{"slug": "can-we-automate-the-work-of-a-software-engineer-the-story-behind-healer", "title": "Can We Automate the Work of a Software Engineer? The Story Behind HEALER", "summary": "A developer has built HEALER, a system that automates the software engineering loop by detecting, diagnosing, and fixing problems in code. Building on the X-Ray observability system, HEALER uses a layered architecture with an AST-based patch engine and verification to propose and apply fixes, aiming to automate the engineering process itself.", "body_md": "In my previous article, I wrote about **X-Ray** — an observability system that grew out of my work on PAD+ AI.\n\n[Read the X-Ray article on Habr](https://habr.com/ru/articles/1057236/)\n\nBefore X-Ray, I could see two things:\n\n```\nRequest → Response\n```\n\nAfter X-Ray, every request became a detailed execution map.\n\nI could see individual phases, calls, timings, failures, state transitions, and causal relationships inside the pipeline.\n\nAt first, that seemed to solve the problem.\n\nIt didn't.\n\nIt created a new one.\n\nAfter X-Ray appeared, I stopped looking for problems manually.\n\nX-Ray was finding them for me.\n\nBut then I noticed something strange.\n\nEvery time it reported a problem, I was doing almost exactly the same thing:\n\n```\nRead the diagnostic\n      ↓\nFind the cause\n      ↓\nDesign a fix\n      ↓\nApply the change\n      ↓\nRun tests\n      ↓\nKeep or rollback\n```\n\nThen repeat.\n\nDay after day.\n\nWeek after week.\n\nAnd eventually I realized:\n\n**I wasn't making a new engineering decision every time. I was repeatedly executing the same algorithm.**\n\nThat led to a much more interesting question:\n\nIf a system can already observe and diagnose its own problems, why couldn't it execute the engineering loop as well?\n\nThat question became the starting point for **HEALER**.\n\nThe term *self-healing* is already widely used.\n\nUsually it means something relatively simple:\n\n```\nFailure → Detect → Restart\n```\n\nA crashed container gets restarted.\n\nA failed service gets recreated.\n\nA node disappears and another one takes over.\n\nUseful, but I was interested in something different.\n\nI wanted to automate the **engineering process itself**.\n\nNot just:\n\n\"Something failed. Restart it.\"\n\nBut:\n\n\"Something is wrong. Find out why. Propose a change. Apply it safely. Verify the result. Roll back if necessary. Remember what happened.\"\n\nSo before writing the implementation, I wrote down the cycle.\n\n```\n1. Detect a problem\n       ↓\n2. Diagnose the cause\n       ↓\n3. Generate a fix\n       ↓\n4. Verify the fix\n       ↓\n5. Roll back if verification fails\n       ↓\n6. Learn from the result\n```\n\nOnce the process was explicit, the architecture became much easier to design.\n\nInstead of building one giant \"AI repair agent\", I split HEALER into independent layers.\n\n```\nLayer 5: Meta-Learning\n         ↑\n         │ remembers results\n         │\nLayer 4: Orchestrator\n         ↑\n         │ controls the cycle\n         │\nLayer 3: Verification\n         ↑\n         │ tests the proposed change\n         │\nLayer 2: Patch Engine\n         ↑\n         │ generates code changes\n         │\nLayer 1: Diagnostics\n         ↑\n         │ detects and analyzes problems\n         │\nLayer 0: X-Ray Kernel\n         │\n         └── execution evidence\n```\n\nEach layer has a specific responsibility.\n\nX-Ray provides the execution evidence.\n\nIt records what happened inside the application: phases, timings, errors, relationships and execution state.\n\nHEALER doesn't have to guess what happened.\n\nIt can start with a trace.\n\nDiagnostics analyzes the available execution data and looks for known classes of problems.\n\nThe current implementation contains multiple detectors covering issues such as resource leaks, slow operations, import-related problems and causal violations.\n\nThe important part is the separation:\n\n**X-Ray observes. Diagnostics interprets.**\n\nOnce a problem has been identified, HEALER needs to construct a possible correction.\n\nFor Python code, the Patch Engine can work with the **AST (Abstract Syntax Tree)** rather than blindly modifying text.\n\nThat gives the system a structural representation of the code it is modifying.\n\nA generated patch is not considered successful simply because it was generated.\n\nIt has to survive verification.\n\nThe basic principle is:\n\n```\nPatch\n ↓\nSyntax check\n ↓\nTests\n ↓\nResult\n```\n\nIf verification fails, the change should not remain in the codebase.\n\nThe Orchestrator controls the overall cycle and determines how HEALER operates.\n\nThe architecture supports different levels of automation, including monitoring, suggestion and automatic execution.\n\nThis is important because **autonomous code modification should not have to be an all-or-nothing decision**.\n\nThe final layer records what happened.\n\nA successful repair is useful information.\n\nA failed repair is useful information too.\n\nThe goal is not to make the system magically \"smarter\", but to accumulate structured experience that can influence future repair attempts.\n\nThe strangest part wasn't the first automatically generated patch.