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After competing in and following this year's Google Code Jam competition, I couldn't help but notice the incredible number of [successful] contestants that used C/C++ and Java. The distribution of languages used throughout the competition can be seen here.
After programming in C/C++ for several years, I recently fell in love with Python for its readable/straightforward nature. More recently, I learned functional languages like OCaml, Scheme, and even logic languages like Prolog. These languages certainly have their merits and, in my opinion, can be applied more easily than C++ and Java for certain situations. For example, Scheme's use of call/cc simplifies backtracking (a tool required to answer several problems) and Prolog's logic specification, although inefficient due to its brute-force nature, can drastically simplify (and even automatically solve) certain problems that are difficult to wrap one's brain around.
It is clear that a competition contestant should use the tools that are best suited for the challenge. Even x86 assembly is Turing complete - that doesn't justify solving problems with it. In this case, why are the contestants that use less common languages like Scheme/Lisp, Prolog, and even Python significantly less successful than contestants that use C/C++ and Java? Worded differently, why don't successful contestants use languages that, although may be less mainstream, are arguably better tools for the job?
There are several motivations for my question. Most importantly, I would like to become a better programmer - both in the practical aspect and the competition aspect. After being introduced to such beautiful paradigms like functional and logic programming, it is discouraging to see so many people discard them in favor of C/C++ and Java. It even makes me question my admiration for said paradigms, worrying that I cannot be successful as a Lisp/Scheme/Prolog programmer in a programming competition.
Great question! As someone who has dabbled in programming contests a bit myself, I may have something to say.
[Let's get the standard disclaimer out of the way: contest programming is only loosely related to "programming in the real world", and while it tests algorithmic and problem-solving skills and the ability to come up with fast bug-free working code under time pressure, it does not necessarily correlate with being able to build large software projects, write maintainable code, etc (beyond the fact that well-structured programs are easier to debug).]
Now for some answers:
C++/Java are more common than other languages in the real world as well, so you'd expect to see a higher proportion anywhere. (But it's even higher in the contest population.)
Many of these participants are students, or got into contests as students, and C++/Java are more common "first languages" that students learn. (Undergrad students these days may start with Scheme, Haskell, Python, etc., but high-schoolers (often self-taught) less often.) In fact, many of the Eastern European participants still use Pascal, and are more amazing with it than the rest of us will ever be with any language.
The school- and college-level contests usually use these languages. The International Olympiad in Informatics (IOI) allows only C, C++ and Pascal (or maybe it allows Java now; I haven't kept up), and the ACM Intercollegiate Programming Contest (ACM ICPC) allows only C, C++ and Java. TopCoder allows C++, Java, C# and VB (really :p); and recently, Python. So you could say the "contest ecosystem" has more C++/Java programmers in it. Google Code Jam and IPSC are among the few contests that allow code in any language, actually.
Now the question is, in GCJ where the contestants are free to choose a language, why wouldn't they choose Python or Scheme? The most relevant factor is that these languages are slow. Sure, for most real-world programming they are easily fast enough, but for the tight loops that are often involved in getting a program to run under the n-second limit for all test cases, these languages don't cut it for any of the algorithmically more involved problems. (A problem designed to accept O(n log n) solutions but not Θ(n2) solutions for C/C++ frequently rules out even optimal O(n log n) solutions in slower languages. Even Java used to be given a handicap at USACO; I'm not sure this is still the case.)
Another factor is the libraries: C++ and Java have better libraries for frequently useful algorithms and data structures (e.g. red-black trees, C++'s next_permutation), while Python's libraries (good enough for the real world) are less useful here, and Prolog and Scheme... I don't know about their libraries. This is a relatively minor factor, because these programmers can write their own code when necessary. :-)
General-purpose multi-paradigm languages are more useful for just getting things done within the time constraints of the contest, than languages that force a philosophy or way of doing things on you. This is why Prolog will always remain unpopular, for instance. (General philosophy: some languages are "enabling" languages that let you do anything including shooting yourself in the foot, some are "directing" that force you to do things the right way.) This is also why C++ is three times more popular than Java in the general contest participants, and much more popular among the top contestants. Since code doesn't have to be read by anyone else, it's ok and even useful to have loop macros like FOR(i,n) (less code to type, and more importantly less chance of making a bug when in a hurry). Nothing against Java, there are a few top programmers who use Java too. :-)
Finally, although many of these top programmers may have C++/Java/Pascal as their "first language", they are not good because of their language, so you don't have to despair about that. Many of these same programmers have won contests like the ICFP contest even with intentionally using crazy languages like shell scripts, m4 (used in autoconf), and assembly (the team named "You Can't Spell Awesome Without ASM").
