Digma has launched a namesake platform the provides developers with continuous feedback from observability data that surfaces directly within their integrated development environment (IDE).
Digma CTO Roni Dover said the Digma Continuous Feedback platform leverages machine learning algorithms to enable developers to easily analyze their code at runtime to identify issues, anomalies and regressions. Digma Continuous Feedback removes the need to consult a separate dashboard that requires developers to log into an observability platform.
In fact, the reason developers often don’t make use of the data that observability platforms provide to write better code is that it’s not readily accessible at the time they are writing code, noted Dover.
In general, it’s become much easier to collect logs, metrics and traces by using open source OpenTelemetry agents that can be embedded within software libraries. The Digma Continuous Feedback platform applies machine learning algorithms to that data to provide developers with insights on improving code in real-time, said Dover.
The ultimate goal is to make it simpler for IT teams to distinguish when, for example, a software component actually needs more memory or is simply inefficient, he added. Those insights can be applied to code written by developers or code generated by an AI platform.
Whether code is written by a human or generated by a machine, quality is often in question. Developers with varying levels of experience will naturally write code that can differ substantially in quality and efficiency. At the same time, developers may be asked to fix code someone else wrote; the original author no longer works for the organization, noted Dover. In either use case, the Digma Continuous Feedback platform immediately surfaces issues that will enable organizations to set higher baselines for code quality, he added.
That baseline is especially critical as more code starts to be generated by generative AI platforms based on general-purpose large language models (LLMs) trained using code samples of varying quality collected from across the web. Developers will be able to immediately determine any issues that may arise should they choose to incorporate that code within an application.
Developers, of course, don’t deliberately set out to write bad code, but the tools that enable them to identify issues are not easily accessible. In fact, too many developers tend to view observability platforms designed for IT operations teams as being overly complex, noted Dover. The Digma Continuous Feedback platform takes that issue off the table by identifying issues without increasing the level of cognitive load developers are expected to carry, he added.
Eventually, thanks to the rise of machine learning algorithms and generative AI platforms designed specifically to write high-quality code, the overall state of software engineering will improve. The issue is determining the best way to get the benefits these technologies provide directly to the developers who write code—an issue that is at the heart of so many DevOps challenges.