Boosting Developer Productivity: Top 3 AI-Enhanced Tasks

I talk with developers every day, especially about the impact of generative AI on software development and their productivity. The common idea is that generative AI revolutionizes software development, offering significant time savings across various developer tasks. On the other hand, it also sounded to many like a threat. I’m diving into a few thoughts to help you position Generative AI, specifically ChatGPT-like tools, in your overall productivity. My Key discovery is that developers can now do a few tasks in nearly half to two-thirds of the usual time, such as documenting code functionality and writing or optimizing new code. 

However, the extent of these time savings varies, particularly with complex tasks or those undertaken by less experienced developers, highlighting the technology’s role in augmenting rather than replacing developers. This advancement necessitates a comprehensive strategy encompassing AI training, use case selection, and properly positioning the Generative AI in building your developer’s team. Also, I have a few tips to share with developers to feel more relaxed and open with Generative AI.

The Top 3 Developer Tasks Enhanced by AI

In this piece, we explore how generative AI significantly boosts productivity in software development, particularly in tasks like refactoring code into microservices, enhancing application functionality, and documenting code. Our findings highlight four significant areas these AI tools have profoundly impacted, which we will examine throughout the article.

Code Documentation Efficiency

Code documentation traditionally follows code development or the transfer of an application between developers. The lag or deprioritization of code documentation often leads to suboptimal documentation—cryptic, inconsistent, and incomplete—neglecting the comprehensive maintenance needs like deployment and monitoring.

Fortunately, the advent of generative AI has introduced a significant shift. Tools such as ChatGPT and GitHub Copilot now excel in understanding and interpreting code, paving the way for not just coding assistance but also the generation of explicit, thorough internal documentation. This capability ensures that the resultant code is professional, maintainable, and accessible without compromising the efficiency or speed of the code author.

By leveraging AI for documentation, the software development process becomes more streamlined, enhancing code maintainability and boosting overall productivity. These tools represent a pivotal evolution in tackling code documentation challenges, ensuring that developers can focus more on creativity and less on the mundane aspects of their work.

Streamlining Tactical Code Generation

Developers often find themselves bogged down writing boilerplate code, where templates offer a solution but come with their challenges. Understanding the template’s workings, customization, and debugging can be time-intensive and complex.

Enter generative AI, a game-changer that marries template efficiency with developer-specific customization, streamlining the bootstrapping of new applications. It allows for rapidly creating standardized tasks, like coding an API function in Flask for Python developers, extending even to unit testing. This drastically speeds up the process of writing high-quality code.

Moreover, generative AI tools like ChatGPT can demystify new frameworks and libraries, making them accessible within hours, not days. For instance, Chat GPT taught me Flask and helped me to use it 5 times faster than otherwise. I was able to understand code snippets’ logic behind them in a much shorter time.  The productivity boost I got astonished me back then.

Optimizing Code Refactoring

Code refactoring to address technical debt is a nuanced task that demands developers navigate through the logic of existing code, understand undocumented architectural decisions, and link dispersed codebases. Transitioning code to a new platform further complicates the process. It goes linearly with the lines of code that you need to understand and refactor.

• Developers need to understand or recall the exact logic of the code and why it was built this way. It is like walking on a landmine. Developers need to understand the undocumented architectural decisions. 
• Developers must connect the dots between dispersed code bases, files, and resources. They must also understand the new environment well, especially if the refactored code is to run on a different platform. 

Generative AI, such as tools found within GitHub Copilot, can simplify this by quickly unpacking the code’s logic, reducing cognitive load. 

• Code Base Understanding. Generative AI can help developers quickly understand the code’s logic and summarize some details without going through much cognitive overload. With the increasing context window, developers can now feed a significant chunk of their legacy code base into the model and ask it a lot of interesting questions about it, such as creating a logical diagram, explaining why a certain function is calling another, logically tracing the code, and so on. 
• Automatic Incremental Documentation. As explained through my experience, Generative AI can help developers learn new frameworks and platforms while achieving the job since they can get immediate explanations for the latest code snippets the AI generates. It is like having a peer programmer who never gets bored or judges you 🙂 

Improving Code Quality and Collaboration

Collaboration and exchanging ideas are pivotal in software development, yet teams are often hampered by the learning curve of less experienced developers. This can particularly affect code review processes and sharing of detailed contextual information about code, designs, or architectural decisions, slowing overall progress.

Generative AI offers a solution by serving as a go-to resource for engineers seeking answers or collaboration on straightforward tasks that don’t require another human’s input. A McKinsey report suggests that generative AI has significantly boosted collaboration efficiency. By bridging knowledge gaps and streamlining communication, generative AI enhances code quality and team collaboration, ensuring that all members can contribute effectively, regardless of their experience level.