I liked Jerry Coffin's idea of plotting contestants of the Google AI contest, so I took all of the results and plotted them (calculated mean, standard deviation, and then graphed the normal distribution curves in Excel).
With Lua and JS, got this:
Without (there were few contestants, so maybe the results are skewed):
It looks like Java participants did markedly worse than the rest, while Go, Common Lisp, and C are on the better end.
Why we all speak English and not Esperanto? Well, it just happened so. Even though English is inconsistent and bloated and Esperanto is intentionally designed as 'better tool'.
Thus, one reason is a tradition. In most schools programming is still taught in C/C++, Java, Pascal or even Basic. And participate in those contests mostly students, which choose language they know better.
Also, you can notice that most algorithmic books feature psedudocode in style of Pascal or Ada, and very very rarely - Lisp. I don't know why, perhaps also a tradition. Or perhaps it's just not so good for the algorithms.
Another reason would be speed. Although it's not a problem for Google Code Jam, in almost all contests 2x speed gap is a difference between 'Accepted' and 'Time Limit' verdicts.
In other words, if optimal algorithm in C++ runs 10 times faster than in Ruby, it may mean that sub-optimal algorithm in C++ will still be faster than a good one in Ruby. And contest authors usually don't want to allow O(n^2) submissions, if O(n*logn) can be achieved.
First, I'd question your premise [edit: or what I take to be a premise -- that contestants using C++ and Java fare about equally well]. For example, here's what languages were used for the entries that came in the first 100 places and the last 100 places in Google's recent AI contest:
Contestants using C++ and Java did not seem to be anywhere close to equally successful in that contest. Contestants using Python didn't seem to fare particularly well either, though there were considerably fewer of them, weakening any conclusion in that regard.
Second, of course, an awful lot of the explanation (as others have pointed out) is undoubtedly just the number of people who are familiar with each language. There are probably more people taking a course in Java right now than the total number of people who've ever written any Lisp, Scheme or Prolog.
Edit: I think a third possibility is simply versatility. To pick an extreme example, Prolog is very well suited to a few problems, but equally poorly suited to many others. Few people can (or at least do) learn more than one or two languages well enough to use them in a contest, so most people who are interested in such things are likely to choose languages that can work reasonably well for almost anything, rather than attempting to learn a specialized language for every problem that might be chosen.
In nearly all Google Code Jam rounds, more of the higher-performing contestants code in C++.
Below are the language stats from Google Code Jam 2012 Round 1A, 1B, and 1C (listed top to bottom).
The number of contestants in each round are 3,686, 3,281, and 3,189 respectively.
fun question, probably should be community wiki.
Look at number of finalists by countries: http://www.go-hero.net/jam/10/regions. notice number of people from East Europe and Russia. those places have very strong C++ communities, as well as Java, for number of reasons.
look at number languages in qualifiers: http://www.go-hero.net/jam/10/languages/0 and finals: http://www.go-hero.net/jam/10/languages/6. C++ starts out less than half and has 75 percent in finals. either good programmers prefer C++ or C++ makes the programmers. Probably by the time you master C++, other things become trivial.
You are free to draw your own conclusions though.
First of all, as you have pointed C++ and Java are mainstream languages. These automatically means that people who start doing programming competitions will be introduced to them first - by the way who learns Lisp as a first language:) I also participate regularly in such competitions - I use C++ to compete, although my favorite language is Java. It is just I want to practice another language apart from Java - also C++ is a little bit less verbose and runs faster which is important for programming competitions.
Now to my point - people become experts first in mainstream languages. To participate in programming competitions you must have quite a good grasp of the language you are using. You don't have time to search on the internet for stupid things - like forgot a construct. It is just that speed is an important factor there. To use Lisp in a competition, you must be fond of it. I don't think there are such many people out there. Correct me if I am wrong. And honestly the pros you have mentioned like simplifies backtracking: In whatever language backtracking is easy - declare a method and just call it again for every possible outcome. It couldn't be simpler. I haven't felt till now that the language I am using is trying to trip up my feet for programming competitions.
OMG ... People are all going through the Stats and Figures !!
Lets not forget the basics.. These are the only two languages (mostly) which are taught to people in college/schools...!
That might answer the heavy rush!
A vital reason might be that every contests don't support languages like python or prolog. Specially ACM ICPC World Finals support C/C++ and Java. And TopCoder also supports only C++, Java, C#, VB, and now Python. It is natural for the contestants that they will choose one language that is available in every contest. Another reason might be execution speed. And yes, another reason is these are the languages that most of the people learn first.
Big libraries were a selling point for Java in ACM ICPC. It's handy to be able to realize you want some random data structure or algorithm and just pull it out of the standard libraries.
