The Cost of Unresolved Errors

We’ve all been there before: a looming deadline, a pesky bug. Even worse, it’s intermittent. You can just feel the piercing glare of your tech lead at your Monday standup: “In progress? Still?”

Plenty of ink has been spilled on the topic of software errors, debugging, best practices, and the like. We can all agree that bugs are a major headache. (I know, what a revelation.) Unless you’re in a coding competition, it’s hard to find joy in the process of discovering, root-causing and fixing a bug in your code. There’s never a ‘good’ time for a bug to show up. Like reluctantly going to the gym or eating your vegetables, debugging and troubleshooting are things we do with sheer willpower. It gets in the way of our immediate or ultimate goals, be it shipping a feature before wrapping up for the night, or holding the team back from a major release.

And it’s not just that ‘ugh’ feeling that every developer is intimately familiar with. Studies show that debugging can take up anywhere from 33% to 50% of the typical engineering team’s time, which translates into significant delays and lost revenue for businesses.

While it’s no secret that software errors disrupt productivity, imagine having a tool that not only identifies bugs automatically but also suggests targeted fixes - dramatically reducing downtime and boosting your team’s efficacy. In this post, we’ll explore traditional bug triage software solutions and modern telemetry approaches - namely monitoring, observability, and distributed tracing - that, while once revolutionary, no longer meet today’s sophisticated development challenges. Finally, we’ll introduce CodeComet’s new offering, App Copilot, a tool designed to automatically detect and resolve bugs, so your team can focus on what truly matters.

If you’re ready to reclaim valuable development time and boost your team’s productivity, please read on!

Why Bug Triage Software Alone Falls Short

First, let’s take a quick trip down memory lane. As you can imagine, software errors have been around forever - as long as software itself has existed. (Don’t worry, we’ll spare you the Grace Hopper story about the first “bug” being an actual insect in the hardware.) Starting in the 90s, bug tracking - often referred to as bug triage - emerged at tech companies alongside modern development practices and tools like today’s IDEs. These early systems were designed to help bring order to increasingly complex codebases.

Here’s a simplified version of the bug triage process: a bug is discovered → its details are recorded in the system → context is added on how it manifested and how to replicate it → the bug gets assigned to a developer. Then the developer picks or is assigned when to work on it, and they do their best to interpret the bug report and recreate the conditions that generated the bug. Failing that, or in addition, the developer scours any logs related to the original issue for clues as to why the error occurred in the first place. This is often followed by an ad hoc cycle of guesswork, code changes, testing, and moments of sheer exasperation. Finally, the developer updates their resume and applies to other jobs while breathlessly muttering “Never again”. (Ok, we’re joking about that last part.)

Most importantly, bug triage software plays only a minor role - almost a forgettable cameo - in this software debugging process. It may help record and categorize issues, but it offers zero help where, for example, a missing key in a Python dictionary triggers a KeyError. In short, while these software tools were once transformative, they now resemble nothing more than glorified cloud-based note-taking apps. The heavy lifting of diagnosing and fixing errors falls squarely on the developer’s shoulders.

Why Monitoring and Observability Isn’t Enough

As bug triage software mainstreamed and proliferated in the 2000s, engineering teams ran up against a new challenge: a lack of context. Even the best dev teams could record errors, but they often lacked the details needed to understand the system’s state at the time of the error. As software systems grew more complex, these challenges spurred the near-universal adoption of telemetry tools in the 2010s. Monitoring kicked into high gear, with systems being developed to better track the real-time state of increasingly distributed applications.

Although logging had existed for a while by then, improvements in data storage and the need for greater clarity drove logging to evolve - from unformatted records to structured logs that served more precise snapshots of a system’s state. These advancements made it that much easier for developers to infer critical details from log lines, making bugs that much more approachable and ‘solvable’. However, eventually, logs started to show their age. They often lacked higher-level organization and system-wide consistency, prompting the invention of distributed tracing - essentially, supercharged logs that track code path execution across multiple services.

All these innovations ultimately got wrapped under the umbrella term of observability, and were instrumental (pun!) in helping teams better understand the inner workings of their systems. As a direct consequence of these techniques and tooling, development teams were able to more quickly discover issues in their systems, and they had a lot of records to pore through in order to help explain the unintended behavior of a bug or an issue.

And yet…even with robust telemetry systems offering extensive amounts of context, the hard work of actually resolving the issue still remained in the domain of the engineers. While these modern tools were great at monitoring applications, they stop short of pinpointing what is wrong with the code - leaving that final, critical work to the developer’s intuition and experience.

AI-Powered Code Analysis

Static code analysis tools have long been a staple in an engineering team’s toolkit. These sophisticated, rules-based engines parse through code to flag potential issues and enforce coding standards - but while valuable, they are inherently limited. While ideal for catching basic errors and enforcing coding standards, static analysis tools fall short when it comes to diagnosing runtime issues because of how they analyze code ‘statically’ - without any insight into how the application behaves during actual execution.

