Monthly Archives: December 2013

Tests and static analysis

Ever since I made a static analysis tool for Python called PySonar, I have been asked about the question: “What is the difference between testing and static analysis?” When I worked at Coverity, my coworkers told me that they also spent quite some time explaining to people about their difference. My answer to this question evolves as my understanding of this area deepens. Recently I replied to a comment asking a similar question, so I think it’s a good time to write down some systematic answer for this question.

Static analysis is static, tests are dynamic

Static analysis and tests are similar in their purposes. They are both tools for improving code quality. But they are very different in nature: static analysis is (of course) static, but tests are dynamic. “Static” basically means “without running the program”.

Static analysis is similar to the compiler’s type checker but usually a lot more powerful. Static analysis finds more than type errors. It can find defects such as resource leaks, array index out of bounds, security risks etc. Advanced static analysis tools may contain some capabilities of an automatic theorem prover. In essence a type checker can be considered a static analysis with a coarse precision.

Static analysis is like predicting the future, but testing is like doing small experiments in real life. Static analysis has the “reasoning power” that tests hasn’t, so static analysis can find problems that tests may never detect. For example, a security static analysis may show you how your website can be hacked after a series of events that you may never thought of.

On the other hand, tests just run the programs with certain inputs. They are fully dynamic, so you can’t test all cases but just some of them. But because tests run dynamically, they may detect bugs that static analysis can’t find. For example, tests may find that your autopilot program produces wrong results at certain altitude and speed. Static analysis tools can’t check this kind of complex dynamic properties because they don’t have access to the actual running situation.

But notice that although tests can tell you that your algorithm is wrong, they can’t tell you that it is correct. To guarantee the correctness of programs is terribly harder than tests or static analysis. You need a mechanical proof of the program’s correctness, which means at the moment that you need a theorem prover (or proof assistant) such as Coq, Isabelle or ACL2, lots of knowledge of math and logic, lots of experience dealing with those tools’ quirks, lots of time, and even with all those you may not be able to prove it, because you program can easily encode something like the Collatz conjecture in it. So the program’s passing the tests doesn’t mean it is correct. It only means that you haven’t done terribly stupid things.

Difference in manual labor

Testing requires lots of manual work. Tests for “silly bugs” (such as null pointer dereference) are very boring and tedious to make. Because of the design flaws of lots of programming languages, those things can happen anywhere in the code, so you need a good coverage in order to prevent them.

You can’t just make sure that every line of the code is covered by the tests, you need good path coverage. But in the worst case, the number of execution paths of the program is exponential to its size, so it is almost impossible to get good path coverage however careful you are.

On the other hand, static analysis is fully automatic. It explores all paths in the program systematically, so you get very high path coverage for free. Because of the exponential algorithm complexity exploring the paths, static analysis tools may use some heuristics to cut down running time, so the coverage may not be 100%, but it’s still enormously higher than any human test writer can get.

Static analysis is symbolic

Even when you get good path coverage in tests, you may still miss lots of bugs. Because you can only pass specific values into the tests, the code can still crash at the values that you haven’t tested. In comparison, static analysis processes the code symbolically. It doesn’t assume specific values for variables. It reasons about all possible values for every variable. This is a bit like computer algebra systems (e.g. Mathematica) although it doesn’t do sophisticated math.

The most powerful static analysis tools can keep track of specific ranges of the numbers that the variables represent, so they may statically detect bugs such as “array index out of bound” etc. Tests may detect those bugs too, but only if you pass them specific values that hits the boundary conditions. Those tests are painful to make, because the indexes may come after a series of arithmetic operations. You will have a hard time finding the cases where the final result can hit the boundary.

Static analysis has false positives

Some static analysis tools may be designed to be conservative. That is, whenever it is unsure, it can assume that the worst things can happen and issue a warning: “You may have a problem here.” Thus in principle it can tell you whenever some code may cause trouble. But a lot of times the bugs may never happen, this is called a false positive. This is like your doctor misdiagnosed you to have some disease which you don’t have. Lots of the work in building static analysis tools is about how to reduce the false positive rate, so that the users don’t lose faith in the diagnosis reports.

Tests don’t have false positives, because when they fail your program will surely fail under those conditions.

