少点错误 2024年07月04日
Static Analysis As A Lifestyle
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本文以法国顶级大厨节目为例,探讨了静态分析在各个领域中的应用。静态分析是指在不运行程序或实验的情况下,通过分析代码或数据来发现错误或潜在问题的方法。文章强调了静态分析在各个领域(如烹饪、物理、化学、药物测试、动画等)中的应用,并指出静态分析的局限性以及它与创新之间的关系。

👨‍🍳 静态分析在烹饪中的应用:以法国顶级大厨节目为例,经验丰富的厨师可以通过观察食材和菜品的组合,预测菜肴的成功与否。他们利用自身的经验和知识,模拟菜肴的制作过程,找出潜在的错误,例如缺乏某种口感、味道过重或缺乏复杂性等。

🧪 静态分析在其他领域的应用:除了烹饪,静态分析也广泛应用于其他领域,例如物理学中的量纲分析和数量级估计,化学中的化学方程式平衡,药物测试中的受体结合分析,工程学中的模拟和估算,以及动画中的半满面粉袋测试。

💡 静态分析的局限性:虽然静态分析可以帮助我们发现错误和潜在问题,但它并非万能的。在实际应用中,静态分析技术通常是不完备的,这意味着它可能无法识别所有错误或潜在问题。此外,静态分析方法往往会限制我们的创造力,因为为了进行分析,我们需要对问题进行简化和抽象,这可能会导致我们错过一些潜在的可能性。

🎨 静态分析与创新:静态分析虽然存在局限性,但它仍然是许多领域中重要的工具。它可以帮助我们避免重大错误,并为创新提供基础。通过不断探索和突破静态分析的局限性,我们可以获得新的知识和技能,从而推动创新。

🚀 静态分析的价值:静态分析的价值在于它可以帮助我们更有效地理解和解决问题。通过对问题的分析和预测,我们可以更好地做出决策,并避免潜在的错误。同时,静态分析也可以为我们提供新的思路和方法,帮助我们更好地理解和探索世界。

Published on July 3, 2024 6:29 PM GMT

I’ve been watching French Top Chef (the best Top Chef, fight me) with my wife again, and I’m always impressed by how often the mentoring chefs, all with multiple michelin stars and years of experience, can just guess that a dish will work or that it will be missing something.

By far, whenever a chef points to an error (not a risk, an error), it’s then immediately validated experimentally: either the candidate corrected it and the jury comments positively on that aspect of the dish, or they refused to and failed because of that aspect of the dish.

Obviously, this incredible skill comes from years of cooking experience. But at its core, this is one of the fundamental idea of epistemology that experts and masters rediscover again and again in their field: static analysis.

The core intuition of static analysis is that when you write a computer program, you can check some things without even running it, just by looking at it and analyzing it.

What most programmers know best are type systems, which capture what can be done with different values in the program, and forbid incompatible operations (like adding a number and a string of characters together, or more advanced things like using memory that might already be deallocated).

But static analysis is far larger than that: it include verifying programs with proof assistants, model checking where you simulate many different possible situations without even running tests, abstract interpretation where you approximate the program so you can check key properties on them…

At its core, static analysis focuses on what can be checked rationally, intellectually, logically, without needing to dirty your hands in the real world. Which is precisely what the mentoring chefs are doing!

They’re leveraging their experience and knowledge to simulate the dish, and figure out if it runs into some known problems: lack of a given texture, preponderance of a taste, lack of complexity (for the advanced gastronomy recipes that Top Chef candidates need to invent)…

Another key intuition from static analysis which translates well to the Top Chef example is that it’s much easier to check for specific failure modes than to verify correctness. It’s easier to check that I’m not adding a number and a string than it is to check that I’m adding the right two number, say the price of the wedding venue and the price of the DJ.

It’s this aspect of static analysis, looking for the mistakes that you know (from experience or scholarship, which is at its best the distilled experience of others), which is such a key epistemological technique.

I opened with the Top Chef example, but almost any field of knowledge, engineering, art, is full of similar cases:

But there’s something even deeper about these checks: they are often incomplete.

In technical terms, a static analysis technique is complete if it accepts every correct program (and sound if it rejects all incorrect programs, but that’s not the main point here).

Of course, there are no truly complete techniques in practice. This is because restrictions are powerful: the more you constrain what you and others can do, the easier it is to reason about it, and check much more properties. So type systems often forbid programs that would actually run without runtime errors, but which are completely messed up to think about. That way, they can provide more guarantees.

And this is even truer in much fuzzier fields like art. Most of the rules in art are not actual perfect boundaries outside of which everything is wrong. They’re instead starting points, first approximations.

If you restrict yourself to perfect intervals, like traditional western music for hundreds of years, it’s safe to predict your piece of music will work decently well.

Progress then manifests itself by an epistemic exploration of this rule, a curious probing, looking for which parts can be skirted, bent, broken, while still reaching your end. This leads to things like Jazz, which breaks most of the harmonic and rhythmic code of western traditional music. And it’s great!

Which is why maybe the most exciting part of watching Top Chef are these moments of grace where the chefs and the jury doubt the intuition of the candidate, but the latter just go “Nah, I can get away with that.”, and realizes it so well that it explodes the previous rule.

That’s not to say that these rules are useless. Once again, the productive position is rarely in the false dichotomy between the absolute rule and the useless constraint, but in the perpetual interplay between making the world simpler so you can reason more about it before acting, and just trying things out to push your model of what can work.



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