use. Besides, the more we use it, the more data it collects and analyzes. Then, it can learn more about how to use that data.

AI is currently at a very early stage in most projects. Typically, developers apply it to enhance existing processes. It can also highlight potential issues, which are still often subject to review by a developer. Here, AI might be a part of the code review process together with manual reviewing by developers.

In the future, as the AI industry continues to improve and developers become more familiar with it, these processes will allow making decisions based on the AI’s own analysis.

Currently, AI-based debugging identifies potential bugs or bottlenecks in a codebase. Over time, it can learn from these bugs and fixes to automatically fix bugs it finds. We can already see it in text-processing features such as autocorrect. The same functionality would spot simple mistakes commonly made by developers (missing a variable declaration, forgetting a semicolon, etc.) and fix them. The developers won’t need to do anything, and it is just the beginning.

Now, AI is a relatively new feature. Developers often use it in tandem with the manual review or apply it to simplify making the decisions. In the future, these processes will mature. AI will become more empowered to make decisions on its own.

AI is not going to reduce the roles of developers or testers. It will only reduce their tedious tasks and let people apply their skills to more essential and creative areas.

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