Keep in mind that C++ is not only the majority among all contestants, but as the rounds progress, its percentage just keeps and keeps improving.
I'd say it is true that most of the participants are students (However, since it is an open tournament with chances to a job interview with google, then you have to consider that many who participate are graduated). But the latest rounds are only for people with ton of experience. They are not just students who just learned to code in C++ / Java.
Of course, the student argument also works against languages like LISP and OcaML or ProLog. That is languages, that are used a lot in AI areas but in the mainstream world students are the most likely to be learning and use them.
Big contests other than google's support few languages, but that still wouldn't explain why Pascal or .net are not near the level of Java (As they tend to be equally supported in the major contest events).
A lot of the best coders in these contests know a lot of languages. But they still prefer to use C++ during the rounds it must be for a bigger reason than "learned C++" first.
I would argue against the claim that languages other than C++ or Java are better tools for the job. If direct data says that the finalists are more likely to use C++ and Java it is a direct contradiction to that claim.
Google AI competition data does not actually contradict any premise regarding the code jam. It actually does show that top coders are able to use languages like Common Lisp when it is truly the better tool for the job. If we want to use this data to assume that CLISP is a great tool for AI competitions, then we should also assume that C++ is a great tool for algorithm competitions like GCJ.
I often read about the importance of readability and maintainability. Or, I read very strong opinions about which syntax features are bad or good. Or discussions about the values of certain paradigms, like OOP.
Aside from that, this same question floats about in my mind whenever I read debates on SO or Meta about subjective questions. Or read questions about best practices and sometimes find myself or others disagreeing.
What role does subjectiveness play within the programming realm?
Sometimes I think it plays a large role. Software developers are engineers in a way, but also people. A large part of programming is dealing with code that's human readable. This is very different from Math or Physics or other disciplines with very exact and structured rules. Here the exact structure and rules are largely up in the air, changeable on a whim, and hence the amount of languages in existence. And one person may find one language very readable, and another person may find their own language the most comforting.
The same with practices. One person may not like certain accepted practices. I myself find splitting classes into different files very unreadable, for instance.
But, I can't say rules haven't helped in general. Certain practices have and do make life easier. And new languages have given rise to syntax and structure that make life easier. There's certainly been a progression towards code that is easier to read and maintain even given a largely diverse group of people. So maybe these things aren't as subjective as I thought.
It reminds me, in a way, of UI design. Certainly it's subjective, but then there's an entire discipline involved in crafting good UI and it tends to work.
Is there something non-subjective about the ideas behind maintainability, readability, and other best practices? Is there something tangible to grasp when one develops a new language or thinks of new practices?
Arguably your question is really about the distinction between programming, which is mathematical, algorithmic and scientific, and software engineering, which is subjective, variable and human-focused.
Great programmers are not necessarily great software engineers, and vice versa. The two skillsets, while not exclusive by any means, have less overlap than they appear at first. Their relative importance depends a lot on the project: a brilliant programmer working alone can turn out amazing examples of technical genius, and it doesn't matter that nobody else can understand or maintain it, because he's not going to share the code anyway. But move into an enterprise environment -- like corporate in-house software development -- and I'll gladly trade you ten "cave troll" geniuses for a mediocre programmer who understands the importance of readability and documentation.
It's been my experience that the world needs great software engineers more than it needs great programmers. Relatively few people in this day and age are writing software which is truly performance-critical (OS kernels, compilers, graphics engines, realtime embedded systems, etc), and the Internet allows mediocre programmers to quickly grab algorithmic solutions for problems they couldn't solve alone. But nearly everyone writing professional code has to work within a team. And team productivity rises and falls dramatically on the ability of its members to communicate effectively and distribute workload efficiently, two skills which are highly subjective and impossible to prove by rigid formula.
Most software engineering principles are built on experience rather than objective law. Much like the social sciences, we study, learn, adapt and apply -- but with no real guarantees of outcome. All we can say is that some things seem to work better than others in most groups.
I think, a lot of it is necessarily determined by how much our mind is able to process at one time. So it comes down to how much the language and tools enable a team or a developer to break down the problem into chunks that are meaningful by themselves, but not so large that it becomes too hard to grasp them. The common theme is the art of organizing information (in this case, the code, the logic, ...) But that's not so different from Maths or Physics, by the way.
Just as the best authors borrow from many styles, the best programmers keep a huge range of patterns in their mental arsenal. Slavishly following a few patterns and adhering to some absolute truth is both lazy and dangerous.
Put it another way, the day we rely on robots for code review is the day I quit.
It all depends on your point of view :-)
But to answer your questions, I think one way to view subjectivity is to recognize that software languages, tools, and best practices are a shared means of communication among individuals. Yes, a programming language is a formal way of instructing a computer how to behave, but a programming language may also be viewed as a way to define and communicate specifications to a high level of detail (the code is the ultimate spec, is it not?).
