Tech startups, particularly SaaS companies, are adopting AI-coding tools to streamline various processes within the software development lifecycle. The benefits of these tools are clear; they can synthesize new code, debug existing snippets, and more.
But it’s not as simple as it sounds.
A study by Qodo reports that 65% of developers say AI misses relevant context during critical tasks and a study by Uplevel found that developers using Copilot saw a 41% increase in bug rate.
Companies adopting AI-coding tools need to thoroughly evaluate risks before integrating new technology into development workflows.
This article highlights five critical questions that startups should ask before implementing AI into engineering workflows to ensure a smooth and seamless transition.
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1. Does It Generate Code That Aligns With Style and Quality Conventions?
Error-free compilation is not the only benchmark that determines the efficacy of code. It should reflect the company’s existing code style and structure. This is crucial for scaling, maintaining a business standard and training new team members.
An effective solution is to adopt coding assistants that comprehend existing codebases. This way, the context of a project will be clear, and access to existing, human-generated code will be used as a reference.
AutonomyAI, an AI-coding platform built for front-end software development, does exactly that. Its suite of AI agents integrates into a company’s codebase and builds a deep understanding of existing business frameworks before taking action. Its interface will immediately show a preview of the output to streamline the review process.
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2. What Metrics Will Track Productivity Change?
Measuring the efficacy of AI coding tools goes beyond simple metrics, such as the time taken to generate new code or how many lines of code were created. Companies must dive deep and evaluate criteria that prove whether or not the technology will yield success in the long-term.
The practical answer is “it depends.”
Engineers use AI coding assistants in a variety of ways. For instance, some might leverage it to optimize a poorly-written function. Others might turn to them to find potential bugs when reviewing someone else’s code.
Let’s say a version of a website’s frontend features AI-produced HTML code. But that code is difficult to understand. Consequently, it increases the technical debt down the line when updating the site, diminishing overall productivity.
Simply put, teams need to look at short and long-term gains by tracking how each phase of web or app development is affected.
3. How to Test, Debug, and Validate AI-Generated Code?
Software developers can follow a standardized procedure to test machine-written code through various edge cases. It will reveal whether the code is error-free and delivers the desired results.
Lean teams can rely on SonarQube, an open-source solution that automatically inspects code as it is produced. The static analysis through this solution can uncover code smells, vulnerabilities, complexity, and coverage gaps.
Identifying these issues in AI-generated code helps keep the startup’s codebase clean, useful, and scalable.
When validating AI-generated code, it is important to get it thoroughly reviewed by senior developers. Encourage the team to add contextual comments along with the code to help the next developer understand the logic better.
AI-powered coding assistants can add comments, but they are often vague and lack nuance. For instance, when a new variable is created, they may simply mention “new variable created,” which lacks information about the purpose of the same.
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4. Do We Have a Fallback Plan?
The last thing agile companies want is to create tool-specific dependencies. Sometimes, servers malfunction and AI models fail to generate code for startups. In such situations, teams must have a backup plan to ensure operational resilience.
First, run models on local devices. Open-source AI models such as Code Llama and StarCoder2 can run on local machines. Solutions, such as LM Studio, make this process seamless.
Note that these coding assistants won’t have complete access to the entire codebase, so users must provide contextual information manually. They also have other limitations, such as smaller context windows, which make them relatively inefficient compared to leading coding assistants like AutonomyAI.
However, it is a reliable backup that can offload various tasks, such as crafting unit test cases and reviewing code snippets.
Finally, it is pivotal to help the team upskill. Too much dependency on AI (or any other technology for that matter) can erode critical thinking abilities. Encourage the engineers to stay in touch with foundational concepts and continue practicing.
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5. What Safeguards Will Prevent the Leakage of Sensitive Data Into the Model’s Training Set or Logs?
Codebases, particularly frontend files, handle personal information, which can end up in external logs or future training datasets for AI coding tools. Hence, it’s key to check the chosen AI provider’s data handling policies.
Some coding assistants are automatically hardcoded to mask sensitive data, such as names, addresses, etc., while others log every detail to deliver a better experience. It’s advantageous to err on the side of caution and go with tools that prioritize privacy.
Of course, it is hard to keep an eye on every AI interaction, especially when the developers are operating on tight schedules, which is the norm for startups.
Nightfall AI is a data loss prevention solution that ensures an organization’s information remains out of the public domain. The tool uses advanced models to detect personal information, secrets, API keys, etc., in code snippets automatically, with high accuracy, and anonymizes them before sending the snippet to AI coding assistants.
Wrapping Up
AI coding assistants can accelerate product development and free up engineers to solve harder problems, which is key for startups operating with limited resources. However, improper adoption can introduce risks—technical, operational, and legal.
Before integrating AI-powered coding assistants into the startup’s workflows, teams must ask themselves deep questions.
Does the code align with our standards? Are we getting productive? Can we test and validate outputs? Do we have a backup? And most importantly, how are we protecting sensitive data?
These questions will trigger critical discussions that will help build workflows and strategies to integrate AI coding tools appropriately into software development processes.
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