When Generative AI Fails in Software Development

Generative AI, while transformative, has its limitations in software development, particularly in understanding high-level architectural decisions. It can’t decide on the best database based on future app usage, link technical requirements with business needs, or navigate organizational nuances for microservice separation. Furthermore, considering real-world complexities, it can’t choose between monolithic and microservices architectures. For instance, startups often opt for monolithic designs to speed up development with small teams until reaching product-market fit, a strategic decision beyond AI’s current capability.

What Does it Mean for Developers?

First, don’t stress out about AI eliminating your job. AI cannot perform the complex tasks of understanding complex business needs and building robust applications or cloud platforms. 

Focus on how AI can make you faster.

Leveraging AI can significantly enhance your speed and efficiency in software development. Embracing AI tools and overcoming the initial hesitation can set you apart, giving you a competitive edge. Those who adapt quickly to incorporating AI into their workflows will advance faster, leaving those hesitant to embrace these technologies behind. It’s crucial to explore and integrate AI capabilities and large language models (LLMs) to enhance your productivity and stay ahead in the rapidly evolving tech landscape.

Learn how to train a dragon AI. 

Discover the training process and evolve an AI “dragon” to enhance its capabilities over time for your specific tasks. The future holds the promise of personalized AI assistants tailored to developers, providing consistent support across various tasks and environments, independent of their employers’ tools or AI solutions. This personalized approach could revolutionize how developers interact with technology, ensuring that their unique coding needs are met efficiently, regardless of the workspace or tools at their disposal.

Set the Right Expectations.

Set the right expectations for yourself and your team. As a developer, AI could just take care of almost writing the whole application for me. I failed significantly. I tried using very high-level prompts and got a relatively shallow solution that took me in the wrong direction. I realized that I must take command of the overall logic and high-level architectural decisions. However, it accelerated my progress when I started implementing specific pieces of my application. That’s why I highly recommend using very specific prompts after you set the right expectations and engage the Generative AI at the right time. Read our top recommended ChatGPT prompts for effective software development to learn what I mean by that.

What Does it Mean for Technology Leaders? How Can they Help.

Given these insights, how might tech leaders harness generative AI’s time and quality benefits in software development to enhance productivity and mitigate risks?

Provide developers with generative AI training and coaching.

A structured educational program to improve developers’ development experiences is essential to ensuring they can effectively enhance their workflows with generative AI. This should start with foundational training focusing on creating natural-language prompts, known as prompt engineering. 

Additionally, seminars are vital for outlining the potential challenges of generative AI, including specific concerns around data privacy and intellectual property. These sessions should also cover strategies for critically assessing AI-generated code for its design, functionality, complexity, adherence to coding standards, and overall quality, including the ability to differentiate between beneficial and detrimental tool suggestions.

As developers incorporate these tools into their regular tasks, their educational journey should progress with continuous mentorship from experienced colleagues. This should be complemented by cultivating a community through specialized online forums and team gatherings designed for sharing hands-on experiences. Such initiatives promote perpetual learning, widespread adoption of best practices within the organization, and timely identification of any arising challenges, ensuring a smooth integration of generative AI into development processes.

Explore Beyond Basic Code Generation: Advanced Applications

Exploring advanced applications of generative AI goes beyond just code creation, impacting tasks like code refactoring, which aids in modernization projects often delayed by resource constraints. For instance, quickly refactoring a legacy system with AI tools can free up resources to tackle pending improvements. The selection of AI tools requires careful consideration, as their capabilities vary. Our studies found using multiple tools tailored for specific tasks, such as code refactoring or new code generation, significantly boosts efficiency, highlighting the benefit of a diverse toolkit in enhancing productivity.

Preparing for Shifts in Skill Requirements

As developer productivity rises, leaders must reallocate personnel towards more valuable tasks. Establishing a productivity baseline and tracking enhancements can uncover new organizational capacities. This requires strategizing on utilizing this extra capacity and identifying necessary upskilling to bridge emerging skill gaps, potentially directing efforts towards business growth or frequent product updates, necessitating new software design and architecture competencies.

Final Thoughts

As we conclude our exploration into how generative AI enhances developer productivity across documentation, code generation, refactoring, and collaboration, it’s clear that these technologies are reshaping the landscape of software development. Embracing AI accelerates task completion and uplifts the quality of work, making it imperative for developers and technology leaders alike to adapt and integrate AI into their workflows. 

By leveraging AI’s capabilities, fostering a learning culture, and navigating its limitations with strategic oversight, the potential for innovation and efficiency in software development is boundless. This evolution promises a future where developers can focus more on creative solutions, empowered by AI assistants that complement and enhance their skills.