So as far as we may want to concern ourselves with the degree of subjectivity in software languages, tools, and best practices, I would say that the lack of subjectivity may indicate how well communication is facilitated.
Yes, individuals have certain proclivities that are expressed in their habits and tendencies, but that should not ultimately matter too much in the perfect platform for development.
Turning to my Maths PhD wife I asked if there's any subjectivity in mathematics. Her answer is yes there is, mainly in the way we as humans achieve the answer.
If a mathematical proof is the result, how you get to that result can vary. If the dataset is large you may need to use a computer, which can introduce errors, and thus debated about whether that is the right approach. Or sometimes mathematicians can disagree on the theory - one is trying to prove that x is true while the other is trying to prove that x is false.
I think the same thing exists in computer science. A correct answer is a program that runs correctly, but that definition of correct may be different for each project. Sometimes correct means no bugs. Sometimes it means running efficiently.
From here programmers can argue how best to achieve the "correct" result. A good example of this is is the FizzBuzz application. A simple answer would be just a for loop, but Enterprise FizzBuzz is also "correct" in that it produces the correct answer, but is generally laughed at as "bad" engineering due to its overcomplication of the idea (it was a joke app after all).
How large a role does subjectiveness play in programming? I'd say it's a very large part of what we do, simply because we are human, and because there are multiple ways of getting the "correct" answer so there is disagreement over which way is the best.
Studies have been done showing that certain practices reduce defect rates in software. For instance, a study found a strong correlation between cyclomatic complexity and the probability of being fault-prone. Other studies show the average effectiveness of design and code inspections are 55 and 60 percent. So it appears to be in our best interests to favor simplicity, check metrics, and do code reviews.
We're talking probabilities here, though. If I review your code, I'm not guaranteed to find 60% of your bugs. There are also few absolutes in software development; experienced developers know that the correct answer is generally "it depends." That said, there are a number of practices with objective data in their favor.
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I started my college two years ago, and since then I keep hearing "design your classes first". I really ask myself sometimes, should my solution to be a bunch of objects in the first place! Some say that you don't see its benefits because your codebase is very small - university projects. The project size excuse just don't go down my throat. If the solution goes well with the project, I believe it should be the right one also with the macro-version of that project.
I am not saying OOP is bad, I just feel it is abused in classrooms where students like me are told day and night that OOP is the right way.
IMHO, the proper answer shouldn't come from a professor, I prefer to hear it from real engineers in the field.
Is OOP the right approach always?
When is OOP the best approach?
When is OOP a bad approach?
This is a very general question. I am not asking for definite answers, just some real design experience from the field.
I don't care about performance. I am asking about design. I know it is engineering in real life.
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Thankful for all contributions. I chose Nosredna answer, because she addressed my questions in general and convinced me that I was wrong about the following :
If the solution goes well with the project, I believe it should be the right one also with the macro-version of that project.
The professors have the disadvantage that they can't put you on huge, nasty programs that go on for years, being worked on by many different programmers. They have to use rather unconvincing toy examples and try to trick you into seeing the bigger picture.
Essentially, they have to scare you into believing that when an HO gauge model train hits you, it'll tear your leg clean off. Only the most convincing profs can do it.
"If the solution goes well with the project, I believe it should be the right one also with the macro-version of that project."
That's where I disagree. A small project fits into your brain. The large version of it might not. To me, the benefit of OO is hiding enough of the details so that the big picture can still be crammed into my head. If you lack OO, you can still manage, but it means finding other ways to hide the complexity.
Keep your eye on the real goal--producing reliable code. OO works well in large programs because it helps you manage complexity. It also can aid in reusability.
But OO isn't the goal. Good code is the goal. If a procedural approach works and never gets complex, you win!
OOP is a real world computer concept that the university would be derelict to leave out of the curriculum. When you apply for jobs, you will be expected to be conversant in it.
That being said, pace jalf, OOP was primarily designed as a way to manage complexity. University projects written by one or two students on homework time are not a realistic setting for large projects like this, so the examples feel (and are) toy examples.
Also, it is important to realize that not everyone really sees OOP the same way. Some see it about encapsulation, and make huge classes that are very complex, but hide their state from any outside caller. Others want to make sure that a given object is only responsible for doing one thing and make a lot of small classes. Some seek an object model that closely mirrors real world abstractions that the program is trying to relate to, others see the object model as about how to organize the technical architecture of the problem, rather than the real world business model. There is no one true way with OOP, but at its core it was introduced as a way of managing complexity and keeping larger programs more maintainable over time.
OOP is the right approach when your data can be well structured into objects.
