Leveraging Artificial Intelligence Across the SDLC
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What Is AI-Assisted Development?
AI-assisted development is a feature that guides users through the software development process using the expertise learned from millions of anonymized code patterns, recommending the correct tools and patterns for any situation to the developer. It’s designed to increase focus and productivity by helping create logic flows faster and with more confidence, even for complex tasks.
It can also provide ‘just-in-time’ help pointing to reference documentation and other sources of information, and provides pre-built component suggestions which eliminate friction and knowledge gaps.
Benefits of AI-Assisted Development
There are two main benefits to AI-Assisted development.
The first is that it can make software development more accessible to those without coding skills or enhance the skills of existing developers to make them more efficient and productive. With a million open developer vacancies in the US alone, AI-assisted development can help to fill this talent gap.
The second benefit is that AI can identify and enforce development best practice. Failing to adhere to these best practices could result in software that runs slowly, or is prone to the occurrence of bugs or security vulnerabilities. AI-assisted development can prevent this from happening and ensure that software is future-proofed, performant and resilient.
How Does AI Help Developers?
Before we get into the core of this topic, let’s first be clear on what we mean by Artificial Intelligence (AI).
At OutSystems, we use this term to describe a set of technologies, including machine learning and automated reasoning, which are used to accelerate and automate a task – in this case, the writing of code.
So, the OutSystems approach to using AI is based around respect for the individual: about applying machine learning to support developers and ensure that they can carry out their responsibilities more quickly and with fewer errors.
We use three pillars to shape the way AI is applied to assist in the development process across the Software Development Lifecycle (SDLC). Essentially, the virtual expert described above fills the role of the tech lead, providing guidance, performing some of the more common tasks, and conducting the code review and validating the quality of the software.
- Guidance: in the moments when developers are stuck, we use AI to ensure that they are given the assistance they are looking for and that we get them back on track sooner. For instance, our Smart Guidance feature provides a contextualized search experience accessed from the development environment that offers up online resources that might help to complete the task they are undertaking. We also provide next-step suggestions for logic flows so, instead of having to go into the side menu and locate the element they want from a long list of possible nodes and data, developers are provided with a list of 1 to 6 suggestions.
- Automation: a lot of common operations can be automated: machine learning is ideal for carrying out repetitive actions or time-consuming actions; and, for tasks that are beyond the skills of the novice developer, we can use automation to apply the lessons learned from earlier similar programming tasks. This helps the developer to be more productive and engaged.
- Validation: Our Discovery tools use AI to analyze the dependencies among modules, identifying violations and ‘red flagging’ the elements (actions, screens, entities) that are assembled in the wrong place. For example, our architecture discovery tools enforce best practice and identify duplicate code, helping to reduce technical debt.
AI also features strongly in the testing tools used by our partners to identify those critical areas where testing is required and to automate the tests that are subsequently run, enabling testing to keep pace with the accelerated production schedules of the DevOps era.
If an application fails a test, AI can also provide feedback to the developer about the areas that need to be remedied.
The Future for AI-Assisted Development
Elite DevOps performers are pushing out clean code into production environments multiple times a day. There is no way that this velocity can be maintained – let alone improved upon – without the automation provided by AI.
In the future, we, therefore, foresee a greater role for AI in the development process, by making simple things easy and the complex possible, reducing and removing unnecessary and repetitive tasks, while maintaining the power required for complex applications.
At OutSystems, our mission is to enable every company to innovate through the power of software. By adding AI to all stages of the development process, the OutSystems platform helps development teams maximize their existing capabilities, freeing them to focus on the software that truly makes the difference, instead of wasting their time on just keeping the lights on.
Beyond the software development lifecycle, AI will be deployed to enhance the experience in end-user applications. For example, through the provision of automatic image detection or sentiment analysis, capabilities which can be easily implemented through the OutSystems platform.
As explained above, AI-assisted development provides a software expert acting as a tech lead, sitting alongside the developer and guiding them through the process. This will open up development to a much broader group of people, and free experts from the repetitive aspects of writing code so they can make an even bigger difference to their organizations.
