Effective strategies for code quality control with continuous integration

In the present era of software development, continuous integration has become the industry standard practice. Not every organization is equally skilled at making continuous integration work, however. The better you know how to benefit from various sorts of automation, the more difference you can make when setting up continuous integration.

Technology leaders making use of tech to improve code quality need to realize that using these systems won't instantly resolve all their issues. They will need to make an investment upfront, and possess the skills and expertise within their teams to use the tools effectively.

The value of code quality control

There isn't a universal way to implement automated code quality control workflows. Rather, businesses can choose between top-down and bottom-up approaches. Each has its benefits for certain types of companies based on the existing situation among their engineering teams.

• Bottom-up methods: This means engineers are responsible for choosing the tools and methods they want to work with. In such a system, each team or individual may end up with a slightly different workflow based on preference or the needs that come from their day-to-day work.
• Top-down methods: Under this style, management or a tech committee sets out the rules about which practices and technology tools engineers should use. This means there will be a standardized and relatively short list of options available to team members, and everyone will operate along the same lines.

There's not one perfect way to implement automated code quality control — each style may be perfect for specific companies.

Larger organizations tend to do best when changes come from the top down, because of the need to set consistent policies within such large businesses. Consistent, mandated methods make it easier to move personnel between teams and set up uniform technology stacks.

Smaller businesses may prefer the bottom-up approach because it allows them to use methods that are relevant to the on-the-ground needs of engineers. These personnel can also make sure their preferred code analysis methods match their current tech stack.

As a small company scales up to become a large one, a transformation may have to take place. Gradually, policies will become more unified around shared practices and software licenses. Leaders should talk to teams during this transition to minimize resistance and ensure the best methods and tools are retained.

‍The code quality toolkit

There are a variety of systems that go into automated code quality monitoring. As organizations dedicate themselves more fully to modernized code analysis, they can add more of these solutions and build a formidable tech stack.

Impactful systems include:

• Static code analysis: These solutions read code in the same way human employees do, spotting definition errors or intentional trade-offs made by engineers that are causing issues, highlighting these spots so engineers can act on them. A code linter should potentially run daily to spot common typos, syntax problems or other errors in code.
• Unit and integration tests: Unit testing involves inspecting elements of an app in isolation, while integration testing consists of assessing the connections between subcomponents. Both types of tests verify that the app is functioning as intended. 
• Code coverage analysis: These systems report which parts and percentages of an app are covered by automatic testing. Highlighting the parts that don't receive automated tests as apps change and grow tells engineers where to focus their efforts.
• Functional/end-to-end testing: By using functional and end-to-end tests, engineers can add automation to the historically manual process of clicking through an app's features and verifying that inputs are causing the correct outputs.
• Security scanning: These utilities, based on machine-learning algorithms, are trained on common security flaws. They can automatically detect problems such as a lack of password protection on source code.
• Performance testing: By running the app at high load levels, these solutions simulate the high-demand situations, ensuring the software can perform under those conditions.
• Third-party dependencies analysis: Using a third-party dependencies analyzer is a way to detect which third-party systems are used within code, with a special focus on detecting outdated or unsupported versions, packages with known security vulnerabilities, commercial license requirements, etc.

Rather than adopting the full toolkit at once, organizations can work their way down the list, starting with foundational systems such as automatic static code analysis. Increasing the amount of automation within the code quality review process over time allows teams to scale up both the amount of work they can accomplish and the accuracy of their processes.

The balance between tools and training

The two elements of automated code quality control are technology and people, and it's the combination of these factors that sets a company up for success. It's far easier to buy a new software license than to train employees, but without the right level of familiarity among the workforce, the new solution can't reach its potential.

Employees don't just need the technical skill to use the latest advanced tools. They also have to adjust their mindset to welcome the new automations and processes into their daily workflows. In practice, engineers should understand how and why the new tools they're using are important — and every team member should be involved.

Giving employees periodic, meaningful updates on how to effectively use automated tech is a stepping stone to adding more sophisticated options over time and committing to a modernized code quality strategy.

The future of code quality

The rise of more advanced artificial intelligence models has the potential to revolutionize automated code quality control. At the moment, companies are deep into the development of machine learning and specifically large language models that can perform more sophisticated scans of code than previous-generation tools.

Further development is ongoing that can insert AI even deeper into the code-writing process. In these cases, generative AI models can assist in writing the code, not just analyzing the code created by humans. Such advanced systems can, therefore, flag potential issues and errors in the code in progress, preventing these issues from presenting problems.

Systems such as GitHub Copilot show where the combination of AI and programming is going. This product suggests functions while developers write, increasing their ability to produce clean, error-free code with less effort expended.

Working with experts to promote code quality

Rather than diving directly into automated code quality tools and practices, your organization can work with expert consultants to draw up a roadmap. Following through on such a rollout strategy helps you hit all the key points — adopting the necessary technologies, adjusting practices and keeping your employees informed at every step of the way.

A roadmap can take a few forms. In some cases, it's best to draw up a formalized plan that leads your engineering from its current software engineering practices and tactics to a more advanced and heavily automated approach. Other times, the best approach is more ad hoc and treated as one component in a more general software engineering consultation.

Better automated code quality review is one of the practices consultants can impart while working with your engineers, developers and designers. Improving your whole continuous integration pipeline to set up continuous delivery is a valuable goal that can help you compete in today's fast-paced software environment.

Transcenda's teams of experts can raise the level of your software engineering performance, whether as part of an ongoing engagement or work on a specific, short-term project. Contact us to find out how we can help with practices such as automated code quality control.