Diffblue
An Oxford spin-out using AI to write code, starting with Java unit test suites that catch errors earlier and faster in a DevOps pipeline.
| Date | Investors | Amount | Round |
|---|---|---|---|
| - | investor investor investor investor | €0.0 | round |
| investor | €0.0 | round | |
| investor investor investor investor | €0.0 | round | |
| investor investor | €0.0 | round | |
| investor investor investor | €0.0 | round | |
| investor investor investor investor investor investor | €0.0 Valuation: €0.0 124.3x EV/Revenue -121.1x EV/EBITDA | round | |
| investor | €0.0 | round | |
| investor investor investor investor investor | €0.0 Valuation: €0.0 | round | |
* | £1.0m | Grant | |
| Total Funding | 000k | ||
| GBP | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2023 |
|---|---|---|---|---|---|---|---|
| Revenues | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
| % growth | - | 4 % | 66 % | 130 % | 14 % | (3 %) | - |
| EBITDA | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
| % EBITDA margin | (370 %) | (836 %) | (509 %) | (450 %) | (110 %) | (103 %) | - |
| Profit | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
| % profit margin | (387 %) | (487 %) | (354 %) | (471 %) | (214 %) | (251 %) | - |
| EV | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
| EV / revenue | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
| EV / EBITDA | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x | 00.0x |
| R&D budget | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 | 0000 |
| R&D % of revenue | 112 % | 506 % | 127 % | 121 % | - | - | - |
Source: Company filings or news article
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Diffblue, a spin-out from the University of Oxford, was established in 2016 by Daniel Kroening and Peter Schrammel. Kroening, a Professor of Computer Science at Oxford, and Schrammel, a former researcher at the university, leveraged their extensive academic background in automated reasoning and program analysis to create the company. Their research provided the foundational technology for Diffblue's core product. The firm has secured significant financial backing, including a $22 million Series A funding round in 2017 led by Goldman Sachs Principal Strategic Investments, and has expanded its operations with a presence in London and the US.
The company operates in the software testing market, specifically focusing on automating the creation of Java unit tests. Its primary offering, Diffblue Cover, is an AI-powered tool that automatically generates and maintains unit tests for Java code. This service is designed to address the time-consuming and often neglected task of writing comprehensive test suites, thereby aiming to improve code quality and accelerate development cycles. Diffblue targets enterprise clients, particularly those with large, complex Java applications and those undertaking application modernization or cloud migration projects. The business model is based on providing its software to these development teams to integrate into their continuous integration (CI) pipelines and development workflows.
Diffblue Cover functions by analyzing a Java codebase to understand its behavior and then autonomously writes unit tests that reflect the current state of the code. This process helps development teams increase their test coverage significantly and catch regressions earlier in the development process. The tool is presented as a solution to enhance developer productivity by freeing them from the manual effort of test creation. One of its key features is the ability to generate tests for existing legacy code, which often lacks adequate test coverage, facilitating safer modernization and refactoring efforts.
Keywords: automated testing, Java, unit testing, AI for code, software testing, DevOps, code quality, regression testing, application modernization, enterprise software