AI Logic Error Detector
Spot hidden logic flaws your tests might miss
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What do you do when the code runs perfectly and the answer is wrong? No error, no trace, no crash, just a total that is out by one every third time?
Logic errors are the expensive category. Nothing tells you they exist, so they survive review, ship, and get discovered by a customer six weeks later. The AI Logic Error Detector reads code that works and looks for the places where working is not the same as correct.
Short answer: The AI Logic Error Detector is a free AIToolsay tool that finds bugs in code that runs without errors. Paste the code with a description of what it should do, and it identifies where the behaviour differs from the intent, explains why and can produce a test that exposes it.
What is AI Logic Error Detector?
A logic error is code doing exactly what you wrote instead of what you meant. Off by one boundaries, an inverted condition, the wrong operator, a filter applied after an aggregation instead of before, a rounding step in the wrong place.
The prompt box asks you to paste the code and the error or behaviour you are seeing. That second half is doing the heavy lifting here. Without a statement of intent there is nothing to compare the code against, because the code is self consistent. It is just wrong.
Setting Issue Type to Logic Bug is what turns the analysis from "does this run" into "does this compute what was described".
Why Use AI Logic Error Detector?
Reviewing your own logic is unreliable for a simple reason. You read what you intended, not what is on the screen, because you know what it is supposed to do. A reader who only has the code and the stated intent does not have that blind spot.
The second reason is that logic errors cluster in predictable places. Boundaries, empty collections, negative numbers, time zones, rounding and the interaction between filters and totals. A systematic check of those areas finds more than a careful read of the whole file.
| Where logic errors hide | Typical symptom |
|---|---|
| Boundaries in a range | One record too many or too few |
| Inverted or partial conditions | Correct for most input, wrong for one category |
| Order of filter and aggregate | Totals that do not match the visible rows |
| Rounding applied per row | Sums that are a penny out |
| Time zone and date boundaries | Reports wrong for one hour a day or one day a month |
Note Write down what the code is supposed to do before you paste it, in your own words, including the edge cases. That sentence is the specification the analysis compares against, and a vague sentence produces a vague review.
How Does AI Logic Error Detector Work?
Prompt box. Open the AI Logic Error Detector and paste the code plus a plain description of the behaviour you expect.
Model selector. Choose the engine, from MSB AI, OpenAI ChatGPT, Google Gemini, Anthropic Claude AI, xAI Grok AI, DeepSeek, Qwen, Meta AI, NVIDIA AI, OpenRouter AI and MiniMax. Logic review is a good place to run two engines and compare.
Advanced options accordion. Ten controls: Language, Issue Type, Analysis Depth and Output as dropdowns, four toggles, a Thoroughness slider and a free text field.
Generate button. Code, intent, model and settings pass through the prompt engineering layer written for debugging, which is the instruction set that makes it compare behaviour against intent.
Output card. The findings appear under the button with a live word count, plus copy, listen, reuse, download and open in full view.
Export row. DOC, TXT and HTML on every result. DOC when the findings are going into a review comment.
Activity history. Session generations stay listed, so a first pass at standard thoroughness and a second at high thoroughness can be read side by side.
Key Features
Intent versus behaviour
The analysis compares what you said the code should do against what it actually computes.
Boundary focus
Empty collections, single elements, negatives, zero and the first and last item get checked deliberately.
A failing test
Show Test to Reproduce turns a suspicion into a test that either passes or does not.
Adjustable strictness
Thoroughness decides between a quick sanity check and a line by line examination.
Advanced Options Guide
| Option | What it controls | When to change it | Suggested start |
|---|---|---|---|
| Language | Auto Detect, Python, JavaScript, TypeScript, Java, C#, C++, Go, PHP or Ruby | Set it. Integer division, truthiness and sorting stability differ between languages | Your language |
| Issue Type | Auto Detect, Syntax Error, Runtime Error, Logic Bug, Performance, Memory Leak, Exception or Crash | Logic Bug is the setting that makes this tool do its job | Logic Bug |
| Analysis Depth | Quick, Standard or Deep | Deep for anything financial, date based or involving several conditions | Deep, because logic errors are not shallow |
| Output | Fix + Explanation, Corrected Code, Root Cause or Step by Step Debug | Step by Step Debug when you want to trace the values yourself | Fix + Explanation |
| Explain Root Cause | Explains the difference between what the code does and what you described | Leave on. This is the whole output | On |
| Provide Fixed Code | Returns the corrected implementation | Turn off until you have decided which behaviour is actually right | Off first, on once you agree |
| Suggest Prevention | Suggests assertions, types and tests that would have caught it | Leave on. Logic bugs recur unless a test pins the behaviour | On |
| Show Test to Reproduce | Writes a test that fails on the current behaviour | Always. A logic bug without a failing test is an opinion | On |
| Thoroughness | Slider from 1 to 100 controlling how exhaustively conditions and boundaries are checked | Raise it for money, dates and anything with nested conditions | 75 |
| Custom Instructions | Free text up to 1000 characters over the settings | Use it to state the rules the code is meant to implement | "Weeks start on Monday, refunds are exclusive of the day of purchase" |
Important A finding is a hypothesis until a test proves it. Some reported logic errors will be deliberate decisions you made for a reason. Read each one, check it against the rule you actually want, and only then change anything.
