A new technology has emerged that turns non-deterministic properties into adjustable variables, allowing developers to perfectly reproduce bugs.
Imagine this: You are using an app on your smartphone, and the screen suddenly freezes. Frustrated, you tell the developer, “The app just stopped,” but the developer has no idea where or what to start fixing. Bugs (states where a program does not behave as intended or has missing functions) are common in software, but the most dreaded phrase for a developer is, “I can’t reproduce it” Reference 1.
Why does this happen? In many cases, it’s because the bug only appears on specific smartphone models or environments. It means that the general diagnostic tools (debuggers) or virtual environments (emulators) available to the developer cannot recreate the exact moment the bug occurred Reference 3. Today, we introduce an interesting tool that claims it will perfectly conquer these “non-reproducible bugs” that have been tormenting developers.
Why is this important?
To fix a bug, the first step is the process of recreating the “situation” where the bug appears Reference 2. However, reality is tough. Since countless users use apps in different environments, if you cannot accurately record the split second a bug occurs, it becomes very difficult to encounter that bug again Reference 4.
This new technology attempts to go beyond these limits of reproduction. This is because accurately reproducing bugs is an essential process for everyone protecting software quality, from novice testers to veteran developers Reference 5.
Understanding it easily
Simply put, this tool turns software into an “adjustable machine.”
The apps we use every day are so complex that it is hard to predict why a bug might occur. For example, if the screen breaks every time you change a filter in a photo editing app, the developer would need to check tens of thousands of cases, such as the order in which those filters are applied and the state of the memory at that time.
This tool turns the “non-deterministic properties” (the nature of changing randomly) that software possesses into “adjustable variables (knobs),” much like the sliders in a photo editing app Reference 9. By doing this, developers or AI can precisely recreate the exact point where a bug occurs, as if they were operating a machine Reference 13.
Metaphorically, it is like perfectly reconstructing a crime scene to catch a culprit. If previously it was impossible to know which way the culprit fled, you now possess a system that can accurately replicate all the conditions at the time of the incident (time, lighting, direction of the wind, etc.) and experiment again.
Current status
Currently, this technology is proving its powerful performance to the extent of finding bugs even in the database field (programs that store and manage data), one of the most meticulously tested software areas in the world Reference 9. Until now, developers have recorded screens to find bugs, analyzed log files for days, or persevered through countless repetitive tests Reference 7.
Now, we are entering an era of escaping from these grueling repetitive tasks and tracking bugs systematically through technical strategies Reference 5. Of course, it is not magic that solves all bugs immediately. The observation skills and the ability to grasp patterns by test experts remain highly important Reference 6.
What happens next?
In the future, the appearance of bug reports will change. Instead of vague reports simply stating “the app freezes,” reports will be generated that include the exact variable values that allow the developer to immediately reproduce the issue. To expand its ecosystem, this technology is offering $100 in free credits to its first 100 subscribers Reference 9. Developers will now be able to spend less time wrestling with bugs and pour more energy into creating better features.
MindTickleBytes AI reporter’s perspective
The time developers spend wrestling with bugs is one of the largest costs in the software ecosystem. This attempt to pull bugs out of the realm of “reproduction,” which depends on chance, and into the realm of “control,” where they behave as intended, will be a significant change that fundamentally raises code quality to the next level.
References
- How to make a bug more easily reproducible
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[Tips and Tricks - How to reproduce the bug if it is hard to reproduce? Software Testing Class](https://www.softwaretestingclass.com/tips-and-tricks-how-to-reproduce-the-bug-if-it-is-hard-to-reproduce/) -
[My Top 5 ways to reproduce a “Hard to Reproduce” Bug! Software Testing Tricks](https://www.softwaretestingtricks.com/2007/05/my-top-5-ways-to-reproduce-hard-to.html) - Ways to reproduce a “Hard to Reproduce” Bug!
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[Reproducible Test Environments: Bug Replication & Debug Guide bugpilot.io](https://bugpilot.io/2026/02/27/reproducible-test-environments-bug-replication-debug-guide/) - Steps to Reproduce a Not-Reproducible Defect in Testing
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[Reproducible Bug Techniques: 5 Ways to Reproduce Bugs in Software Testing bugpilot.io](https://bugpilot.io/2025/10/31/reproducible-bug-techniques-5-ways-to-reproduce-bugs-in-software-testing/) - Show HN: Make every bug perfectly reproducible
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[Show HN: Make every bug perfectly reproducible Hacker News](https://news.ycombinator.com/item?id=48607073) -
[Nuxt HN Show](https://hn.nuxt.space/show/1) -
[Nuxt HN Show HN: Make every bug perfectly reproducible](https://hn.nuxt.dev/item/48607073) -
[New Show Hacker News](https://news.ycombinator.com/shownew?next=48607670&n=31) - A VM designed to simulate… - SaaS Insight - roipad.com
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[Show Hacker News](https://news.ycombinator.com/show)
- A state that operates perfectly
- A state that is missing or behaves incorrectly
- Code for performance improvement
- Because the developer wrote the code too well
- Because they occur only on specific devices, making them hard to verify with a debugger
- Because the server is too fast
- Randomly deleting code
- Converting non-deterministic properties into adjustable variables
- Leaving it to developer's luck