Why We Spent 4 Years on Debugging

Introduction: The Silent Bottleneck

Over the past decade, AI has transformed software development workflows. Autocompletion tools like GitHub Copilot, code generation models like Codex, and retrieval-augmented generation pipelines have changed how developers interface with code.

But one layer of the stack remained conspicuously unsolved. Debugging.

Debugging remains the costliest and most time-intensive phase in the software lifecycle. It often consumes more than 50 percent of engineering time in production-scale systems. Yet most LLM-based developer tools treat it as a peripheral issue. Debugging is often seen as a downstream consequence of generation rather than a standalone, structured reasoning task.

At Kodezi, we have spent the last four years focused on this problem. But the story starts earlier.

Part 0: The 2018 Origin, BERT and the Limits of Autocomplete

Kodezi started in 2018 as a project built on fine-tuned Google BERT models trained on Java codebases. Our goal was to provide semantic autocompletions, inline bug suggestions, and simple test scaffolding by repurposing natural language techniques for code.

It worked. To a point.

BERT could understand syntax, surface similar past code, and produce plausible suggestions. But even in the early days, we noticed a blind spot. When something broke, the system had no idea what to do.

The original BERT-based Kodezi could assist with writing, but it could not help with repairing. And as the product scaled and users brought in more complex codebases, the failures became more obvious.

That was the inflection point. If we were serious about helping developers, we had to address the thing every engineer spends their time doing. Finding, diagnosing, and resolving bugs.

Part I: The Early Assumption That Completion Would Be Enough

Our earliest iterations followed the prevailing pattern. We built code generation pipelines, added documentation-aware retrieval, and fine-tuned transformers on PR diffs. These systems produced clean code and passed syntactic benchmarks.

But they consistently failed in production debugging.

When tested against real regressions such as CI failures, flaky integration tests, or broken dependency chains, the results were predictable.

• Superficial patches that bypassed symptoms

• Hallucinated logic disconnected from runtime behavior

• Fixes that introduced regressions elsewhere

These were not edge cases. They were common.

The failure was not in token prediction. It was in causal reasoning.

Part II: Why Debugging Is Different

Debugging is not code synthesis. It is applied inference across dynamic and distributed systems. Through failure analysis, we identified three reasons debugging breaks most LLMs.

1. Temporal and Structural Complexity

Bugs often result from interactions between modules and evolve across commits. Their resolution requires looking backward at diffs, logs, and stack traces, and outward across dependencies, call graphs, and tests.

2. Asymmetric Workload

While most LLM use cases are input-heavy with long prompts and short outputs, debugging is the reverse. Fixing a bug might start with a 200-token traceback but result in over 3,000 tokens of fixes, tests, documentation, and changelogs.

3. Iterative Verification

Fixing real bugs is not a one-shot operation. It is a feedback loop that involves proposing a change, running validations, and refining until resolution. Most language models trained on next-token prediction do not natively support this process.

Debugging required a system that could reason over time, structure, and outcomes. Not just generate plausible code.

Part III: Our First Three Models Failed

Before Chronos, we built and retired three major approaches.

Version 1: Diff-Based Fine-Tuning

Trained on bug-fix PRs. It could mimic changes, but failed at generalizing across unseen issues or multi-file causality.

Version 2: GPT-3.5 with Retrieval

Used embeddings to fetch related files and prompt GPT with them. Retrieval was useful, but generation lacked structural grounding. Fixes were often shallow and incorrect.

Version 3: Memory-Augmented Prompting

Introduced a retrieval memory layer with PR history and CI logs. Recall improved, but the system lacked a validation loop. Suggestions were not tested, and failure signals were not incorporated.

Each system failed for a different reason. But all shared the same core issue. They treated debugging as a language modeling task rather than a systems reasoning task.

Part IV: The Shift From Models to Systems

In 2023, we stopped treating debugging as an extension of code generation. We began designing Chronos as a dedicated debugging system, with its own architecture and constraints.

Chronos consists of:

• Memory Engine

A persistent graph-indexed memory of code, commits, logs, and previous fixes.

• Adaptive Graph-Guided Retrieval (AGR)

A retrieval engine that walks the memory graph to find semantically linked artifacts, guided by query complexity and confidence signals.

• Reasoning and Orchestration Core

A transformer trained specifically for debugging workflows. It performs root cause analysis, patch synthesis, test generation, and documentation. It is driven by a controller that loops through test validation until a fix is confirmed.

Part V: Why It Took Four Years

Chronos did not result from a single breakthrough. It emerged from sustained iteration and layered work across:

• Dataset construction: 15 million GitHub issues, 8 million stack traces, 3 million CI logs, all aligned to fix outcomes

• Benchmarking: Development of the Multi Random Retrieval (MRR) benchmark designed to test retrieval and reasoning across scattered signals

• Execution sandboxes: Allowing Chronos to test, observe, and refine its patches within CI-like environments

• Systemic ablations: Removing and reworking components that failed in real-world evaluations

We rebuilt Chronos multiple times. Not because the models were weak, but because the framing of the problem was wrong.

We were not building an assistant. We were modeling a debugging process.

Part VI: Where Chronos Stands Today

Chronos-1, our first full debugging-native model, achieves:

• 65.3 percent success rate on end-to-end debugging workflows

• 87.1 percent retrieval precision from memory graph traversal

• 2.2 average fix cycles, compared to over 6 for baseline models

• High cost-efficiency from adaptive inference and tight validation loops

Chronos operates silently inside developer environments. It maintains long-term memory. It proposes structured fixes. It explains its rationale. And it improves with each feedback cycle.

Final Thoughts: Debugging as a Frontier

Most developer tools help you write code faster.

Chronos is designed to help you sustain it.

As systems grow in scale and complexity, debugging will not remain a side task. It becomes the foundation of reliability and team velocity.

We spent four years on debugging not because it was glamorous, but because it was repeatedly neglected. Solving it required a new architecture, new evaluation criteria, and a shift in how we think about software maintenance.

Chronos was built for that purpose. Not just to generate code, but to reason through it.

The work is ongoing. But the foundation is here.