\n\nIt was realizing that the complete engineering loop could be executed without me manually performing every step.\n\nPreviously:\n\n```\nX-Ray\n  ↓\nI read the report\n  ↓\nI found the cause\n  ↓\nI wrote the fix\n  ↓\nI ran the tests\n  ↓\nI rolled back if necessary\n```\n\nWith HEALER:\n\n```\nX-Ray\n  ↓\nDiagnostics\n  ↓\nPatch Engine\n  ↓\nBackup\n  ↓\nVerification\n  ↓\nMeta-Learning\n  ↓\nRollback if necessary\n```\n\nThe difference is subtle but important.\n\nHEALER isn't simply another component that \"fixes bugs\".\n\nIt is an attempt to turn a repeated engineering workflow into an **executable system**.\n\nConsider a simple resource leak.\n\nSuppose a diagnostic detects that a function opens a file without reliably closing it.\n\n``` python\ndef read_config():\n    f = open(\"config.json\", \"r\")\n    data = json.load(f)\n    return data\n```\n\nThe diagnostic identifies the problematic pattern.\n\nThe Patch Engine can transform the structure of the code into:\n\n``` python\ndef read_config():\n    with open(\"config.json\", \"r\") as f:\n        data = json.load(f)\n    return data\n```\n\nBut generating this code is only the middle of the process.\n\nHEALER then has to deal with the consequences.\n\nBefore modifying the file, the original version is preserved.\n\nFor example:\n\n```\nread_config.py\nread_config.py.healer.bak\n```\n\nThe backup provides a recovery point.\n\nThe modified code is checked.\n\nAt minimum, the system needs to establish that the generated code is syntactically valid and that the relevant tests pass.\n\n```\nGenerated patch\n      ↓\nSyntax validation\n      ↓\nTests\n      ↓\nPASS\n```\n\nOnly then is the repair considered successful.\n\nNow consider the opposite scenario.\n\nThe generated patch causes a test failure.\n\nThe process becomes:\n\n```\nPatch\n ↓\nVerification\n ↓\nFAIL\n ↓\nRollback\n ↓\nOriginal code restored\n```\n\nThis is one of the most important principles of the entire system.\n\n**Autonomous modification without autonomous verification is dangerous.**\n\nThe ability to undo a change is therefore not an optional feature. It is part of the architecture.\n\nThis distinction matters.\n\nHEALER does **not** mean:\n\n\"An AI programmer that can independently build any software.\"\n\nThat's not what I am claiming.\n\nThe current system automates a much narrower and more measurable problem:\n\nCan a system observe a known class of engineering problem, diagnose it, construct a candidate repair, verify the repair, and recover safely when the repair fails?\n\nThat is a much more interesting engineering question.\n\nAnd it is testable.\n\nHEALER didn't appear as an isolated project.\n\nIt emerged from **PAD+ AI → X-Ray → HEALER**.\n\nThe progression was almost inevitable:\n\n```\nPAD+ AI\nCognitive architecture\n       ↓\nX-Ray\nObserve execution\n       ↓\nHEALER\nAct on detected problems\n```\n\nPAD+ AI provided the complex execution environment.\n\nX-Ray provided visibility.\n\nHEALER became the experimental layer that could act on that visibility.\n\nThis separation is important because observability and automated intervention should not be the same thing.\n\n**X-Ray observes. HEALER acts.**\n\nHEALER is integrated into the PAD+ AI platform and can be inspected through the application interface.\n\nThe HEALER section currently exposes several parts of the system.\n\nShows what HEALER learned from previous cycles and what changes were made.\n\nHEALER activity is itself observable through the X-Ray tracing channel.\n\nThat means the system doesn't have a blind spot simply because the component performing the repair is autonomous.\n\nShows the current operating mode and system state.\n\nAllows the automatic diagnostic/repair cycle to be enabled and monitored.\n\nThe frontend also receives events through WebSocket, including detector activity and cycle results.\n\nSo the goal isn't to create a mysterious autonomous process running somewhere in the background.\n\nThe goal is almost the opposite:\n\n**make the autonomous process observable.**\n\nThis is probably the most important architectural lesson I learned while building the system.\n\nImagine giving an automated repair system permission to modify code without first having a reliable picture of what happened.\n\nYou would have:\n\n```\nUnknown problem\n      ↓\nUnknown reasoning\n      ↓\nAutomatic modification\n```\n\nThat is a dangerous combination.\n\nWith X-Ray:\n\n```\nObserved execution\n      ↓\nDiagnostic evidence\n      ↓\nCandidate repair\n      ↓\nVerification\n      ↓\nControlled modification\n```\n\nThe repair mechanism is therefore built **on top of evidence**, rather than operating entirely from assumptions.\n\nBefore allowing a system like this to modify code, the obvious question is:\n\n**Does the repair cycle itself work?**\n\nThe current HEALER implementation has been tested with:\n\nThe current implementation is also designed to be self-contained, using Python's standard library rather than requiring a large external runtime.\n\nThese numbers are not proof that autonomous software engineering is solved.