For instance, for an embedded device that's processing an incoming stream of bytes from a sensor, there might not be much that can be clearly objectified.
Also in cases where ABSOLUTE control over performance is critical (when every cycle counts), an OOP approach can introduce costs that might be nontrivial to compute.
In the real world, most often, your problem can be VERY well described in terms of objects, although the law of leaky abstractions must not be forgotten!
Industry generally resolves, eventually, for the most part, to using the right tool for the job, and you can see OOP in many many places. Exceptions are often made for high-performance and low-level. Of course, there are no hard and fast rules.
You can hammer in a screw if you stick at it long enough...
My 5 cents:
OOP is just one instance of a larger pattern: dealing with complexity by breaking down a big problem into smaller ones. Our feeble minds are limited to a small number of ideas they can handle at any given time. Even a moderately sized commercial application has more moving parts than most folks can fully maintain a complete mental picture of at a time. Some of the more successful design paradigms in software engineering capitalize on the notion of dealing with complexity. Whether it's breaking your architecture into layers, your program into modules, doing a functional breakdown of actions, using pre-built components, leveraging independent web services, or identifying objects and classes in your problem and solution spaces. Those are all tools for taming the beast that is complexity.
OOP has been particularly successful in several classes of problems. It works well when you can think about the problem in terms of "things" and the interactions between them. It works quite well when you're dealing with data, with user interfaces, or building general purpose libraries. The prevalence of these classes of apps helped make OOP ubiquitous. Other classes of problems call for other or additional tools. Operating systems distinguish kernel and user spaces, and isolate processes in part to avoid the complexity creep. Functional programming keeps data immutable to avoid the mesh of dependencies that occur with multithreading. Neither is your classic OOP design and yet they are crucial and successful in their own domains.
In your career, you are likely to face problems and systems that are larger than you could tackle entirely on your own. Your teacher are not only trying to equip you with the present tools of the trade. They are trying to convey that there are patterns and tools available for you to use when you are attempting to model real world problems. It's in your best interest to accumulate a collection of tools for your toolbox and choose the right tool(s) for the job. OOP is a powerful tool to have, but by far not the only one.
No...OOP is not always the best approach.
(A true) OOP design is the best approach when your problem can best be modeled as a set of objects that can accomplish your goals by communicating/using one another.
Good question...but I'm guessing Scientific/Analytic applications are probably the best example. The majority of their problems can best be approached by functional programming rather than object oriented programming.
...that being said, let the flaming begin. I'm sure there are holes and I'd love to learn why.
Is OOP the right approach always?
Nope.
When OOP is the best approach?
When it helps you.
When OOP is a bad approach?
When it impedes you.
That's really as specific as it gets. Sometimes you don't need OOP, sometimes it's not available in the language you're using, sometimes it really doesn't make a difference.
I will say this though, when it comes to technique and best practices continue to double check what your professors tell you. Just because they're teachers doesn't mean they're experts.
It might be helpful to think of the P of OOP as Principles rather than Programming. Whether or not you represent every domain concept as an object, the main OO principles (encapsulation, abstraction, polymorphism) are all immensely useful at solving particular problems, especially as software gets more complex. It's more important to have maintainable code than to have represented everything in a "pure" object hierarchy.
My experience is that OOP is mostly useful on a small scale - defining a class with certain behavior, and which maintains a number of invariants. Then I essentially just use that as yet another datatype to use with generic or functional programming.
Trying to design an entire application solely in terms of OOP just leads to huge bloated class hierarchies, spaghetti code where everything is hidden behind 5 layers of indirection, and even the smallest, most trivial unit of work ends up taking three seconds to execute.
OOP is useful --- when combined with other approaches.
But ultimately, every program is about doing, not about being. And OOP is about "being". About expressing that "this is a car. The car has 4 wheels. The car is green".
It's not interesting to model a car in your application. It's interesting to model *the car doing stuff. Processes are what's interesting, and in a nutshell, they are what your program should be organized around. Individual classes are there to help you express what your processes should do (if you want to talk about car things, it's easier to have a car object than having to talk about all the individual components it is made up of, but the only reason you want to talk about the car at all is because of what is happening to it. The user is driving it, or selling it, or you are modelling what happens to it if someone hits it with a hammer)
So I prefer to think in terms of functions. Those functions might operate on objects, sure, but the functions are the ones my program is about. And they don't have to "belong" to any particular class.
Like most questions of this nature, the answer is "it depends."
Frederick P. Brooks said it the best in "The Mythical Man-Month" that "there is no single strategy, technique or trick that will exponentially raise the productivity of programmers." You wouldn't use a broad sword to make a surgical incision and you wouldn't use a scalpel in a sword fight.