This is where AI eventually steps into the picture. Early AI-powered troubleshooting efforts harnessed the then AI-flavor-of-the-day, Machine Learning, and attempted to fix issues in code by training models on vast databases of documented errors and fixes. Unfortunately, such techniques never lived up to optimistic expectations, and these techniques never gained widespread adoption in industry.

Enter the latest AI breakthrough: Large Language Models (LLMs). These models ingest and process copious amounts of textual data, and somewhat magically are capable of extracting meaningful insights from it. While LLMs are known to be great at analyzing and generating natural language, they are equally impressive at analyzing and generating software code. (If it helps you digest this seemingly surprising fact, think of code as structured language.)

This brings us to AI-powered code analysis - the spiritual successor to static code analysis, although the two techniques share little genealogy. LLMs have proven that they excel at not only interpreting segments of code but even at generating new code. The key factor is that they draw on extensive training of vast repositories of open-source and public code, giving them an unprecedented level of contextual understanding.

Imagine the value this brings to the world of debugging and troubleshooting: a system that is not only adept at looking at code in the abstract to decipher faults and suggest improvements. When coupled with detailed error context, LLMs effectively assimilate and contextualize that information to drive meaningful debugging - essentially 'reasoning' through problems in a manner that bridges the gap between detection and resolution.

This new paradigm calls for new tooling - precisely what CodeComet has developed.

CodeComet’s Approach: From Telemetry to Code Fixes

CodeComet’s flagship product, App Copilot, is an innovative LLM-powered debugging agent that works in the background to automatically detect errors in your software and propose targeted code fixes - even generating a pull request. Conceptually, CodeComet combines together the two key pieces of the debugging puzzle: telemetry (for error context) and code analysis (for debugging).

CodeComet integrates seamlessly with your existing telemetry - currently Sentry and DataDog - to glean critical context from logs and traces. It also connects directly to your codebase via GitHub, enabling deep code inspection, traversal and analysis where needed.

When your monitoring system detects an error, CodeComet piggybacks on that signal to retrieve detailed error context. It then analyzes this data to generate a list of potential target modules and functions relevant to the error. Next, it pulls the relevant code segments and analyzes them for possible adjustments and enhancements to mitigate or fully address the error. The specific code patches are then processed and displayed to the end-user - either within our web app or via your team’s preferred collaboration platform, like Slack, Discord or Google Chat.

The best part? All of this happens almost as quickly as your existing tools notify you of an error - ensuring that a potential code fix is ready for review before you even finish reading the alert. Imagine this happening at scale across a team’s entire set of applications, or even across an entire engineering organization. And for bonus points, we can even generate a PR in one click, which gives developers a great starting point (and sometimes conveniently an endpoint) for reviewing and deploying a fix.

Keen readers will currently be wondering: don’t LLMs hallucinate or otherwise generate inaccurate results? Definitely, and we have a shared responsibility to account for that. CodeComet is designed with multiple internal checks - such as verifying that referenced code files and functions exist - to ensure that our system only proposes meaningful fixes. Similarly, our system is intentionally designed to not automatically deploy proposed remediations. We believe that fixes should always undergo human review. In a well-integrated workflow, CodeComet-generated pull requests will flow directly into your team’s existing code review and testing processes.

In summary, CodeComet is a powerful toolkit that monitors your software applications for errors (currently Python and JavaScript/TypeScript), and intelligently traverses your codebase to determine the root cause of the issue, resulting in a neatly packaged solution proposal for your dev team to review and deploy.

Conclusion

We hope you now appreciate the power of modern AI-powered code analysis and debugging systems like CodeComet. Deploying our system within your project or org equips your team with a tireless and omnipresent agent that swiftly detects errors and immediately goes to work analyzing and resolving them.

The key organizational benefits include:

• Enhanced Reliability and Stability: Your systems become increasingly resilient over time.

• Improved Developer Morale: Reduced context switching and tedious tasks enable your team to focus on innovation.

• Accelerated Development Cycles: Engineers can devote more time to higher-value tasks, speeding up feature releases.

At CodeComet, we are proud to offer one of the first specialized AI debugging agents. Leveraging cutting-edge LLM technology alongside proven telemetry and observability tools, our system seamlessly correlates errors from your logs with the relevant code segments. This combined context enables our system to generate actionable remediations that your developers can quickly review and deploy.

We hope you enjoyed this blog post. Stay tuned for future posts where we’ll dive deeper into our technological innovations and share more about our startup journey.

If you’re ready to significantly reduce the time between “error detected” and “fix deployed,” please sign up for a trial or book a demo today!