Today, Artificial Intelligence has evolved into something more than another gadget for developers to use. Increasingly, AI has become a trusted partner for developers by eliminating many of the hassles and obstacles hindering productivity while gaining insight into context and predicting a developer's intent.
Let's look at how AI assistants have improved the pace of software development in today's world and the actual shifts in productivity taking place versus all the hype surrounding it.
1. They Allow Developers To Go From Doing Busy Work To Solving Real Problems
Developers appreciate how much time it takes to do everything except actually build something creative:
- Get boilerplate code
- Search for code syntax
- Find documentation (of course there are docs for that!) and
Now developers are wasting their time performing the above tasks by using their AI assistants to complete those tasks.
By letting developers stay in the "flow" and concentrate on creating logic, architecture and meaningful decisions about their development instead of hopping around tabs, repo searches and chats, productivity is amplified. The simple act of allowing developers to stay in the same flow will provide more of a boost to productivity than anything else available.
2. AI Is By Far A Better Way To Catch Bugs In Code Before They Get Big
Today’s AI coding assistants are no longer just "autocomplete" style tools. The vast majority of coding assistants today will continuously analyze your code while you are writing it, and send the developer real-time notifications as they are developing. Some examples of this real-time feedback include:
- Indicating when the developer is not adhering to best practices within their code
- Recommending safe coding patterns for developers to make use of
- Allowing developers to identify potential issues with their code before the code has actually been written
- Identifying anti-patterns in a developer's codebase
- Providing recommendations on which libraries a developer should utilise that are both safe and efficient in terms of performance
3. AI Shortens Onboarding Time for New Developers
As a result of deploying AI to onboard new developers, the onboarding time has been significantly reduced due to:
1. Providing an explanation of functions
2. Providing documentation of progress
3. Providing file summaries
4. Providing a clarification of complicated code
5. Providing insight into previous code revisions
AI provides valuable insight to seasoned engineers who are working on both monolithic and microservices-based applications.
4. Documentation Finally Stops Being a Pain Point
Additionally, AI has improved the documentation process for developers by auto-generating:
1. Function-level summaries
2. API documentation
3. Comment blocks
4. Changelogs
5. Architecture notes
And because these are generated directly from the application code, the occurrence of out-of-date documentation is reduced.
Improvements in documentation also enable faster collaboration, increased productivity in the review process, and greater clarity in communications across departments.
5. Testing Pipelines Improve—Both in Depth and Speed
Finally, AI is changing how developers perform testing workflows:
1. Auto-generating unit test cases
2. Identifying edge cases that have been overlooked
3. Simulating user interactions with the application
4. Documenting gaps in test coverage
This makes the process of comprehensively testing software less daunting for developers while enabling a shift left testing methodology where more problems are identified and resolved prior to reaching the QA team.
6. AI Assists with System Design and Architecture Thinking
Modern AI algorithms search thousands of distinct architectures for patterns. This allows Developers to quickly identify:
Designing API Architecture
Monolithic Applications
New Service Development
Choosing Frameworks
Data Modelling
AI Algorithms provide Developers with verified patterns rather than forced guesses.
Although AI will not replace Physical Architecture Thinking, it will enable Developers to rapidly accelerate the development cycle and decrease Technical Debt.
7. Developers Ship Faster—But With More Confidence
Developer output is defined as the ability to produce a large volume of high-quality results in a short period of time. The measure of Developer productivity is through:
- The length of time between Concept Development and Working Prototype
- The ability to Refactor Code Immediately
- The ability to predict Code Changes and Impact
- The ability to identify future Dependency Relationships
- The ability to identify Optimisations That Human Developers Would Otherwise Miss
- These benefits lead to increasingly shorter Development Cycles, More Stable Code, and More Time to Innovate.
8. Teams See Clear Cultural Shifts Too
The advantages of AI extend far beyond technical applications.
AI fosters:
- Collaboration
- Speed of Communication
- Reduced Developer Burnout
- Enjoyment in Creativity
- Decreased Cognitive Load
- Developers are able to focus less on routine tasks and focus more on Building. This change to their work improved Developer morale immensely.
Will Developers Start to be Replaced by AI?
Not even close; AI replaces the repetitively performed mechanical part of the developer's job. The only part of development that did not need human creativity is the repetitive and mechanical parts of coding.
The following areas will still be driven by developers:
- Architecture
- Problem Solving
- Security decision making
- System Design
- Creativity
- Quality Judgement
As with many technologies, AI is simply a tool to help developers do whatever it is they do faster, cleaner, and smarter; the goal is to build better products faster than ever before.
Final Thought
As developers begin to incorporate AI code assistants into their workflow, there will be significant gains to be had through increased productivity from concentrated and focused effort, better outputs through increased levels of team collaboration, improved quality, and reduced time required to learn, create prototypes, and produce final applications.
The future is not an AI vs developer scenario, but instead a developer who knows how to use an AI tool vs. one who does not.
