How to Use AI to Increase Software Development Productivity
11 engineering leaders have shared their specific cases on how they are using AI to increase Software Development productivity!
Intro
I have asked 11 engineering leaders to share their insights on how they or their teams are using AI to increase Software Development productivity.
These engineering leaders are sharing their insights from their experience working at all different sizes of companies -> from startups, mid-size companies and all the way to Big Tech.
List of people who contributed to this article:
Chris Ruddel, Engineering Manager at BigCommerce,
Moiz Imran, Senior Engineering Manager at Tintash,
TJ Musser, XD Development Manager at Illumina,
Jordan Cutler, Senior Software Engineer at Pinterest,
Sidwyn Koh, Staff Software Engineer at Meta,
David Garcia Berenguer, Head of Engineering at Cense AG
Deyan Genovski, CTO at Appolica
Shaun Wallace, Director of Engineering at Signal AI
Sumit Jaju, Technical Manager, Kinsale Insurance
Nawaz Sheikh, Senior Engineering Manager at Adobe
John Stearns, CTO at Roo
These leaders have shared 10 different cases, each having their own specific insights and lessons learned.
If you are interested in improving Software Development productivity → this is a must-read article for you!
Let’s go straight to the first case.
1. Using AI to review the code changes made by engineers
This is what Chris Ruddel, Engineering Manager at BigCommerce has shared. He created a script that sends the code change (pull request diff) to an LLM, which analyzes the change in 4 things:
Summarizes what the change is all about
Identifies potential bugs
Identifies potential security risks
Determines whether the variable and function names are clear and concise
Results have been mixed so far for them because it’s only looking at the difference between the current code and the new code. It lacks context.
This most often results in false positives, where it might identify something as a potential bug when the PR may actually be fixing the bug instead. However, it’s nice to look at it as part of a PR review to get some extra context.
He also set up a size limit to the code changes (to not be too costly), which has also positively affected the overall size of the code changes to be smaller → which is already considered a win in his mind.
2. Using AI for researching all the way to creating mockups
From Moiz Imran, Senior Engineering Manager at Tintash. He is managing a team that is fully distributed across various projects, so there are challenges with up-skilling the team.
They introduced AI in 3 different steps:
They started with a very basic replacement of Stack Overflow with ChatGPT.
Since ChatGPT was already pretty popular at that time, engineers were comfortable posting questions there and getting the answers.
This improved productivity slightly as it reduced “googling” time. However, AI was still prone to giving the wrong solutions.
Started using code suggestion tools like Github Copilot, Codeium, Cody, etc.
They started to regularly use the tools mentioned above for code completion, creating generic functions, refactoring, etc. This led to an uptick in not just productivity but also in the quality of code.
Using Cursor to increase the productivity further
The ultimate productivity increase came as a result of Cursor. Engineers can feed more context to the AI and also it allows them to experiment with different LLMs, with Claude Sonnet coming out as a favorite.
Engineers are able to convert mockups to code, create boilerplates and understand legacy codebases faster than ever.
Now, every engineer in his team regularly uses Cursor. For planning everyone is recommended to use o1-mini and for code generation → claude sonnet.
Other use cases include database design, query generation and code cleanup.
3. Using AI to migrate components from one framework to another
Shared by TJ Musser, XD Development Manager at Illumina. He’s responsible for a team of 5 front-end engineers known as the UX Platform team which is part of a larger Experience Design team. He is both a manager and a lead engineer.
They roughly maintain around 100 web components and these components were originally built using Angular and converted using Angular Elements.
They are currently in the process of migrating the components to using Lit Elements.
Cursor AI has especially provided useful for them for migrating from Angular components to Lit. It has sped up things dramatically, especially for some engineers who are not as familiar with Lit.
He really likes Cursor’s integration into the IDE and the use of the agent that can go on a roll editing many files at once. Things that might have taken him a couple of days have been reduced to several hours.
It has also positively affected documentation (storybook) creation, managing dependencies and overall getting things done faster.
However, he shared a word of caution → “While it’s been an incredibly useful tool. I’m worried about it for less experienced engineers. There are a number of pitfalls that can be hard to spot for someone less experienced and I can see it benefitting an experienced senior engineer much more than a junior one.”
4. Using Cursor AI to increase productivity working on a side project
This is from Jordan Cutler, Senior Software Engineer at Pinterest and Sidwyn Koh, Staff Software Engineer at Meta.
They are using Cursor AI for building their side project called WriteEdge.
They’ve been using Cursor AI to write and scaffold a lot of their code. It's been great for them since they work on this side project outside of work hours and don't have a lot of time.
They use it for three major purposes:
Scaffolding code (50% reduction in time)
Understanding stack traces (30% reduction in time)
Finding bugs (infinite value) since it scans for bugs continuously in the code
We will do a deeper dive on how they use Cursor AI in one of the future articles!