\n\nThey are simply evidence that the current experimental implementation can execute and test the intended workflow.\n\nAnd that distinction matters.\n\nThere is an easy trap here.\n\nOnce you see a system successfully detect a resource leak, generate a patch, run tests and keep the result, it is tempting to conclude:\n\n\"We've built a self-programming AI.\"\n\nWe haven't.\n\nA controlled repair of a known problem class is very different from general software engineering.\n\nReal software contains:\n\nA passing test suite does not automatically mean that an architectural decision was correct.\n\nThat is exactly why HEALER is still an **experimental research platform**, rather than a claim that autonomous programming has been solved.\n\nX-Ray answered one question:\n\nWhat happened inside the system?\n\nHEALER started answering another:\n\nCan the system act on what it observes?\n\nBut this creates a much harder question.\n\nIf the system can:\n\nthen eventually we have to ask:\n\nWho decides what should be changed in the first place?\n\nA detector can tell us that something is wrong.\n\nA patch engine can propose a correction.\n\nA test can tell us whether the correction passes a defined validation.\n\nBut none of those things necessarily tells us whether the **architecture itself should change**.\n\nAnd that is where the next layer of the problem begins.\n\nThe evolution of the project currently looks like this:\n\n```\nPAD+ AI\n   │\n   ├── Cognitive Architecture\n   │\n   └── X-Ray\n         │\n         └── Observability\n                │\n                └── HEALER\n                      │\n                      ├── Diagnostics\n                      ├── Patch Engine\n                      ├── Verification\n                      ├── Rollback\n                      └── Meta-Learning\n```\n\nThe next question isn't simply:\n\n\"Can we make HEALER repair more bugs?\"\n\nIt is much more fundamental:\n\nCan an AI system distinguish between a local implementation problem and a deeper architectural problem?\n\nThat's where I want to take the research next.\n\nAnd this is also where I am looking for other engineers and researchers who want to experiment with the system rather than simply watch a demo.\n\nPAD+ AI is an open research platform.\n\nThe goal isn't to build another AI chatbot.\n\nThe goal is to investigate what happens when an LLM-based system is surrounded by explicit architecture for memory, state, observability, verification and controlled evolution.\n\nIf you're interested in:\n\nI'd be much more interested in **your experiments, criticism and pull requests** than in people simply clicking through the demo.\n\nThe HEALER repository is currently private while the architecture is being stabilized, but I can provide access to people who want to study the implementation or contribute.\n\n**📚 Read the full PAD+ AI series:**\n\n**🌐 PAD+ AI — live platform:**\n\n[Open PAD+ AI on Render](https://pad-plus-ai.onrender.com/)\n\n**💻 PAD+ AI repository:**\n\n[PAD+ AI on GitHub](https://github.com/Ovladimirovich/pad-plus-ai)\n\n**💻 X-Ray integration kit:**\n\n[X-Ray Integration Kit on GitHub](https://github.com/Ovladimirovich/xray-integration-kit)\n\nThe X-Ray repository is currently private. If you want to examine the architecture or participate in its development, contact me via Telegram and I can provide access to interested contributors.\n\n**💻 HEALER:**\n\n[https://github.com/Ovladimirovich/Healer.git](https://github.com/Ovladimirovich/Healer.git)\n\nThe HEALER repository is currently private. If you want to examine the architecture or participate in its development, contact me via Telegram and I can provide access to interested contributors.\n\n**💬 Telegram:**\n\n[PAD+ AI Telegram](https://t.me/padplusai)\n\nWhen I started PAD+ AI, I wanted to understand whether an LLM could become part of a larger cognitive architecture.\n\nX-Ray came from the need to understand what that architecture was actually doing.\n\nHEALER came from the realization that once you can observe a complex system, you can begin asking whether some of the work of maintaining it can itself be automated.\n\nNow I am left with a harder question:\n\n**If an AI system can eventually diagnose, modify and verify its own code — where does the engineer's job actually begin and end?**", "url": "https://wpnews.pro/news/can-we-automate-the-work-of-a-software-engineer-the-story-behind-healer", "canonical_source": "https://dev.to/_a9de0f38ed294cfb7e5e/can-we-automate-the-work-of-a-software-engineer-the-story-behind-healer-2mge", "published_at": "2026-08-22 00:45:20+00:00", "updated_at": "2026-08-22 01:14:54.070398+00:00", "lang": "en", "topics": ["artificial-intelligence", "developer-tools", "ai-agents"], "entities": ["HEALER", "X-Ray", "PAD+ AI"], "alternates": {"html": "https://wpnews.pro/news/can-we-automate-the-work-of-a-software-engineer-the-story-behind-healer", "markdown": "https://wpnews.pro/news/can-we-automate-the-work-of-a-software-engineer-the-story-behind-healer.md", "text": "https://wpnews.pro/news/can-we-automate-the-work-of-a-software-engineer-the-story-behind-healer.txt", "jsonld": "https://wpnews.pro/news/can-we-automate-the-work-of-a-software-engineer-the-story-behind-healer.jsonld"}}