Platform engineering demands a comprehensive understanding of various aspects, with code confidence being a pivotal focus. This term refers to the reliability of the final product, but to reach high levels of confidence, engineers have to tackle a few problems.
Notably, compliance assumes a crucial role in identifying and mitigating vulnerabilities. Thus, enhancing code confidence becomes imperative for optimizing regulatory and internal standards compliance.
Combining those ideas can help engineers produce even better results, and for projects on the scale of generative AI platforms, a concise line of thinking can help organize any attempts to use compliance to foster code confidence.
Let’s dig in a little and explore what that could mean.
What Are the Primary Challenges in Ensuring Code Confidence?
How do you inspire internal code confidence? There are various approaches, but you can concentrate on three key aspects that focus on the essence: compliance, standards, and enforcement. Let’s look at these ideas in greater detail, starting with compliance.
Understanding the Role of Compliance
Compliance applies to two facets of Platform Engineering. In either case, compliance is really about consistency and robustness. The internal or regulatory standards remain in place to prevent common problems, address significant concerns, and keep involved parties aligned on goals and expectations.
This is particularly true when it comes to the role of compliance in generative AI. Generative AI offers radical new ways to complete old tasks and can efficiently operate outside existing compliance standards.
Finding ways to develop and use AI that stays within those confines steers clear of accuracy, ethical, and reliability issues.
Utilizing Code Standards
Outside of compliance restrictions, setting standards for coding practices is common. These help maintain consistent quality levels, and that improves overall outcomes.
Code standards can specifically apply to narrower aspects of development.
For instance, quality metrics focus entirely on practices and techniques that impact the quality of the code. Strong quality guidelines can certainly build code confidence, so they require careful thought and scrutiny when under construction.
Meanwhile, security and privacy measures address an entirely different set of issues. These standards prevent mistakes and vulnerabilities that could risk users, data, and the whole company.
Enforcing Standards for Confidence
For standards to improve compliance and boost code confidence, they have to be taught and enforced. Educational methods will be discussed later, but standards enforcement remains an essential part of the equation.
You can split enforcement into several strategies, each complementing the other. For starters, you can create team policies explaining standards to team members and why those standards must be followed.
Having a well-defined process for PR reviews and a quality metric for deliveries can be used for performance evaluations. This way, you can establish team practices that promote growth with compliance.
You can also automate a lot of compliance enforcement. If you have a documentation policy, you can use coding tools that force developers to document their work as they go. You can use similar automation for testing and other areas where compliance matters.
Automation is invaluable in building code confidence as it guarantees participation in the practices you have deemed essential to the cause.
The underlying point here is that every single approach perfects standards enforcement. Like any other aspect of platform engineering, you want to use a mix of tools and ideas.
When you do, you’ll improve compliance and the regulatory side. In both cases, you end up with more confident code.
7 Strategies for Fostering Code Confidence
With a more robust understanding of the role of compliance in platform engineering and generative AI, we can take a detailed look at strategies and best practices that build that confidence. The seven strategies in question are as follows:
- Implement through code reviews;
- Adhere to coding standards;
- Integrate automated testing;
- Educate team members on compliance;
- Implement secure coding;
- Utilize generative AI responsibly;
- Understand the importance of using your context.
Implementing Through Code Reviews
Code reviews (also called peer reviews) allow multiple developers to review each unit of code to scrutinize it for problems and opportunities for improvement.
Regular code reviews provide several benefits that start with improved code quality, but developers can also grow and improve through the process.
So, how do you incorporate code reviews into your implementation process?
Start by building the reviews into the development process. When building out workflows and standards, carve out time in implementation that allows for code reviews.
Once code reviews are baked into the process, you can optimize them with a few best practices:
- Create a checklist. Some items to include in your checklist might be readability, security, architecture, reusability, and test coverage.
- Standardize metrics. Create clear metrics that you can use for peer reviews, along with goals that steer the metrics. Some metrics include inspection rates, reflect rates, and defect densities.
- Keep reviews small. Try not to review more than a few hundred lines of code for any review session. This increases review precision, lowers cognitive burden, and prevents burnout. You will also catch more bugs and prevent more issues this way.
- Supplement with automation. Automation is virtually a theme in these discussions, and for good reason. Utilize automation to its fullest, even in code reviews.
Adhering to Coding Standards and Best Practices
There are too many coding conventions to list here, but creating a checklist of standards and conventions improves collaboration and general code quality.