The value of static analysis

Although static analysis tools don’t have the power to guarantee the correctness of programs, they are the most powerful bug-finding tools that don’t need lots of manual labor. They can prevent lots of the silly bugs that we spend a lot of time and energy writing tests for. Some of those bugs are stupid but very easy to make. Once they happen they may crash an airplane or launch a missile. So static analysis is a very useful and valuable tool. It takes over the mindless and tedious jobs from human testers so that they can focus on more intellectual and interesting tests.

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Posted by on December 27, 2013 in static analysis, testing


On object-oriented programming

[written at the end of 2013 AD, during the Dark Ages of programming]

The programmer’s world is full of fads and superstitions. Every now and then there will be somebody who come up and announce: “I can save the world!” No matter how bad the ideas are, there will always be followers, and the ideas soon become their religion. They then develop their community or camp, try to let those ideas dominate the world, and try to make the ideas live forever.

Object-oriented programming (OOP) is such a religion. It claimed to be able to save the world from the so-called “software crisis”, but as a hindsight after so many years since it was introduced, not only didn’t OOP save us, it brought us way more confusion and harm than benefits. Unfortunately its dogmas and mispractices have become so wide-spread and deeply intrenched. In this article, I hope to provide my viewpoint into this matter and try to find out the lessons that we can learn.

Like every article on my blog, the opinions are completely personal and not representing my employers or professors.

Is everything an object?

“Everything is an object” is the core dogma of OOP and deemed as the highest standards of OO language design. Now let’s take a careful look to see if it is true, or if it is a good idea to make things that way.

Many people take “everything is an object” for granted because when this sentence is taken literally it matches their everyday experience. Since the word “object” in English basically means “a thing”, how can “everything is an object” be not true? But be careful since the definition of an “object” in OOP has a specific meaning which is very different from its meaning in English.

OOP’s definition of an object is “a combination of data fields and associated procedures known as methods“. Can you really fit everything into this model?

First let’s look at the real world and see if this definition can capture everything. Cars, trees, animals may sometimes be thought of as objects, but what about a change of the objects’ position, its velocity and duration? What methods do they have? Well, you may define classes called Velocity or Time, with methods such as addition, but do velocity and time really contain the things that you call “methods”? They don’t. They are just your imagination. You can add the velocities or time, but how can velocities or time contain the addition procedure? This is like saying that the bullets contain the gun.

So the most you can say is that “everything is an object” is a good way of thinking, but that is not true either. The definition of an object implies that a method can only belong to one object, but most of the time it doesn’t make sense thinking of it as belonging to any object. Say we have the expression 1+2, does the operator ‘+’ belong to 1, or does it belong to 2? You have to make some arbitrary choice. Since you can make a choice, this means that the ‘+’ operator doesn’t really belong to either of them. The operation is inherently outside of the objects.

So thinking of some things as objects may be helpful, but thinking of everything as an object is neither true nor useful. Unfortunately “everything is an object” has been taken as a dogma and the highest standard of OO language design. Some OO languages claim that everything is an object in them. Whenever you notice that something is not an object, somebody will try to make it one. They may succeed in that, but things get very complicated that way, because that’s not how things work.

The idealism of “everything is an object” is similar to “everything is a function” in the functional programming world and “everything is a set” in the math world. Before computer science was conceived there was a thing called the lambda calculus. Some people encoded everything including numbers and their operations, various data structures and control structures, … all in lambdas. One of the encodings of numbers is called the Church numeral. Every programming language researcher has played with them during their education. But unlike “everything is an object”, “everything is a function” has never become a dogma or marketing phrase. Those formulations sometimes provide thought experiments and inspirations to the researchers but nobody really use them for actual computation, because they are inefficient and they are not really how things work. They are just approximations (models) to some essence of computation that we can’t see. If you really use them for practical projects, things become complicated.

Mathematicians have a similar concept: set theory. Some geniuses encoded everything — numbers, operations on numbers, mathematical structures, … all in sets. Everything is just sets containing sets containing sets and so on. What’s the problem? But when they really tried to do their proofs using those sets, the proofs fell under their own weights. They are too complicated. Even with the complexity, set theory is not expressive enough to capture whatever the mathematicians have to say. Many people tried to fix it, but they all failed.

