GenAI will not replace Static Analysis. Yes, GenAI can understand code in any language, and also knows about unsafe programming patterns. But it is not reliable enough to reason about bugs in your code, just like humans. Instead, it has other strengths that make it a powerful companion for Static Analysis.
In the past years, Generative Artificial Intelligence (GenAI) has become a key topic for software development. Nobody can afford to ignore it, since it can double productivity of some tasks. Moreover, it also expands developer capabilities significantly (according to this BCG study).
But is GenAI also an efficient way to find bugs in your code? Will it make Static Analysis useless? Let’s start with a self-experiment: Open a new browser window, and ask your favorite LLM to find a bug in a piece of code (here is a sample if you need one). Seriously, do it if you haven’t tried it already. I will wait here for you.
Are you back? What was your experience (you can find mine here)? It is easy to agree that GenAI can be used for finding bugs, and that it is very good with language. Even those spoken by computers. It explains well, perhaps sounds like an experienced developer, and the feedback seems very plausible.
It is very tempting to think that Static Analysis is no longer needed, and that we will just use AI from now on to find bugs. However, there is strong evidence suggesting that GenAI will not replace traditional Static Code Analysis any time soon, perhaps never. Here is why.
It’s not “A versus B”
First, I need to make an almost philosophical point: It makes little sense to say that GenAI will replace Static Analysis. Instead, we should see GenAI as a new method to perform Static Analysis.
As we have learned previously, the term “Static (Code) Analysis” simply means that a computer consumes code, analyzes it somehow, reports and potential bugs. The analysis methods may vary.
GenAI fits this definition perfectly: It transforms the inputs (code) to an output (bug list). It doesn’t run the code, hence it is static. With the right prompt, GenAI simply is a method for Static Analysis, and hence there is no argument whether GenAI will replace Static Analysis.
So let’s rephrase the question:
Correction: Does GenAI become the future method to perform Static Analysis?
I still have doubts. Let’s dive into several aspects.
Speed and Scalability
Speed: The speed of traditional Static Analysis can vary. Analyses based on “linting” methods are mainly based on (AST) pattern matching, and can be lightning fast. Others, which aim for mathematical proofs (that reminds me, I really should write about that soon…), can take longer. In all cases, the analysis time correlates with code complexity. More (complex) code takes longer to parse and analyse. Often, the relationship between code size/complexity and analysis time is somewhat polynomial.
GenAI is not much different. Longer inputs require more tokenization efforts, more context handling, and of course more server load. However, it is (at least to me, after some research) unclear whether there is any kind of deterministic scalability relationship. Resource-wise, today the required processing power for AI is higher than running a traditional method. Only very high-end developer machines can run GenAI at the moment.
Limits: traditional methods for Static Code Analysis are typically only limited by memory and CPU speed, and routinely consume millions of lines of code in one analysis. In contrast, pretty much all LLMs come with a token and/or bandwidth limit. That is, you cannot feed your whole code base into it. Therefore, GenAI can only be used on small parts of your program, and will miss some types of bugs.
Winner: Traditional Static Analysis methods.
Cost and Value for Money
Tool cost: Traditional Static Analysis tools are carefully crafted by homo sapiens. Development takes time, therefore, whoever spends time building such a tool, is likely to ask for monetary compensation.
In contrast, GenAI is now free. Yes, it cost massive dollars to build the technology, and to train the networks. But in the past 2 years, we can just to download, run, and tune our own LLMs. No charge, no license, no subscription necessary.
Running cost: As discussed in the previous section, you need more expensive hardware for GenAI compared to traditional Static Analysis, which could easily offset the running cost for tool licenses. Moreover, there is an open question of how much GenAI would improve our process, for example bugfix commits. While experts and consulting companies (McK, BCG) are bullish, there are also opposing studies that find developers possibly get slowed down by AI, because they spend more time reviewing AI output (Cornell).
Let’s summarize all of the running cost as “cost of ownership”, and admit that we don’t have enough data in the software community to know how that would pan out.
