Developers are constantly balancing demands to provide quality features of the highest standard at a fast pace. Every aspect of business now relies on software, which means developers are constantly working to write and produce the best software they can. Continuous Integration (CI) and Continuous Delivery (CD) help facilitate the creation of that software, but without the right quality assurance steps in place, they can inadvertently let potentially major code issues fall through the cracks.
Maintaining a balance between building high-quality software and doing it quickly can be challenging. Shift-left often appears as a common solution, but to be truly lean and agile we must shift-left on quality that takes into consideration both unit testing and static code analysis. This way, developers can ensure they produce good, clean code that results in top-quality software. By catching small bugs or quality issues early on in the process, developers can mitigate the possibility of writing code that causes security risk or breaks down further into the deployment process — at a high cost to the business.
Shifting Left on Quality
We must first agree on a new mindset — we shouldn’t be focused on finding problems. We should be focused on preventing those problems in the first place. All developers strive to write the best code they possibly can, but errors tend to be inevitable. Testing software code early — shifting left — helps catch errors and bugs soon enough in the development process that they don’t become sizable, expensive, disastrous problems later on. This kind of early testing on quality enables developers to create code that is reliable, adaptable, maintainable, and of course, secure. That’s where shifting left toward a focus on the code quality first, versus finding security issues already existing in code, can create significant inroads and provide a clearer path.
Shifting left on quality can also help mitigate errors caused by an increasing dependency on AI code generators. While AI coding assistants can make an impact on the developer workload and help boost efficiency or productivity at a time when demands for output are greater than ever, they aren’t a failsafe. They need to be thoughtfully governed and controlled. For example, in a recent study, it was found that ChatGPT-generated code is prone to various code quality issues, including compilation and runtime errors, wrong outputs, and maintainability problems. In fact, GitHub Copilot docs acknowledge this, recommending that those using Copilot conduct rigours testing to ensure the generated code is of high quality:
“You are responsible for ensuring the security and quality of your code. We recommend you take the same precautions when using code generated by GitHub Copilot that you would when using any code you didn’t write yourself. These precautions include rigorous testing, IP scanning, and tracking for security vulnerabilities.”
Quality checks still rely on special tools and human review to ensure code quality overall. The more code we write with the help of AI, the more safeguards must be in place to check that the code is accurate and issue-free. That’s why developers must enhance typical testing processes, shifting them further left, to avoid or help identify future errors that could also affect the quality of software. Employing the right combination of unit testing and static analysis throughout the software development lifecycle (SDLC) is a pivotal part of these guardrails that pave the path for top-quality software.
Balancing Unit Testing and Static Analysis
Developers often prioritize unit testing while embracing a shift-left mentality as a means of ensuring features and functionality work correctly. However, unit testing on its own cannot test for quality or cover every bug and problem within software code. The more bugs fall through the cracks, the more developers compromise the quality and security of their software as it reaches deployment.
The solution? Developers need to incorporate static code analysis, which can be done through automation. In comparison to dynamic analysis, which works at runtime, static analysis looks at the internal structure of an application and works on a variety of different code languages. By incorporating both unit testing and static analysis, developers can control code quality through the development stages, quickly detect and fix bugs, and improve overall software reliability.
Further, while developers may think of static analysis as purely a tool for finding bugs — or patterns that might lead to bugs — the right static analyzer can also help understand the why behind an issue or a better way to do something, helping them to learn as they code. Context matters, and becomes even more crucial for developers who are increasingly strapped for bandwidth.
Clean code Makes Better Software
A shift-left approach to quality that strikes a balance between static analysis and unit testing ultimately allows developers to write clean code — code that is consistent, intentional, adaptable, and responsible ultimately becomes easier to maintain. More than that, this Clean-as-you-Code process accelerates testing as a whole and gives developers the power to detect and address quality issues as soon as possible.
Earlier and more comprehensive testing in the SDLC enables a much more efficient way for developers to work. Waiting to address poor-quality code creates delays in deployment in addition to allowing that bad code to slip through to deployment, requiring reverting and refactoring of software code. The kind of feedback loops associated with checking for issues later in the process are lengthy and iterative, and can disrupt the development process by forcing a developer to return to work they might have done weeks or months ago, when it’s no longer fresh on the brain.
As developers try to reuse or repurpose code where possible, making sure it’s top quality is paramount. Incorporating static analysis with unit testing ultimately allows developers to continue building software they know is secure, maintainable, reliable, and accessible at any point in its lifecycle. It’s the best way to keep up with increasing development speeds.
Maintaining Code Quality
Particularly as developers balance increasing workloads with new AI coding assistants and tools, quality assurance is more important than ever. While some tools may enable more efficiency and developer productivity, they’re never a full replacement for the kind of analysis that prohibits costly bugs and mistakes from slipping through to production.
Developer teams must understand that a shift-left on quality approach, employing both unit testing and static analysis, helps strike the necessary balance between delivering software quickly and delivering software that’s high-quality. As those two characteristics become more and more crucial to developer demands, quality, maintaining that balance, and understanding the principles behind it, puts teams in a position to help their organizations see business results.
To learn more about Kubernetes and the cloud native ecosystem, plan to attend KubeCon + CloudNativeCon Europe in Paris from March 19-22.