SapientAI emerged from stealth today to automate the writing of test code using a combination of generative artificial intelligence (AI), machine learning algorithms and data science.
Fresh from raising $5 million in seed funding, SapientAI CEO Rishi Singh said the goal is to leverage AI to push application testing further left using an AI Test Coder the company built.
The concept of AI Test Coder is similar to the co-pilot capabilities that GitHub and Google are providing to help developers write better code faster, but also goes beyond generative AI to add deeper analysis and contextual understanding to the tests it automatically generates, noted Singh.
In the longer term, the ability to use AI to create tests will result in the deployment of more secure, higher-quality applications because there will be no need to short-shrift application testing processes in the interest of meeting deadlines, added Singh.
Prior to the rise of AI, building application tests has historically been prone to error. AI Test Coder is designed to be a plug-in for an integrated development environment that will leverage the large language models (LLMs) created by OpenAI and cloud service providers to employ natural language to create tests based on an analysis of the application environment created using a combination of machine learning algorithms and data science techniques, said Singh.
As part of the effort, AI Test Coder instantly inspects all software code to understand the software testing environment, including any incorrect assumptions that stem from errors and bugs, before generating any test code. It then leverages code intelligence to understand existing code and automatically identify and detect patterns to anticipate any new software issue that may arise. AI Test Coder also locates where code changes will have an effect before any test code is created. This eliminates time that would otherwise be wasted on manually debugging code.
Once those steps are completed, Test Coder then generates more accurate tests without relying on the current trial-and-error approach to testing that can slow application development, said Singh.
In effect, AI Test Coder provides each developer with their own assistant dedicated to managing testing tasks that ultimately improve quality assurance, including removing many of the vulnerabilities that create application security issues that have become the bane of software supply chains.
It’s too early to determine precisely what impact AI will have on application development and deployment, but many of the manual tasks that create a sense of drudgery are about to be eliminated. That means the overall pace at which applications can be built and deployed is about to be greatly accelerated. DevOps teams are likely to find themselves pushing more code simultaneously through pipelines than ever.
Naturally, AI will soon be applied to not just writing code and tests faster, but also DevOps workflows. The issue now is determining which of the tasks that today require manual intervention will be eliminated in a way that enables DevOps teams to focus more of their time and effort on more complex challenges.
