How Akvelon Maximizes Avail and Minimizes Risks Using Generative AI in Software Development

Every now and then, new technologies pop up not just to grab our attention, but also to shake up the status quo. AI-powered coding assistants have been seamlessly integrated into the market landscape and are on their way to becoming part of the best practices for an efficient SDLC.

Tools like GitHub Copilot, Google’s Duet AI, and JetBrains’ Code With Me, to name a few, can help with code writing, software testing, and creating product documentation. These tools promise the development of far more sophisticated applications, swifter project deliveries, and much higher elevated standards of code quality.

However, the reality isn’t quite this straightforward. The true impact of AI-powered coding assistants does not exclusively depend on the capabilities of the tools, but also on the mastery of their users, the duration of use, the level of supervision, and other critical variables.

Actively using AI in software development, Akvelon has harnessed the power of cloud solutions like GitHub Copilot and on-premises code-writing tools to deliver product updates faster, maintain overall code homogeneity, achieve better code quality from junior and middle-level engineers, and produce more explicit documentation, all of which provides enhanced value to our clients. In the upcoming sections of this article, we’ll delve into our approach to integrating AI code-writing tools into the development flow, our best practices for onboarding teams, and our tips for overcoming the hurdles of these tools and unlocking their full potential.

The Core Benefits of AI in Software Development

AI code-writing tools offer developers, quality assurance engineers, and tech writers various benefits, including enhanced productivity, accelerated coding, test case creation, and providing code reviews. Let’s explore their core advantages and how they can benefit your teams and business.

Cost Efficiency and Reduced Development Timelines

In software development, the equation is simple: time = money. The faster you can bring a product to market without compromising its quality, the greater your competitive edge is. AI coding assistants enable rapid feature development and product realization, with their capabilities spreading beyond coding alone.

While researching the outcomes of using AI in software development, Akvelon surveyed multiple GitHub Copilot users and uncovered some interesting findings. First of all, our survey results showed that there was an average time reduction rate of 16% when completing tasks with GitHub Copilot. Moreover, some developers reported an even higher acceleration of up to 50%. Read our GitHub Copilot Efficiency Explored article to learn more about our survey.

The cost of using GitHub Copilot or similar tools pales in comparison to the time and cost savings gained by enabling teams to complete tasks faster with these tools.

High Quality of Code

AI code-writing tools not only enhance developers’ skills but also help preserve the high quality and consistency of code across teams. While adapting to individual, team, or even project coding styles, GitHub Copilot maintains a mid-level proficiency and a reliable quality benchmark. This commitment to code quality benefits junior developers, too, as they gain virtual mentorship on coding nuances and best practices.

Furthermore, AI tools’ ability to adapt to existing high-quality code leads to accurate coding suggestions, minimizing extensive code reviews. This assures senior developers of the code quality, saves them time, and enhances project cost-efficiency.

Consistent Code & Documentation Styles and Streamlined Onboarding

Another compelling benefit of AI code-writing tools is their ability to foster consistency in code and documentation styles. GitHub Copilot streamlines project documentation by suggesting accurate descriptions for methods and classes, enhancing the team’s wiki. Furthermore, it can contribute to maintaining a uniform code style within the development team, fostering readability, consistency, and collaboration.

Finally, during project onboarding, AI code-writing tools efficiently explain project-specific details, speeding up the familiarization process for new team members.

Top Reasons Why Teams May Struggle with AI Coding Assistants

Having spent over 100,000 hours utilizing, integrating, and testing AI code-writing tools, Akvelon’s engineers encountered and overcame several obstacles that have plagued many teams who are exploring AI coding assistants. As a result, we’ve compiled a shortlist of some of these most common obstacles.

Shortage of Experience and Knowledge Base

Experience is a priceless asset when it comes to AI coding assistants. Engineers must grasp how to wield these tools optimally, delving into their principles, strengths, and limitations. Usually, it takes longer than a couple of working days, so it’s best to practice patience and avoid drawing hasty conclusions right off the bat.

