Are Pre-Emptive Technologies the Key to Reliable AI-Generated Code?Are Pre-Emptive Technologies the Key to Reliable AI-Generated Code?

By Nir Shafrir, Digma

Companies are rushing to adopt AI code generation to slash development costs and accelerate innovation. Many development teams that have hopped on the AI wagon are still trying to find the right process to safely deploy code they didn't write and may not fully understand. Google recently announced it is using AI to generate more than one quarter of new code for its products. Yet a 2023 study by Stanford University found that the code developers are writing using AI coding assistants tends to include more bugs.

With the rise of AI-powered coding tools, companies are also generating code at a faster pace. That's great for efficiency, but without proper guardrails, simply increasing velocity also means potential issues can compound quickly. AI helps teams move faster, but without a way to pre-emptively detect performance and scaling problems, the risks grow significantly.

For example, let's say a company wants to start processing payments through its website. It could take a developer a couple of weeks to build an API, test it, and refine it. With AI, that same API could be generated in minutes, with refinements made in hours instead of weeks. While that's incredible, it also means companies are deploying code they haven't fully vetted. They might not fully understand how it works, whether it can handle real-world demands or whether it is full of potential issues that could introduce considerable business risk.

These are only a few of the fundamental questions surrounding AI code generation, and they underpin a critical need for a shift in how organizations approach complex code development. While traditional observability tools have helped identify potential performance and scaling issues, pre-emptive technologies will ultimately serve as the crucial bridge between AI efficiency and production reliability.

Beyond Traditional Observability to Pre-emptive Performance Analysis

Traditional observability tools excel at detecting problems in production environments. They monitor thresholds, trigger alerts when something goes wrong, and provide dashboards that help teams troubleshoot issues after they've already impacted users.

While valuable, this approach has a significant drawback: It's reactive. It enables organizations to monitor what's going on under production and react when that arises. However, it's a model that forces developers to context-switch from current feature development to emergency troubleshooting, creating costly disruptions to workflow and business continuity.

It also often translates into a huge effort invested in ensuring optimal system performance, but one finding many issues still being discovered in complex code bases late in production. Engineering teams may spend nearly half their time addressing issues discovered late in production environments, using engineering resources to fix production problems.

This approach hits where it hurts when it comes to scaling because it creates barriers to technology growth that hinder organizational expansion. The key is to shift the focus to finding issues earlier in the software development lifecycle.

Pre-emptive observability is a new software quality approach that uses machine learning to analyze observability data streams (like logs, traces, and metrics). It can detect patterns in how code behaves during development and testing, predicting how these patterns will manifest under production loads and automatically tracing issues back to specific code changes or commits.

Leveraging pattern matching and anomaly detection techniques, pre-emptive observability  can extrapolate expected application performance metrics, enabling teams to detect deviations or potential problems that have not yet impacted the application. By analyzing the tracing data, engineering teams can pinpoint the issue to the specific responsible code and commits.

The Cost of Fixing Production Issues

The true cost of issues with complex code in production is also becoming evident. Scaling issues are costly in terms of cloud storage and processing. Every issue prevented before deployment frees up team capacity, which can help accelerate software delivery. For example, for a 100-developer team that spends 40% of its time addressing production issues, reducing production problems by just 10% can reclaim 4% of the team's capacity. Additionally, reduced context-switching penalties add an extra 1.6%, leading to a total productivity gain of 5.6%.

A team that spends 25% of its time on production issues gains 0.25% capacity for every 1% reduction in production problems. If 20% of production issues are prevented in pre-production, the team reclaims 5% of its capacity.

Fixing production bugs is significantly more expensive than resolving them earlier. The Systems Sciences Institute at IBM reported that it costs 6x more to fix a bug found during implementation than to fix one identified during design. The institute also found that the cost to fix bugs found during the production phase could be 15x more than the cost of fixing those found during design. These increased costs result from reduced developer productivity, operational disruptions, end-user impact, and potential revenue losses.

Pre-emptive observability enhances pre-production testing by enabling research and development teams to detect latency spikes, resource overloads, and hidden dependencies before they cause real-world issues. It helps improve test coverage by identifying gaps and refining test cases while validating component interactions under realistic conditions.

This type of analysis also captures performance issues that traditional testing often misses. By tracking latency, throughput, and resource consumption during testing, teams can detect performance deviations before they impact users. Additionally, pre-emptive observability enables teams to simulate real-world conditions to stress-test applications effectively.

The Future of Trusted Complex Code Lies in Pre-emptive Technologies

Without proper safeguards, organizations risk exposing their customers and transactions to untested code. That's why pre-emptive validation is critical. Even well-tested code can fail in production, and every major production issue has, by definition, passed testing. Without a way to pre-emptively identify potential failures, companies can't responsibly rely on AI-generated code.

Pre-emptive technologies offer a crucial bridge between AI efficiency and production reliability, and they may be the key to enabling a future in which development teams can safely leverage AI's capabilities while maintaining high performance and reliability standards.

About the author:

Nir Shafrir is CEO at Digma. With over 15 years in the global enterprise technology industry, he specializes in building and accelerating sales/presale and BDR processes at companies transitioning from startup to mid-stage. Formerly VP of Global Field Engineering and Customer Success at Nyotron, Shafrir has experience across endpoint and network security starting from a medium-sized local security company in Israel through large companies as Trusteer (now an IBM company). He has held both technical and commercial positions in the information security space and has a deep understanding of the technical aspects of computer and network security, as well as promoting innovative approaches for product lines, elevating value for customers.