At Greptile, we focus on leveraging AI to improve code reliability through advanced bug detection capabilities. Detecting subtle and intricate software bugs is significantly more challenging than generating new code, as it requires not only pattern recognition but also deeper reasoning about code logic.
Recently, I evaluated two of OpenAI’s language models—o1-mini and o4-mini—to determine which performs better at identifying hard-to-find bugs within complex software systems.
Evaluation Setup
For a fair and comprehensive assessment, I introduced 210 realistic, challenging bugs across five widely-used programming languages:
- Go
- Python
- TypeScript
- Rust
- Ruby
Each bug was intentionally subtle, representative of real-world errors developers might overlook during typical code reviews, automated tests, and linting processes.
Results
Overall Performance
Overall, OpenAI o4-mini slightly outperformed o1-mini:
- OpenAI o4-mini: Identified 15 out of 210 bugs.
- OpenAI o1-mini: Identified 11 out of 210 bugs.
Though the numbers appear modest, the complexity of these deliberately subtle bugs underscores the significant challenge faced by current AI models in software verification.
Language-Specific Breakdown
Let's examine how each model performed by programming language:
-
Go:
- OpenAI o1-mini: 2/42 bugs detected
- OpenAI o4-mini: 1/42 bugs detected (o1-mini demonstrated stronger capability here)
-
Python:
- OpenAI o4-mini: 5/42 bugs detected
- OpenAI o1-mini: 2/42 bugs detected (o4-mini performed substantially better)
-
TypeScript:
- OpenAI o4-mini: 2/42 bugs detected
- OpenAI o1-mini: 1/42 bugs detected (Marginal difference, slight advantage o4-mini)
-
Rust:
- OpenAI o4-mini: 3/41 bugs detected
- OpenAI o1-mini: 2/41 bugs detected (Close performance, slight o4-mini advantage)
-
Ruby:
- Both models: 4/42 bugs detected (Equal performance)
Insights and Analysis
These results illustrate the differing strengths of the two models. OpenAI’s o4-mini, which incorporates explicit reasoning steps, appears particularly adept at handling languages like Python, where logic errors and nuanced syntax problems frequently occur. This reasoning component enables the model to logically deduce and simulate code execution, making it effective in detecting bugs beyond surface-level pattern recognition.
In contrast, o1-mini, a model primarily reliant on pattern matching, performed slightly better in Go, a language widely represented in training data and characterized by distinct idiomatic patterns. This indicates that traditional pattern-based models may excel in well-documented, structured environments, whereas reasoning-enhanced models excel in scenarios involving subtler, logic-driven errors.
The even performance in Ruby could reflect inherent complexities or specific coding patterns that neither model currently fully addresses, indicating areas for future model improvement.
Highlighted Bug Example: Async Keyword Misuse in Python
One particularly illustrative bug highlights the reasoning capabilities of o4-mini. In Python test #29, involving a bioinformatics toolkit, OpenAI o4-mini identified an asynchronous syntax error that o1-mini overlooked:
- OpenAI o4-mini’s Analysis:
"The code mistakenly usesawait self._calculate_distance_matrix(sequences)in a non-async method. Since_calculate_distance_matrixreturns a list synchronously, awaiting it results in a TypeError: 'list' object is not awaitable."
This subtle yet critical error demonstrates o4-mini’s reasoning ability—recognizing improper asynchronous usage by logically simulating the method's execution. OpenAI o1-mini’s inability to detect this bug underscores the advantage of reasoning-enhanced models in nuanced error detection scenarios.
Final Thoughts
Although both OpenAI models demonstrate meaningful bug detection capabilities, o4-mini’s embedded reasoning step clearly provides a promising advantage in detecting complex, logic-driven software errors. As AI continues evolving, models capable of sophisticated logical analysis, like OpenAI o4-mini, will likely become invaluable tools for developers, substantially improving software reliability and efficiency in the development process.