Equally important is having access to a knowledge base and best practices, often shared by experienced colleagues. Without this foundation, the full potential of AI coding assistants may remain untapped.

Lack of Programmers’ Expertise

Beginner-level programmers may struggle to effectively intervene or validate AI-generated code, which can result in frustrating outcomes like errors and code that doesn’t align with project requirements.

Also, AI coding assistants typically operate within a narrow scope and have a limited understanding of the broader project context, including project architecture and specific application requirements. This is another reason why a certain level of programmer proficiency is a must to filter poor AI suggestions that may conflict with other code elements.

Resistance to Change

Many in the tech industry tend to resist change naturally, especially in the adoption of new tools, despite their many benefits. Most software development teams have established routines and workflows that have proven effective over time, and using AI-powered assistants means shifting their set flow significantly. This shift may seem disruptive or unnecessary.

A smooth adoption path with supporting materials, guidance, and enough time to gain ample experience working with these new tools will help engineers realize the benefits of AI co-pilots and become motivated to adopt them.

Akvelon’s Unique Expertise Using Generative AI for Software Development

We are proud to be one of the early adopters of AI tools in our software development processes, including GitHub Copilot and Copilot Chat. We don’t just dabble; it’s an essential part of how we build software. What sets us apart is our ability to integrate AI seamlessly into our clients’ projects. To do that, we’ve fine-tuned a comprehensive procedure that encompasses everything from addressing security concerns to measuring the results.

In this section, we’ll unveil the intricacies of our journey to AI-powered software development and advise on seamlessly integrating AI coding assistants into your team’s routine.

The Algorithm for Successful AI Coding Assistants Adoption

The market offers a variety of AI solutions, like GitHub Copilot, JetBrains, Duet, etc., that teams can adopt to enhance their processes. However, integrating and using these tools also comes with specific challenges, including:

• Privacy considerations
• Security measures
• Optimal usage practices
• Integration with existing workflows

These and other aspects will directly impact your team’s impression of their experience with AI, as well as the value of AI for your software product.

Our team already successfully implements GitHub Copilot in our workflow for commercial solutions and client projects as a common practice applied across our organization. Let’s dive into the adoption flow step-by-step, with insights and practical knowledge we’ve gained.

#1 Evaluating the tool’s suitability

Before implementing AI tools into a workflow, it’s crucial to ensure they benefit your project. When we’re considering using GitHub Copilot, we make sure that it aligns with the specific project’s needs and our team’s work specifics. For instance, we’ve found that while it benefits many of our projects, it’s not a good fit for some projects, specifically any that involve technical support, troubleshooting, or complex engineering tasks.

Yet, the majority of Akvelon’s software engineering teams, who deal with a lot of code writing, find the tool helpful in streamlining their working routine.

For more info on this, take a look at our survey focused on GitHub Copilot’s efficiency.

#2 Addressing Security and IP Concerns

Based on our experience with GitHub Copilot, it’s important to navigate questions regarding AI’s security and intellectual property aspects. Following best practices such as keeping sensitive data out of source files is essential, as well as consulting official documentation to understand data management and ownership rights: GitHub Copilot Trust Center, GitHub Copilot Product Specific Terms, Copilot Copyright Commitment.

We also recommend prioritizing project requirements, especially those concerning code privacy and security. Before integrating AI tools like GitHub Copilot, we consult with each client, ensuring consensus before implementation. For businesses that are restricted from using AI tools due to domain specifics, we offer self-hosted LLMs as an alternative that delivers AI benefits. For a deeper dive into this topic, check out our article on Empowering Your Business With Local LLMs.

