Seattle-based TestSprite, which plans to launch an AI-powered autonomous software testing platform this year, confirmed it has raised fresh funds from a range of different investors.
The capital injection, totalling $1.5 million, comes from investors that include Techstars, Jinqiu Capital, MiraclePlus, Hat-trick Capital, EdgeCase Capital Partners, as well as angel investor Rafael Barroso.
An undisclosed additional amount came from the founding team, the company said in a statement.
Funds will be used to accelerate and expand product development, hire new team members, and scale operations to meet growing demand.
Established investor Andres Barreto, managing director at Techstars and a veteran in the software testing investment space, explained his firm’s decision to allocate fresh funds to TestSprite.
“Automated testing is becoming more crucial as AI-assisted software development rapidly becomes the de-facto way of building software in a market that is pushing for faster, more efficient software delivery from software engineers,” New York City-based Barreto said.
“We believe the Testsprite team and product is poised to become the leader in this new market shift,” he was keen to share.
AI-generated coding
As AI-generated code becomes increasingly prevalent, developers face growing challenges in testing and validating complex code they did not write, stressed Yunhao Jiao, the co-founder and CEO of TestSprite.
Jiao said that “with this funding, we can ready our new autonomous testing tool.”
“Funding comes on the heels of TestSprite introducing an early access beta program for its forthcoming end-to-end QA tool, which fully automates testing across back-end and front-end systems,” he continued.
“As AI-generated code becomes more complex, our vision is to let AI test AI, allowing developers to focus on innovation while maintaining software quality, Jiao added.
Current AI co-pilot tools still produce bugs while leaving critical gaps in testing and debugging.
Jiao claims its tools address this directly, delivering comprehensive validation without manual inspection, accelerating development cycles 10x faster.
The former Amazon software engineer claimed that the company’s software tools “learn everything possible” about the context available from a user or team’s software up front to do most of the work.
“It can scan objects, inspect software and interpret documentation. The AI then uses that framework to draft test plans in natural language for review before test code generation,” Jiao explained.
“This allows humans to remain in the look should test engineers want to change anything before testing proceeds,” he concluded.
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