In a world driven by software services, the process of actually developing software is far from simple.
The process of developing complex apps at scale can be plagued by sprawling environments that drain developer resources and take time away from the creativity process.
Veteran software engineers Arjun Iyer and Anirudh Ramanathan saw firsthand how long feedback loops and multiple stages for pre-production testing were not only slowing down software release cycles significantly, but also making it harder for developers to find and fix bugs and issues at an early stage of the process.
In turn, this was driving down software quality, pushing costs up, and eroding job satisfaction for engineers.
This served as the catalyst they needed to launch a solution.
Signadot was launched as part of the Y-Combinator 2020 cohort to bring automated and intelligent microservices testing to the development community and cut down on complexity.
Batched software testing erodes productivity
With microservices, the software development life cycle (SDLC) flows through a patchwork of disconnected environments. Code moves from local development to integration through preproduction setups that don’t test in the real environment.
Engineers often develop code in isolation, run local tests, submit a pull request, and get it approved. The request is merged into the main branch, where it joins dozens of other changes waiting to be deployed.
This batched approach to testing and release carries enormous hidden costs. Changes can take days or even weeks to reach production, greatly extending the overall lead time. In fact, engineers forced to revisit old code can suffer productivity losses of 20% to 40% with each switch. With less ownership over the release process, engineers invest less in test quality and automation. Overall, this causes teams to become tightly coupled by release schedules and limits the ability of engineers to drive agile innovation that’s key to business competitiveness.
To achieve faster software development loops, Signadot’s core offering is a Kubernetes-based platform with a number of features designed to tackle this specific challenge in order to tackle environment sprawl and unify the testing process completely.
A “shift left” approach to testing
In recent years, an approach known as “shift left testing” has been gaining momentum. The strategy aims to identify and fix defects sooner by starting testing activities at an earlier stage of the development process to help developers launch high-quality products at a faster pace.
As a company, Signadot embodies the “shift left” testing philosophy.
Its platform promises to help software teams take control of testing but also improve cohesion between developers.
With one unified platform, developers can spin up lightweight, isolated sandboxes that mirror the live environment to get faster feedback and more meaningful insights. These work without duplicating the entire environment to cut cloud costs and shorten the code-test-debug loop.
In addition, its solutions can catch contract breaks before they reach production, with its technology spotting meaningful API changes, filtering out false positives to help developers focus only on the most important changes – those that affect service consumers.
In 2022, this innovative approach helped Signadot raise a $4 million seed round led by Redpoint Ventures, along with participation from some of the industry’s top angel investors.
The founders’ stories
Prior to Signadot, CEO Arjun Iyer led Engineering and Data Science teams at Appdynamics with decades of experience building Cloud Native Systems.
As senior director at Data Science at Appdynamics, Iyer was responsible for building a next-gen data science platform to facilitate rapid iteration and delivery of machine learning based features into the product.
He worked to evangelize the space within the company and worked closely with its product team to unleash innovative solutions within the AIOps market segment and grow a cross-functional team of Data Scientists and Data Engineers.
Meanwhile, CTO Anirudh Ramanathan played a key role in developing Kubernetes technology at Google, evolving it from an emerging technology into a cornerstone of modern cloud infrastructure.
He also made significant contributions to AI through his work on Apache Spark, a powerful platform that laid the foundation for large-scale data processing. This work established critical infrastructure that has become integral to the development and scaling of AI technologies worldwide.
This transformation not only revolutionized how organizations deploy and scale applications but also set a new global standard for resilience and scalability in cloud computing across various industries.
