Organizations we help
Antithesis helps organizations make their software systems more reliable, and their engineering teams more efficient. Antithesis is particularly well suited to detecting and debugging problems in distributed systems, which includes many software systems in use at large and medium-sized companies today.
Our current model works best with software engineering organizations that are building and maintaining their own software systems. We have also worked with a number of organizations that are buyers of software, by helping to evaluate their vendors.
Here are a few of the teams we've worked with:
Antithesis is an exciting and unique tool for debugging blockchains and distributed systems. We used it extensively when testing the Merge. Antithesis was able to deterministically explore and find bugs in very exotic states and scenarios, ones that would have been nearly impossible to hand-code and unlikely to be hit in less stateful, traditional fuzzing.
This investigation has exceeded my expectations and has had a concrete influence on our release schedule for our annual release. This is the kind of thing we'd often take a month+ to solve with all hands on deck, getting there in a week improves our release confidence and saves me 3+ weeks of some of my most expensive engineers. I'm impressed, really well done.
Testing Stardog with Antithesis gives us increased confidence that unanticipated execution paths are being explored and cleared of bugs before they impact our customers, and their ability to perfectly reproduce every bug found has made diagnosing and debugging issues much faster for our team.
We could hire 100 distributed systems engineers and make them write integration tests for an entire year, and they probably wouldn’t be able to trigger all the interesting states and behavior that a single Antithesis run covered in 6 hours of wall clock time.
Antithesis's deterministic testing framework, featuring state space exploration and sophisticated fuzzing techniques, empowers us to rigorously examine and challenge uninstrumented programs in a methodical and predictable manner. As we embark on this collaboration, our aim is to significantly expedite the process of identifying and resolving bugs, ultimately enhancing the dependability and performance of our platforms.