#3 Management of Team’s Expectations

As already mentioned, some engineers may be hesitant to use AI tools like GitHub Copilot due to superstition about new things or even fear of pressure to perform better. To address these concerns, we present concrete results from trusted sources that showcase the benefits for software development teams, such as reduced routine, streamlined task execution, and increased job satisfaction. Secondly, we collect feedback from engineers and try to provide solutions in cases where expected benefits haven’t been apparent.

#4 Provision of Training and Settings Guidance

When we first introduce GitHub Copilot to a new team, providing useful resources and guiding videos on best practices greatly facilitates its uptake. By now, we have a whole set of our own best practices and step-by-step guides, including tips, tricks, and useful findings, allowing engineers to start using the tool with less stress and greater satisfaction.

We also ensure that security and privacy measures are taken, and that all development teams preserve security configurations for AI tools.

#5 Generated Code Validation

Our experience emphasizes the need for developers to carefully assess AI’s output. Tools like GitHub Copilot are smart and supportive, but they can’t replace the expertise of experienced engineers, so human oversight remains crucial.

#6 Consistent Monitoring of Tools’ Updates

Adopting AI coding assistants is an ongoing journey that requires teams to stay current with the evolving tech landscape, embracing the latest updates and features. In our organizational changes, each team at Akvelon has a dedicated manager responsible for the leadership in AI tools usage. They ensure the team stays informed about new features, fostering continuous learning and adaptation.

#7 Ongoing Knowledge Base Adjustment

To ensure sustained team productivity and growing value from AI-powered tools, we conduct surveys, gather team feedback and ideas, and continually fine-tune our knowledge base. This way, our developers receive immediate access to proven use cases and best practices based on our collective experiences and don’t repeat past mistakes.

GitHub Copilot & GitHub Copilot Chat Use Cases

Before wrapping up, let’s delve a bit deeper into the scale of AI coding assistant adoption in Akvelon and the reasons behind it. Now, we’re sharing a concise summary of the results and benefits of using GitHub Copilot for our clients’ projects, including the areas and use cases where we see the greatest value gained from the tool.

GitHub Copilot Efficiency Zones

1. Code writing and completion

GitHub Copilot shines when it comes to ongoing code writing. In many instances, it can generate entire code snippets from scratch, even through complex methods like project sorting.

2. Docs generation

Copilot also excels in generating documentation. It can suggest markdown syntax, complete documentation text, come up with a documentation text for explaining code snippets or project structure and architecture, and even suggest code snippets relevant to the documentation.

3. Streamlined testing

Writing unit tests has become significantly more efficient with GitHub Copilot’s assistance. The tool can generate test cases for a specific function with different input and expected output combinations.

4. Code refactoring and maintenance

GitHub Copilot Chat excels in code refactoring and maintenance due to its contextual understanding of your code. 37.5% of our developers who’ve been using this feature found it helpful, stating an increase of up to 10% in code quality and speed of refactoring.

5. DevOps

GitHub Copilot helps streamline script development and enhances automation in DevOps workflows, autocompleting repetitive sections of code such as Kubernetes configurations and Terraform modules.

6. Streamlined onboarding for new libraries and languages

Over 50% of our developers found GitHub Copilot helpful for mastering new programming languages and libraries. As programmers interact with the tool, it exposes them to different language constructs, syntax, and library usage, accelerating the onboarding with new technologies.

Realized Benefits

• Increased coding speed
• Heightened productivity
• Enhanced code quality
• Improved documentation quality

Final Thoughts

AI can substantially upgrade your business functions, streamlining product launches, bolstering team efficiency, and optimizing budget. However, a strategic utilization framework, explicit goals, and measurable KPIs are vital to leverage the technology successfully.

Our approach involves iterative cycles of improvement, where we establish best practices of the AI coding tools’ usage, gather feedback, address weaknesses, and then refine our practices accordingly. This constant evolution of our process, combined with a commitment to security, privacy, and ethics, ensures maximum benefits from AI for our projects.

We encourage you to explore the potential of using AI in software development, armed with our strategic implementation framework and extensive field experience in smooth AI tools adoption.