Example Outputs
Input: a function that calculates a refund amount, described as "refund the unused days of a membership term, counting from the day after cancellation to the end of the period".
Settings: Language Python, Issue Type Logic Bug, Analysis Depth Deep, Output Fix + Explanation, Show Test to Reproduce on, Thoroughness 80.
FINDING 1 (boundary)
The loop runs from cancellation_date to period_end inclusive
on both ends. Your description says counting starts the day
after cancellation, so every refund is one day too generous.
FINDING 2 (rounding)
The daily rate is rounded to two places before multiplying by
the number of days. On a 31 day period this loses up to 30
times the rounding error against the stated period price.
...The second finding is the more interesting one, because nothing about the code looks wrong. Rounding early is a completely normal thing to write, and it is the sort of error that produces a discrepancy nobody can explain for months.
Once the findings are agreed, pin them with the AI Test Case Generator so the behaviour cannot drift back.
Tips & Common Mistakes
- Write the intended behaviour first, in plain words, including edge cases.
- State the business rules explicitly. Week start, inclusive or exclusive dates, rounding direction.
- Set Issue Type to Logic Bug so the analysis is not distracted by style.
- Keep Thoroughness high. This is not the tool to run in Quick mode.
- Treat every finding as a question rather than a defect.
- Turn each confirmed finding into a test before you change the code.
What it catches well
- Boundary and off by one errors
- Conditions that are right for the common case only
- Rounding and ordering mistakes in calculations
- Filters and aggregations applied in the wrong order
What it cannot catch
- Rules you never described, since it compares against your words
- Bugs that live in code you did not paste
- Whether the requirement itself was wrong
- ✅ Intended behaviour written down before pasting
- ✅ Business rules stated, not implied
- ✅ Issue Type set to Logic Bug and Thoroughness high
- ✅ Each finding checked rather than accepted
- ✅ A failing test written before the fix
Comparison Table
| Approach | Catches logic errors | Cost |
|---|---|---|
| Compiler and type checker | Almost never. The code is valid | Free, already running |
| Code review by a colleague | Often, if they know the domain | Their time, and scheduling |
| Unit tests you wrote | Only the cases you thought of | Time to write them |
| AI Logic Error Detector | Boundaries and conditions, systematically | One generation, plus your judgement |
Pro tip Run it on code that has no known bug. This is not a repair tool, it is a review tool, and the best time to use it is on a finished feature before anyone depends on it. If the function is complicated enough that the review is hard to follow, the AI Code Complexity Explainer is worth a pass first.
AIToolsay is a free AI tools platform made of dedicated workspaces rather than one chat box with a long list of names. Each tool has its own prompt engineering and its own options panel, which is why this one asks about issue type and thoroughness rather than tone and length. All the tools are free and no account is needed to run one. The engine that answers is up to you, from MSB AI, OpenAI ChatGPT, Google Gemini, Anthropic Claude AI, xAI Grok AI, DeepSeek, Qwen, Meta AI, NVIDIA AI, OpenRouter AI and MiniMax, and for logic review two engines reading the same function often find different things. Sitting beside the tools are an AI directory, an AI models directory, courses, prompts, guides and news, all reachable from the AIToolsay homepage.
Frequently Asked Questions
Is the AI Logic Error Detector free?
Yes. It is free to use, nothing is installed, and no account is needed to run a review.
Why do I have to describe what the code should do?
Because a logic error is a gap between intent and behaviour. The code alone is self consistent, so without your description there is nothing to compare it against.
Will it find every logic bug?
No, and no tool will. It is systematic about boundaries, conditions, ordering and rounding, which is where most of them live. Treat it as a thorough reviewer rather than a guarantee.
What if a finding is actually intended behaviour?
That happens, and it is useful. It means the behaviour was surprising enough to be flagged, which is worth a comment in the code even if you change nothing.
Can I run it on code with no known bug?
Yes, and that is the best use. Running it as a review step on a finished feature is far cheaper than running it after a customer finds the problem.
Does it work on long files?
Work function by function. A focused review of one calculation with its rules stated beats a shallow pass over four hundred lines.
Should I always generate the reproducing test?
Yes. A logic finding without a failing test is an opinion, and a test is also the only thing that stops the same bug returning after a refactor.
Code that runs is not the same as code that is right, and nothing in your toolchain will tell you the difference. Write down what the function is meant to do, hand both to the AI Logic Error Detector, and turn every finding you agree with into a test before you touch a line.
Thanks for reading, and good luck with the review. If this becomes part of how you finish a feature, join the AIToolsay community, follow along on social media, turn on push notifications for new tools, and subscribe to the newsletter for the occasional round up.
Let AI Speak.
Sabir
Founder of AIToolsay and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
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