Digma, the company pioneering the use of pre-production observability data to identify issues in complex codebases, announced its groundbreaking Preemptive Observability Analysis engine. The new engine will serve as a powerful checks and balances system to reduce the coding issues that plague codebases as they scale up on usage and complexity, slowing down engineering teams and impeding growth.
As AI Code Generators Become More Prevalent, Applying Preemptive Observability in Pre-Production will Become Critical
According to Digma, Preemptive Observability will become a critical part of the pre-production testing stage of the software development lifecycle, and not a moment too soon. A 2023 study by Stanford University demonstrated that developers using AI coding assistants tended to include more bugs, yet companies like Google have recently announced they are using AI to generate more than one quarter of new code for their products.
Preemptive Observability is set to become a critical differentiator to help enterprise engineering teams do more with less: companies using it can capitalize on the efficiencies of AI code generators while also increasing confidence in human-developed code by ensuring bugs and issues are flagged and fixed in pre-production.
Digma’s Preemptive Observability Analysis engine is designed not just to tackle bugs introduced by AI code generation, but also the longstanding issues many companies have had with unreliable human-generated code that could cause performance issues and SLA degradations. This will be particularly transformative for organizations in high transactional environments such as fintech, e-commerce, and retail.
To date, many companies have relied on Application Performance Monitoring (APM) tools to highlight service problems, SLA failures and monitor production status. APMs can send alerts when services slow down or fail in production, or when thresholds are breached, but they don’t offer insight into the root cause of problems and are limited when used in non-production environments.
By shifting the focus to finding issues earlier in the software development lifecycle with Preemptive Observability, teams can prevent problems rather than deal with their impact, validate system behavior more holistically, and address areas that are often overlooked in production-only observability. This also helps enterprises reduce cloud costs through early identification of performance and scaling issues.
Also Read: Semgrep Raises $100M in Series D to Boost AI Code Security
Digma’s Preemptive Observability Analysis engine gives engineering teams code-level insight into the root cause of these issues while adding AI-driven fix suggestions to identify and resolve performance issues, architectural flaws, and problematic runtime behaviors. Preemptive Observability can identify issues before they impact production environments and become a significant drain on productivity. It achieves this by analyzing observability tracing data, even when data volumes are low.
Leveraging pattern matching and anomaly detection techniques, Digma’s algorithm extrapolates expected application performance metrics, enabling it to detect deviations or potential problems that have not yet impacted the application. In analyzing the tracing data, Digma pinpoints the issue to the specific responsible code and commits.
“We’re seeing a lot of effort invested in assuring optimal system performance, but many issues are still being discovered in complex code bases late in production,” said Nir Shafrir, CEO and Co-founder of Digma. “It means that engineering teams may spend between 20-40% of their time addressing issues discovered late in production environments, with some organizations spending up to 50% of engineering resources on fixing production problems. Beyond this, scaling has often remained a rough estimation in organizations anticipating growth, and many are hitting barriers in technology growth that arise precisely during periods of significant organizational expansion.”
Digma’s Preemptive Observability Analysis engine’s new capabilities include:
- Pattern-based issue identification before code reaches production
- AI-driven fix suggestions based on runtime behavior analysis
- Team collaboration insights to prevent code conflicts between teams
- Cloud cost optimization through early detection of scaling issues
- Comprehensive management dashboards for non-coding engineering leaders
- Sandbox environment for evaluation without deployment
“While there are many code suggestion bots that scan code syntax, we’re uniquely analyzing code as it executes in a pre-production environment,” explained Roni Dover, CTO and Co-founder of Digma. “By understanding runtime behavior and suggesting fixes for performance issues, scaling problems, and team conflicts, we’re helping enterprises prevent problems and reduce risks proactively rather than putting out fires in production.”
This launch follows Digma‘s recent $6 million seed funding round, highlighting growing investor confidence in the company’s innovative approach to software quality. The funding supports continued product development focused on enterprise needs, particularly addressing the challenges faced by engineering managers, team leads, architects, and directors responsible for delivery timelines and code quality.
Source: Businesswire