Winner: Tie
Applicability
Preconditions: When does the method work, when not? On what kind of code or programming language? What kind of bugs can it find? Traditional Static Analysis is always limited to languages and rules that were developed by a highly adaptable bipedal primate species. In contrast, GenAI just works on any programming language, with comments in Swahili, and can answer whatever verification question you pose (including: Does this code look sexy?). The only limitation is regarding scalability (see above – I won’t deduce points here again).
Handles Missing Parts: Just for completeness…traditional Static Analysis methods need the code to be compilable, but they allow some parts to be missing (e.g., missing function bodies). GenAI is even more laissez-faire and doesn’t care even if you have syntactic errors.
Winner: GenAI
Quality of Results
Accuracy: It is difficult to have a conclusion on who produces better results, and subjective, too. GenAI’s quality heavily depends on the prompt pattern, and it is clear that it can have both false positives and false negatives. One good example is Codex, which reaches good yet unsatisfying precision.
On the other hand, traditional Static Analysis methods have a clear goal or even a guarantee. Some methods can guarantee no False Negatives, others aim for low False Positives. We can make no such statements about GenAI. We don’t know. One more downside of GenAI comes from its limited scalability: Since it typically cannot digest full programs, it lacks global/project-level understanding, hence missing bugs.
Trained with buggy code: GenAI was trained on public code, and virtually all code has bugs. Additionally, most public code is not commercial or safety-critical, and hence not written to be compliant to coding guidelines or to be 100% robust. It is fair to assume that an AI-only verification approach will miss some bugs, or even introduce some.
Half-knowledge on coding standards: A common verification goal for Static Analysis is to check compliance to coding guidelines like MISRA C. Some of these guidelines are only available in exchange for money. Although we cannot be sure that the information in these documents is not leaked into the training data, or that inferences can be made from StackOverflow conversations and other things, it’s fair to assume that the GenAI’s knowledge on the standards is rather shaky.
Winner: Traditional Static Analysis methods.
Determinism & Trust
That’s an important aspect. We know that humans have many weaknesses, hence we must be able to trust the result of Static Analysis, otherwise it doesn’t add much value, or may even do harm.
Determinism
A.k.a. repeatability. If you a feed GenAI twice with the same inputs, you can expect different results (side note: Does that mean we are insane, according to Albert Einstein?). Now imagine you run the analysis after fixing something in your code, but warnings come and go randomly. Or you add some more analysis context to constrain data flows, and the result changes. Either it has changed because GenAI has understood the context, or it’s just random – hard to tell. That’s a big problem, which does not affect traditional Static Analysis methods that they follow strict algorithms.
Moreover, this non-determinism makes GenAI unsuitable for automation purposes. If you try to compare bugs in two software revisions (e.g., in a pull request), and also use this comparison as a decision criteria (e.g., pull request is denied because you added a new bug), then you need to be sure that the analysis produces reliable numbers. Traditional Static Analysis methods typically have this property.
Wrong, you might be objecting! LLMs have a temperature parameter that we can use to remove this kind of randomness. True, you can set the temperature to zero to get (more) deterministic output, but then you throw away all the positive features of GenAI. If you go and try this out, please let me know if you arrive at the same conclusion.
Trust
How much confidence can we place in the results? GenAI seems fancy, but nobody can fully explain how it comes up with an answer. That is a whole research domain called “explainable AI”, and we are waiting for answers. In contrast, traditional methods have decades of development time and scrutiny by end users, so I’d argue they are more trustworthy.
About two years ago, I did an experiment: I have tried to use a famous LLM chat bot to check my code for bugs. It was never sure about its answer, and could be easily convinced of the opposite. Just last week I have repeated that experiment with a more recent version. Today’s versions are always confident and somewhat stubborn, even when they are completely wrong. I am not the only one observing this — see Bruce Schneier’s blog article on that topic🔥, a highly recommended read.
