Can We Automate the Work of a Software Engineer? The Story Behind HEALER
In my previous article, I wrote about X-Ray — an observability system that grew out of my work on PAD+ AI. Read the X-Ray article on Habr Before X-Ray, I could see two things: Request → Response After X-Ray, every request became a detailed execution map. I could see individual phases, calls, timings, failures, state transitions, and causal relationships inside the pipeline. At first, that seemed…
In a recent article, the author delved into the workings of X-Ray, an observability system that emerged from their work on PAD+ AI. Initially, X-Ray allowed the author to visualize request and response patterns, revealing individual phases, calls, timings, failures, state transitions, and causal relationships within the system. However, this newfound visibility created a new challenge.
Once X-Ray began identifying problems autonomously, the author found themselves executing the same engineering loop repeatedly, day after day and week after week.
This realization sparked the idea for HEALER, a system designed to automate the engineering loop itself, rather than merely detecting and restarting failed components. The author sought to create a more comprehensive solution that could diagnose issues, propose fixes, verify results, and learn from the process.
To achieve this, HEALER was broken down into six distinct layers:
1. Diagnostics (Layer 1): This layer analyzes the execution data provided by X-Ray to identify known problem patterns, such as resource leaks, slow operations, or import-related issues.
2. Patch Engine (Layer 2): Upon detecting a problem, the Patch Engine generates potential code corrections. For instance, in the case of a file opening issue, it could transform a function like this:
Before:
```python
def read_config():
f = open("config.json", "r")
data = json.load(f)
return data
```
After:
```python
def read_config():
with open("config.json", "r") as f:
data = json.load(f)
return data
```
3. Verification (Layer 3): The generated patch undergoes syntax checks and passes through a series of tests to ensure its validity. If verification fails, the change is not implemented.
4. Orchestrator (Layer 4): This layer controls the overall cycle and determines the level of automation, ranging from monitoring and suggestion to automatic execution. Autonomous code modification should not be an all-or-nothing decision.
5. Meta-Learning (Layer 5): This layer records the results of successful and failed repairs, accumulating structured experience to inform future repair attempts and improve the system's performance over time.
The key takeaway from this story is that by automating the engineering loop, HEALER aims to transform a repetitive process into an executable system. It moves beyond simply fixing bugs to become an integral part of the software development workflow, learning from each iteration to improve its performance and effectiveness.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