There are amazing benefits to OOP, but you need to be comfortable with the pattern to take advantage of these benefits. Knowing and understanding OOP also allows you to create a cleaner procedural implementation for your solutions because of the underlying concepts of separation of concerns.
I've seen some of the best results of using OOP when adding new functionality to a system or maintaining/improving a system. Unfortunately, it's not easy to get that kind of experience while attending a university.
I have yet to work on a project in the industry that was not a combination of both functional and OOP. It really comes down to your requirements and what are the best (maybe cheapest?) solutions for them.
OOP is not always the best approach. However it is the best approach in the majority of applications.
OOP is the best approach in any system that lend itself to objects and the interaction of objects. Most business applications are best implemented in an object-oriented way.
OOP is a bad approach for small 1 off applications where the cost of developing an framework of objects would exceed the needs of the moment.
Learning OOA, OOD & OOP skills will benefit the most programmers, so it is definately useful for Universities to teach it.
The relevance and history of OOP runs back to the Simula languages back in the 1960s as a way to engineer software conceptually, where the developed code defines both the structure of the source and general permissible interactions with it. Obvious advantages are that a well-defined and well-created object is self-justifying and consistently repeatable as well as reliable; ideally also able to be extended and overridden.
The only time I know of that OOP is a 'bad approach' is during an embedded system programming efforts where resource availability is restricted; of course that's assuming your environment gives you access to them at all (as was already stated).
The title asks one question, and the post asks another. What do you want to know?
OOP is a major paradigm, and it gets major attention. If metaprogramming becomes huge, it will get more attention. Java and C# are two of the most used languages at the moment (see: SO tags by number of uses). I think it's ignorant to state either way that OOP is a great/terrible paradigm.
I think your question can best be summarized by the old adage: "When the hammer is your tool, everything looks like a nail."
OOP is usually an excellent approach, but it does come with a certain amount of overhead, at least conceptual. I don't do OO for small programs, for example. However, it's something you really do need to learn, so I can see requiring it for small programs in a University setting.
If I have to do serious planning, I'm going to use OOP. If not, I won't.
This is for the classes of problems I've been doing (which includes modeling, a few games, and a few random things). It may be different for other fields, but I don't have experience with them.
My opinion, freely offered, worth as much...
OOD/OOP is a tool. How good of a tool depends on the person using it, and how appropriate it is to use in a particular case depends on the problem. If I give you a saw, you'll know how to cut wood, but you won't necessarily be able to build a house.
The buzz that I'm picking up on is that functional programming is the wave of the future because it's extremely friendly to multi-threaded environments, so OO might be obsolete by the time you graduate. ;-)
What are your criteria or things that you consider when you are an early adopter of a programming language or technology?
Two of the most common explanations I've heard are:
It should be "fun" (what I've heard from technical people).
It should be capable of solving our problem (what I've heard from business people).
So what's yours?
I've made this change several times over my career spanning various companies, moving from C to Java to Ruby to Haskell for the majority of my software development.
In all cases, I've been looking for more expressive power and better abstractions. This is always driven by business needs: how can I develop better software more cheaply? To me, the challenge of this problem is "fun," so fun rather automatically comes along with it. Justifying the business value to managers can be difficult, however; they often don't have the technical skills to understand why one programming language can be better than another, and are worried about moving to technology that they understand even less than the current one. (I solved this problem by taking over the manager's job as well: I started a company.)
It's hard to say what exactly to look for in a new language. You obviously don't have a detailed grasp of the language, or you would already be using it or know why you're not. Vast experience will bring an instinct that will make certain languages "smell" better than others, but—and this can make it especially hard to convince others to look at a new language—you won't know precisely what features give you big advantages. An example would be pattern matching: it's a feature found in relatively few languages, and though I knew about it, I had no idea when I started in with Haskell that this would be a key contributor to productivity improvement.
While it's negative ("avoid this") advice rather than positive ("do this") advice, one fairly easy rule is to avoid spending a lot of time on languages very similar to ones you already know well. If you already know Ruby, learning Python is not likely to teach you much in the way of big new things; C# and Java would be another example. (Although C# is starting to get a few interesting features that Java doesn't have.)
Looking at what the academic community is doing with a language may be helpful. If it's a fertile area of research for academics, there's almost certainly going to be interesting stuff in there, whereas if it's not it's quite possible that there's nothing interesting there to learn.
My criteria is simple:
wow factor
simple
gets things done
quick
I want it to do something easily that is hard to do with the tools I'm used to. So I moved to Python, and then Ruby, over Java because I could build a program incrementally, add functions easily, and express programs more concisely (esp. with Ruby, where I can pass blocks/Procs and have clean closures, plus the ability to define nice DSLs making use of blocks and yield.)