So “everything is an object” is in some sense on the same track of “everything is a function” and “everything is a set”. Good thought exercise, but doesn’t really work well in practice. I don’t think that there is some “one true language”. When compared to the “absolute truth” every theory is wrong, but some theories are more wrong than others. The model of OOP is too far from correct or practical. It’s somewhat like the flat earth theory. Until today some people still believe that the earth is flat and make all kinds of theories to prove it. Some of their arguments look very scientific, but do you believe in their formulas or a picture of the earth from a spaceship? When you get the fundamental things wrong and don’t throw them away, you have to patch them endlessly with even more complicated theories. You will have to make theories that bend the light and gravity in weird ways. And that’s what happened to OOP.

Are functions objects?

From what I know, the original motivation of putting functions inside objects was to support GUI applications. You click on a button and some function (a callback) will be invoked. For the convenience of referring to the button, the callback takes the triggered object as its first argument. Since the callback does nothing more than this, it seems to be convenient to just store it inside the button. And thus we had an “object” which combines the attributes of the button and a method (the callback). Indeed this is a good idea, but this limited usage case can’t really justify a universal notion of “everything is an object”, just like a two-mile walk can’t prove that the earth is flat.

If you really understand what is abstraction, you may have noticed that even the above story contains a subtle mistake: the callback in the button is not really a method. The true purpose of a method is to provide abstraction to the attributes, but the callback’s purpose is not to provide abstraction. It is just a usual function triggered by the button, which happens to take the button as its first argument.

Very few functions should be considered methods of an object. If you look carefully, most of the time the objects just serve as a namespace (or module) in which you can store attributes and functions, but those functions don’t logically belong to the objects. They just take the objects as inputs and produce some output. Only the functions that are most intimately connected to the attributes and provide an abstraction layer to them should be considered methods. Most of those are called “getters” or “setters”. Some others hide implementation details for more complex data structures such as lists, hash tables, sets etc.

In some languages such as Scala or Python, functions are also treated as objects, but they actually just wrapped the functions into an object, give them names such as “apply” or “call“, so that when the objects are “invoked” you know which functions to call. But putting a function into an object doesn’t really mean that functions are also objects, just like inviting friends to your house doesn’t make them your family.

Functions are fundamental constructs. They don’t belong to objects. They describe a change, transition or transformation of objects. They are not objects and can’t be simulated by objects. They are like a base case of an inductive definition. They are where the illusion of “everything is an object” ends.

The cost of excessive abstraction

The major appeal of OOP is abstraction (and thus code reusing and DRY), but actually most of those abstraction facilities are already provided by traditional procedural languages and functional languages. Some of them do it even better than OO languages. OO claims its originality by emphasizing abstraction much more strongly than other languages. The result is that OO programmers usually overdo it. Some of them pursue abstraction and code reusing to the degree as if they are everything about programming.

For the purpose of code reusing, OO encourages a level of abstraction which makes programs hard to understand and hard to analyze. I often see Java programs with multiple levels of inheritance, overloading and design patterns, but actually doing very little. And because there is so much code that doesn’t do useful things, it is really hard to find out which part of the code is doing the thing you want. It is like going through a maze. Another nice word for this is “robustness”. If I have to go into all this trouble to make code reusable or robust, I’d rather just make copies of the code and modify them, but keep each copy simple and easy to understand.

Whenever you criticize Java or C++ for their verbosity, OO proponents will tell you that they are not authentic OO languages. They would ask you to look at Smalltalk. If Smalltalk’s ways are that good, why almost nobody is using Smalltalk now? Because there are real problems in its approach. I think Smalltalk is the origin of over-abstraction and over-complication you find in other OO languages.

The “authentic” OO style of Smalltalk promotes the notion of “extremely late binding”, which basically means that the meaning of the program constructs is determined as late as possible. Late binding gives you a chance to swap out the underlying implementation without forcing the upper levels to change, but this also means that you are no longer sure what a piece of code means. When I look at expressions such as ‘1+2’ and ‘if (t) then … else …’ in Java or C++, I at least know for sure that they mean an integer addition and an usual conditional. But I’m no longer sure about this in an “extremely late binding language”, because the meaning of ‘+’ and ‘if” can be redefined. Giving the programmers the power of defining control structures is a bad idea, because soon your language will be abundant of quirky control structures designed by programmers who try to be clever. It will no longer be the language that you used to know.