The standards align many aspects of code, make it easier for developers to work off each other, and allow for more automation in testing and review.
To help you think about common standards and best practices, here’s a quick list:
- Use whitespace. This improves readability in code and documentation.
- Organize files. Standardize file organization to create a consistent structure.
- Minimize code. Keep code length as short as functionally possible to reduce bugs.
- Comment. Comment code so others can follow, but keep comments concise and specific.
These ideas help with readability and consistency. Additional standards can address format, security, naming, nomenclature, exception handling, and more.
You can improve adherence by integrating your standards into your enterprise development platform.
Integrating Automated Testing Processes
There are many ways to consider automated testing. One involves continuous integration (CI).
CI is a practice where code is run through a Version Control System (VCS). The VCS can automatically test code segments for validation, quickly incorporating automated testing into the development process.
CI is a great way to get a large amount of automated testing into your process, but it’s not the end. There are vast tools for testing automation, and they should be incorporated into various stages of development.
What starts with CI can expand with automated quality assurance tests, automated performance tests, and even automated tests for production-level updates and maintenance.
Educating Team Members on Compliance Requirements
Any effort to educate team members on a topic involves two methods: training and communication. We can fit a lot of ideas and strategies into those categories, but they can help frame how you go about education.
In terms of compliance requirements, communication provides thoroughness and consistency. You can fold compliance requirements into documentation and development resources.
For instance, development applications can include compliance notes and even automated tools to help team members remember common compliance lapses.
Email campaigns, meetings, seminars, and all the rest can supplement these ideas to strongly and consistently communicate your compliance requirements to everyone who needs them.
In terms of training, there are many approaches. You can opt for web-accessible standardized training, like training videos or interactive sessions. You can do in-person training. You can do anything in between.
What matters is that you remain consistent with the standards and expectations of team members.
Implementing Secure Coding Practices
Secure coding is another significant topic that requires considerable thought and effort, but as a starting point, we can hone in on a handful of key concepts.
At the root of secure coding sits input validation. Every application must validate inputs and adequately restrict access. For example, an application that takes keyboard inputs must restrict access to only keyboards known to be trusted or correct.
Alongside input validation is the more general concept of authentication and authorization. It’s core concepts because they control who can access the code and what they can do with it.
Segment access as much as necessary and use tools like two-factor authorization as much as possible.
Moving along, we can look at cryptography. You must encrypt files and data whenever they represent a risk. Additionally, encrypt external transmissions for any application that can touch sensitive data.
Vulnerability management is another pillar of security. Software components will eventually contain vulnerabilities. How you identify and address those vulnerabilities dictates the security of your final products.
This short list covers the foundations of secure coding, but many more resources expand on these ideas.
Utilizing Generative AI Responsibly
Marian Croak, VP of Responsible AI and Human-Centered Technologies at Google, wrote a great piece on this very topic. This will be a quick summary of some of her points, but you can find the full post here.
The first step in responsibility is the search for harm. Consider how your use of generative AI might cause harm to you, your team members, end users, or even competitors.
For example, generative AI could produce consistent output quality issues that damage application efficacy.
To prevent something like that, you need proper testing. Expressly, you can stress test your AI models using an adversarial approach. This can help you look for risks related to social harm, security harm, and functional harm.
Lastly, always clarify the generative AI’s function, intent, and limitation. As a general-use example, OpenAI constantly stresses to users that ChatGPT is not a fact-finding AI but only designed to predict text.
The Importance of Using Your Context to Have Assertive Suggestions
In terms of code confidence, this is the bottom line. Confidence builds internally. If you build up your context for assertive suggestions, you fully understand the motivations and ramifications of those suggestions.
This builds the internal confidence that filters through an entire development team. While the other tips mentioned can help with standardization and consistency, all those efforts translate into confidence with this final piece.
StackSpot helps developers do just that. We centralize technical standards in one place, and our generative artificial intelligence suggests code based on your organization’s context.
This way, the development platform’s suite of tools and resources improves the developer experience and makes software engineering teams more efficient.
Code Confidence Produces Better Results
Your focus on code confidence not only resolves issues and minimizes risks but also enhances the quality of the end product. This awareness creates a positive feedback loop, reinforcing the importance of maintaining strong code confidence.
Applied correctly, this positive feedback loop benefits the entire enterprise.
Combined with that, code confidence and compliance remain inseparable ideas. Adhering to compliance standards helps ensure regulatory compliance. It also contributes to the robustness and reliability of your codebase.
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