I took up Erlang because it expresses Actor-based concurrency well; this makes for easier network programs.
I took up Haskell because it fit with a number of formal methods tools I wanted to experiment with.
Open source.
Active developer community
Active user community, with a friendly mailing list or forum.
Some examples and documentation, preferably a tutorial
Desirable features (solves problems).
If it's for my personal fun, I need very little excuse, as I do love learning new things, and the best way of learning is by doing. If it's for an employer, customer, or client, the bar is MUCH higher -- I must be convinced that the "new stuff", even after accounting for ramp-up effects and the costs that come with being at the bleeding edge, will do a substantially better job at delivering value to the client (or customer or employer). It's a matter of professional attitude: my job's to deliver top value to the client -- having fun while so doing is auxiliary and secondary. So, in practice, "new" technologies (including languages) that I introduce in a professional setting will generally be ones I've previously grown comfortable and confident with in my own spare time.
Someone has once said something to the effect of:
"If learning a programming language doesn't change the way you think about programming, it's not worth learning."
That's one metric (out of many) to judge the value of learning new languages (or other technology) by. Using this, one might suggest learning the following languages:
C, because it makes you understand the Von Neumann architecture better than any other language (and it's sort-of random-access Turing Machine like, sorta'...).
LaTeX (as a programming language, not only as a typesetting system) because it makes you learn about string rewriting systems as a model of computation. Here, sed is similar; learn both, because they're also both useful tools :-)
Haskell, because it teaches you about functional programming, lambda calculus (yet another model of computation), lazy evaluation, type inference, algebraic datatypes (done with ease), decidability of type systems (i.e. learn to fear C++)
Scheme `(or (another) ,Lisp) for its macro system, and dynamic typing, and functional programming done somewhat differently.
SmallTalk, to learn Object-orientation (so I hear)
Java, to learn what earning money feels like :D
Forth, because wisdom bestowed forth learned implies.
... that doesn't explain why I learn python or shell scripting, though. I think you should take enlightenment with a grain of salt and a shovelful of pragmatism :)
Should be capable of solving the problem
Should be more adequate to solve the problem than other alternatives
Should be fun
Should have prompt support, either from a community or the company promoting it
A language should be:
Easy to use, to learn and to code in.
Consistent. Many languages have 50 legacy ways of doing things, this increases the learning curve and turns quite annoying. C# for me is one of those languages.
It should provide the most useful solution with the least amount of code. On the other hand sometimes you do need a bit of expressiveness to make sure you're not making a huge mistake.
The right tool for the right job and maybe the right tool for any job
My criteria that the language should have:
1. New ideas - If the language is just another Scheme variant, if you know one than I don't feel the need to learn this new one. I will learn it if I think I will learn something new.
2. Similar to another language, but better. For example, while Java and C++ have many of the same ideas, Java's automatic garbage collection makes it a better choice in many cases.
Gets the most done with the least amount of effort
Extremely interoperable with different protocols, out of the box
Fast
Has lots of libraries built in for stuff 99% of web developers do (PDF's, emailing, reporting, etc..)
It depends on why I'm learning the new language. If I'm learning it for fun, then it has to meet these criteria:
Is well it supported on my platform?
Something that runs only on Linux
isn't interesting to a Windows
programmer.
Will I learn something new? In
other words, does it come up with a
new way of doing things?
Does it look fun? I don't want to learn Ada even if it has new ways of doing things.
If I'm learning it for work, the criteria are different:
How mature is it? Has it been
proven to work in the real world?
How big is the community?
Will it make my job easier? I.e. is
it worth the time investment versus
just doing the task with a language
I already know.
I come from a fairly strong OO background, the benefits of OOD & OOP are second nature to me, but recently I've found myself in a development shop tied to a procedural programming habits. The implementation language has some OOP features, they are not used in optimal ways.
Update: everyone seems to have an opinion about this topic, as do I, but the question was:
Have there been any good comparative studies contrasting the cost of software development using procedural programming languages versus Object Oriented languages?
Some commenters have pointed out the dubious nature of trying to compare apples to oranges, and I agree that it would be very difficult to accurately measure, however not entirely impossible perhaps.
Most all of these questions are confounded by the problem that individual programmer productivity varies by an order of magnitude or more; if you happen to have an OO programmer who is one of the gruop at productivity x, and a "procedural" programmer who is a 10x programmer, the procedural programmer is liable to win even if OO is faster in some sense.
There's also the problem that coding productivity is usually only 10-20 percent of the total effort in a realistic project, so higher productivity doesn't have much impact; even that hypothetical 10x programmer, or an infinitely fast programmer, can't cut the overall effort by more that 10-20 percent.
You might have a look at Fred Brooks' paper "No Silver Bullet".