An example for this feature is Smalltalk’s conditional structure, which looks like this:

result := a > b
    ifTrue:[ 'greater' ]
    ifFalse:[ 'less or equal' ]

You send a message ifTrue: to a Boolean object, passing as an argument a block of code to be executed if and only if the Boolean receiver is true.

First of all, if you really have a well-designed language, you shouldn’t be wanting to define your own control structures. As a seasoned Lisp/Scheme programmer, I have seen many custom-designed control structures (such as the various looping macros) over the years, but none of them turned out to be good ideas. I’d rather write slightly longer and more verbose code in the vanilla language than to learn those weird control structures. Second, if you are really genius enough to have invented another good control structure, the late binding feature of Smalltalk probably won’t provide you the necessary power for defining it. The power of functions as an abstraction tool is limited. It is strictly less powerful than Lisp/Scheme’s macros. Third, this feature of Smalltalk is not really a novel approach, and it has a serious problem. A similar but more beautiful conditional construct had been defined in lambda calculus since before computer science was born:

TRUE = λx.λy.x
FALSE = λx.λy.y
IF = λb.λt.λf.b t f

This is very beautiful and can be done in any functional language, but why none of the functional languages implement conditionals this way? Because when you see an expression IF b t f, you will have no idea whether it is a conditional or not, because IF can be redefined in the program. Also because IF is just a function, it may also accept unexpected values other than TRUE or FALSE. This may happen to make the conditional construct work but cause trouble later on. This is called “unintentional semantics”. This kind of bug can be very hard to track down.

This approach also makes compiler and static analysis hard. When the compiler sees IF b t f, it no longer knows that it is a conditional so it can’t optimize it that way. It has to treat it as a usual function call. Similarly when the type checker sees it, it doesn’t know what type to expect for b, because it may not be a conditional at all. The above argument against the lambda calculus can easily be adapted to Smalltalk.

So abstraction is a powerful weapon when used moderately, but when you do it in excess, it backfires. Not only does it make it hard for humans to understand the code, it makes automated analysis tools and compiler optimizations difficult or impossible to make.

Design patterns, the brain eater

Although OO languages are touted for their abstraction capabilities, they are actually not strong in terms of abstraction and expressiveness. There are certain things that are very easy to do in traditional procedural languages and functional languages, but has been made unnecessarily hard in OO languages. This is why design patterns appeared. Design patterns’ origin was mostly due to the dogma of “everything is an object”, the lack of high-order functions (or the correct implementation of them) and OO’s tendency of mystifying things.

When I first heard about design patterns I was already a PhD student at Cornell doing some PL research. I mostly used Standard ML and Haskell. After hearing my friends’ high opinions of the Design Patterns book (the “GoF” book) I developed curiosity, so I borrowed one from the library. Within a few hours I found a mapping from all the weird names it introduced to the programming techniques I had been using all the time. Some of them are so fundamental and they exist in every high-level language, so they don’t really need names. Most of the advanced ones (such as visitor) are transcriptions of functional programming concepts into a convoluted form in order to get around OO language’s limitations. Later on I found that Peter Norvig gave a talk on design patterns as early as 1998, pointing out that almost all of the design patterns will be “transparent” once you have first-class functions. This confirmed my observations — I don’t need them.

I have to admit that some of the design patterns are cleverly designed and contain some ingenuity. You really need to get to the essence of the OO languages’ internal magics and also understand lots of functional programming techniques in order to create them. But intelligence =/= wisdom. Even if they can achieve what functional languages can do, they are usually a lot more complicated. Choosing the hard ways can’t really prove your genius. When you have first-class functions, things become so much easier and you won’t even notice the design patterns’ existence. Like Peter Norvig said, they will become “transparent”. So what a good language designer should do is to add first-class functions into the language instead of proposing design patterns as workarounds.