Ambiguity of natural language and subtext: Human language is difficult, if not inherently buggy. Linguists know that, psychologists wrote books about that, and everyone who has friends or a spouse had some form of miscommunication. Natural language is simply not precise and there is always more than one interpretation (see Watzlawick’s 5 Axioms). For example, it makes a difference if you ask “find the bugs in this code” or “does this code contain any bugs” – the latter question implies that you expect there is one, and that bias may leak through to the AI, forcing it to find bugs where there aren’t any.
Hence, I see no way that we can trust the LLM-form of GenAI to do any better, especially not when the flawed human input generators have so much influence on the output. My conclusion: I can’t fully trust it, and it is not deterministic.
Winner: Traditional Static Analysis methods.
Developer Experience and Cool Use Cases
Now, an equally important aspect: How easy are both methods to use, and what else can they do?
Understandability: There should be no doubt that GenAI can always be more user-friendly than any deterministic Static Analysis method, since it has natural language output and conversation capabilities. It can adapt its language to the user, speak even Swahili, say things in different words, and explain every defect in arbitrary tone and depth. It can also be consulted to prioritize findings, which is a big plus because typically Static Analysis has many to throw at you.
Learning Curve: While traditional Static Analysis tools are easy to use these days, again, the natural language interface of GenAI is just more human. Just tell it what you want…you will get results. Of course, prompt engineering will be important, but this is easy enough to standardize or solve with templates.
Capabilities: Perhaps unfair to traditional Static Analysis methods, but we must admit that GenAI can just do more things than “just” finding bugs. Here are some use cases:
- Suggest bugfixes,
- write whole functions from scratch,
- document your code,
- write test cases,
- refactor big functions into smaller ones,
- and point out when you are getting tired.
That’s very impressive, so let’s grant a bonus point here, even though we know it can make mistakes. I’d argue that it surpasses already some some human developers.
Winner: GenAI (with bonus points)
Customizability & Tuning
What if you want to check for a specific property, or want the bugs reported in a format that fits your favorite spreadsheet (please don’t)? What if you want to give the tool some more context information, to refine the analysis? With traditional methods, you often have to do some hacks, write your own rules from scratch, or use specified APIs or annotations to convey more context. This is work. Work that can be buggy in itself.
In contrast, GenAI just needs a chat message from you, and can adapt in seconds. You are only limited by your imagination. I don’t think that traditional methods can win this fight.
Winner: GenAI
Security & Privacy
This is easy, too. Both traditional Static Analysis and GenAI can be executed offline, without sending your code anywhere. Sometimes people don’t know yet that this is possible with LLMs, so I wanted to make this clear.
Winner: Tie
The Verdict & The future
Here are the final scores:
| GenAI for Static Analysis | Traditional Methods for Static Analysis | |
|---|---|---|
| Speed & Scalability | 0 | 1 |
| Cost | 1 | 1 |
| Applicability | 1 | 0 |
| Quality of Results | 0 | 1 |
| Determinism & Trust | 0 | 1 |
| DevEx / Use Cases | 2 | 0 |
| Customizability | 1 | 0 |
| Security & Privacy | 1 | 1 |
| POINTS | 6 | 5 |
This comparison is a snapshot in 2025. It may change over time. GenAI is a baby compared to traditional Static Analysis, and getting better by the minute. Also, GenAI can be very different depending on which model you use. Everything I mentioned above is independent from the models, and hopefully I was fair.
However, there will never be a dominant winner. Traditional Static Analysis methods and GenAI have complementary properties that are unlikely to change over time (e.g. the linguistic weaknesses in LLMs, the scalability, the applicability). They balance each other’s weaknesses, and GenAI is a perfect extension to improve developer experience, while adding new capabilities like refactoring.
Hence, here is my prediction for the future: It is likely that we will see more AI to write better code from the first second. To explain code, to rewrite code, and to prioritize and understand warnings from Static Analysis. Copilots are a great help for things like that. But traditional Static Analysis methods will remain the best way to analyze your code for bugs, with all their ups and downs. They are the deterministic arbiter that we need to produce quality software, especially when Vibe Coding becomes more popular …