After poking around with google I found this paper here. The search terms I used are Productivity object oriented.
The opening paragraphs goes on to say
Introduction of object-oriented
technology does not appear to hinder
overall productivity on new large
commercial projects, but it neither
seems to improve it in the first two
product generations. In practice, the
governing influence may be the
business workflow and not the
methodology.
I think you will find that Object Oriented Programming is better in specific circumstances but neutral for everything else. What sold my bosses on converting my company's CAD/CAM application to a object oriented framework is that I precisely showed the exact areas in which it will help. The focus wasn't on the methodology as a whole but how it will help us sold some specific problem we had. For us was having a extensible framework for adding more shapes, reports, and machine controllers, and using collections to remove the memory limitation of the older design.
OO or procedural offer to different way to develop and both can be costly if badly managed.
If we suppose that the works are done by the best person in both case, I think the result might be equal in term of cost.
I believe the cost difference will be on how you will be the maintenance phase where you will need to add features and modify current features. Procedural project are harder to have automatic testing, are less subject to be able to expand without affecting other part and is more harder to understand the concept part by part (because cohesive part aren't grouped together necessary).
So, I think, the OO cost will be lower in the long run compared to Procedural.
i think S.Lott was referring to the "unrepeatable experiment" phenomenon, i.e. you cannot write application X procedurally then rewind time and write it OO to see what the difference is.
you could write the same app twice two different ways, but
you would learn something about the app doing it the first way that would help you in the second way, and
you may be better at OO than at procedural, or vice-versa, depending on your experience and the nature of the application and the tools chosen
so there really is no direct basis for comparison
empirical studies are likewise useless, for similar reasons - different applications, different teams, etc.
paradigm shifts are difficult, and a small percentage of programmers may never make the transition
if you are free to develop your way, then the solution is simple: develop things your way, and when your co-workers notice that you are coding circles around them and your code doesn't break nearly as often etc. and they ask you how you do it, then teach them OOP (along with TDD and any other good practices you may use)
if not, well, it might be time to polish the resume... ;-)
Good idea. A head-to-head comparison. Write application X in a procedural style, and in an OO style and measure something. Cost to develop. Return on Investment.
What does it mean to write the same application in two styles? It would be a different application, wouldn't it? The procedural people would balk that the OO folks were cheating when they used inheritance or messaging or encapsulation.
There can't be such a comparison. There's no basis for comparing two "versions" of an application. It's like asking if apples or oranges are more cost-effective at being fruit.
Having said that, you have to focus on things other folks can actually see.
Time to build something that works.
Rate of bugs and problems.
If your approach is better, you'll be successful, and people will want to know why.
When you explain that OO leads to your success... well... you've won the argument.
The key is time. How long does it take the company to change the design to add new features or fix existing ones. Any study you make should focus on that area.
My company had a event driven procedure oriented design for a CAM software in the mid 90's created using VB3. It was taking a long time to adapt the software to new machines. A long time to test the effects of bug fixes and new features.
With VB6 came along I was able to graph out the current design and a new design that fixed the testing and adaptation problem. The non-technical boss grasped what I was trying doing right away.
The key is to explain WHY OOP will benefit the project. Use things like Refactoring by Fowler and Design Patterns to show how a new design will lower the time to do things. Also include how you get from Point A to Point B. Refactoring will help with showing how you can have working intermediate stages that can be shipped.
I don't think you'll find a study like that. At least you should define what you mean by "cost". Because OOP designing is somehow slower, so on the short term development is maybe faster with procedural programming. On very short term maybe spaghetti coding is even more faster.
But when project begins growing things are opposite, because OOP designing is best featured to manage code complexity.
So in a small project maybe procedural design MAY be cheaper, because it's faster and you don't have drawbacks.
But in a big project you'll get stick very quickly using only a simple paradigm like procedural programming
I doubt you will find a definitive study. As several people have mentioned this is not a reproducible experiment. You will find anecdotal evidence, a lot of it. Some people may find some statistical studies, but I would examine them carefully. I am not aware of any really good ones.
I also will make another point, there is no such thing as purely object oriented or purely procedural in the real world. Many if not most object methods are written with procedural code. At the same time many procedural programs use OO methodologies such as encapsulation (also call abstraction by some).
Don't get me wrong, OO and procedural programs look and are different, but it is a matter of dark gray vs light gray instead of black and white.
This article says nothing about OOP vs Procedural. But I'd think that you could use similar metrics from your company for a discussion.
I find it interesting as my company is starting to explore the ROWE initiative. In our first session, it was apparent that we don't currently capture enough metrics on outcomes.
So you need to focus on 1) Is the maintenance of current processes impeding future development? 2) How are different methods going to affect #1?