Every time I remove a design pattern (some other people wrote), the code becomes simpler and more manageable. I just removed the last visitor pattern from my Java code a few days ago and I felt so relieved. They gave me nothing but extra work when they existed. They hindered my progress. By deeply understanding how OO languages are implemented, you can write more advanced things than those provided by design patterns but without actually using them. I owe these insights to some functional programming people. If you really want to understand the essence of OO design patterns and how NOT to use them, this little book may be a good starting point.

Unfortunately design patterns somehow got really popular in companies, to the degree of unbearable. I saw the GoF book on almost every bookshelf when I interned at Google. Even if you don’t write them yourself, there was almost no way you could avoid other people slipping design patterns into your code. Design patterns’ marketing strategy was much like weight loss products: “It can burn your fat without you doing any work!” They appeal to some new programmers’ hope that they can write programs without understanding the fundamental concepts of computer science. Just by applying several patterns and patching things together, they hope to have a good program. This is too good to be true. You end up doing more work than you hoped to avoid. Design patterns eat programmers’ brains. After using design patterns for some time, they no longer see things or write programs in clear and straightforward ways.

What is an OO language any way?

To this point we haven’t yet talked about what makes a language an “OO language” and what makes it not. Is it an OO language just because I can put both data fields and functions into a record? Or is it an OO language only if it also provides extremely late binding? How about inheritance, overloading, etc etc? Must I have all of them? Any of them?

It turns out that there is no good answer to this question. There really is no such thing as an “object-oriented language”. Objects can be part of a language, but it is just a small part of it. You can’t really say that a language is object-oriented just because it provides objects as a feature. The so-called OO languages are solidly rooted in traditional procedural programming (PP). OOP basically stole everything from PP, renamed the terminologies and acted as if the ideas were its own.

Historically the term OO was mainly used for marketing reasons. It could give a language some advantages of attracting people if you claim it to be an OO language, but now this advantage is diminishing because more and more people have realized the problems of OO’s methodology.

Harm in education and industry

Although OO has lots of problems, it is very successful in marketing and has risen to a dominant position over the years. Under social and market pressure, many colleges started using OO languages such as Java as their introductory language, replacing traditional procedural languages such as Pascal and functional languages such as Scheme. This in a large degree caused the students’ failure to learn the most essential concepts of programming. The only thing that OO emphasizes is code reusing, but how can you teach it to the students who can’t even write usable code, not to mention that code reusing is not really as important as some people believe.

At both Cornell and Indiana, I have been teaching assistant for introductory programming courses in Java. I still remember how confused the students were. Most of them had trouble understanding things such as the meaning of “this”, why everything needs to be put inside classes, why make every field private and use getters, the difference between a method and a static method, etc etc.

There is a good reason that they don’t understand — because OO is not how things work. Most of the time I feel that I was teaching design flaws and dogmas to them. Many of them learned very little in the end. Worse, some of those students really believed in OO. They ended up being proud of writing over-engineered and convoluted code. They no longer see things or write programs in straightforward ways. This is sad. I feel that we are no longer educating students as creative and critical thinkers, but mindless assembly line workers.

In industry, OO hasn’t really proved its effectiveness with evidence. Good systems may be built in a “OO language”, but the code is often written by people who understand the problems of OO and don’t embrace “everything is an object” or “design patterns”. Good programmers usually use workarounds in OO languages and are essentially writing in a traditional procedural style combined with bits from functional programming. So some OO languages and their tools may be pretty widely used, but the OO style doesn’t really have much influence on the advancements of programming as a field.

Final word

So what does this post has to say? A jihad against OO languages? Advocate functional programming? Neither. As I said, there is no such thing as an “OO language”, so where is the war? Every so-called OO language also contains good elements that it borrowed (or stole) from procedural languages or sometimes functional languages, so they are not completely useless.

It is the extra features added by OO in addition to procedural programming that are causing most of the problems. Those extra “true OO techniques” contain way more confusion than real value. In my experience, accepting even one or two of those ideas may put you into a series of troubles and wrong ways of thinking which may take a long time to examine and recover. They are like diseases.

Thus I suggest not to buy OO’s way of thinking and don’t try to exploit its “features”. By eschewing those problematic features you can still produce acceptable programs in an OO language, because you are essentially using it as an non-OO procedural language.

(Chinese translation by ZoomQuiet)

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Posted by on December 24, 2013 in oop, programming languages