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When all you have is a pair of bolt cutters and a bottle of vodka, everything looks like the lock on the door of Wolf Blitzer's boathouse. (Replace that with a hammer and a nail if you don't read xkcd)
I currently program Clojure, Python, Java and PHP, so I am familiar with the C and LISP syntax as well as the whitespace thing. I know imperative, functional, immutable, OOP and a couple type systems and other things. Now I want more!
What are languages that take a different approach and would be useful for either practical tool choosing or theoretical understanding?
I don't feel like learning another functional language(Haskell) or another imperative OOP language(Ruby), nor do I want to practice impractical fun languages like Brainfuck.
One very interesting thing I found myself are monoiconic stack based languages like Factor.
Only when I feel I understand most concepts and have answers to all my questions, I want to start thinking about my own toy language to contain all my personal preferences.
Matters of practicality are highly subjective, so I will simply say that learning different language paradigms will only serve to make you a better programmer. What is more practical than that?
Functional, Haskell - I know you said that you didn't want to, but you should really really reconsider. You've gotten some functional exposure with Clojure and even Python, but you've not experienced it to its fullest without Haskell. If you're really against Haskell then good compromises are either ML or OCaml.
Declarative, Datalog - Many people would recommend Prolog in this slot, but I think Datalog is a cleaner example of a declarative language.
Array, J - I've only just discovered J, but I find it to be a stunning language. It will twist your mind into a pretzel. You will thank J for that.
Stack, Factor/Forth - Factor is very powerful and I plan to dig into it ASAP. Forth is the grand-daddy of the Stack languages, and as an added bonus it's simple to implement yourself. There is something to be said about learning through implementation.
Dataflow, Oz - I think the influence of Oz is on the upswing and will only continue to grow in the future.
Prototype-based, JavaScript / Io / Self - Self is the grand-daddy and highly influential on every prototype-based language. This is not the same as class-based OOP and shouldn't be treated as such. Many people come to a prototype language and create an ad-hoc class system, but if your goal is to expand your mind, then I think that is a mistake. Use the language to its full capacity. Read Organizing Programs without Classes for ideas.
Expert System, CLIPS - I always recommend this. If you know Prolog then you will likely have the upper-hand in getting up to speed, but it's a very different language.
Frink - Frink is a general purpose language, but it's famous for its system of unit conversions. I find this language to be very inspiring in its unrelenting drive to be the best at what it does. Plus... it's really fun!
Functional+Optional Types, Qi - You say you've experience with some type systems, but do you have experience with "skinnable* type systems? No one has... but they should. Qi is like Lisp in many ways, but its type system will blow your mind.
Actors+Fault-tolerance, Erlang - Erlang's process model gets a lot of the buzz, but its fault-tolerance and hot-code-swapping mechanisms are game-changing. You will not learn much about FP that you wouldn't learn with Clojure, but its FT features will make you wonder why more languages can't seem to get this right.
Enjoy!
What about Prolog (for unification/backtracking etc), Smalltalk (for "everything's a message"), Forth (reverse polish, threaded interpreters etc), Scheme (continuations)?
Not a language, but the Art of the Metaobject Protocol is mind-bending stuff
I second Haskell. Don't think "I know a Lisp, so I know functional programming". Ever heard of type classes? Algebraic data types? Monads? "Modern" (more or less - at least not 50 years old ;) ) functional languages, especially Haskell, have explored a plethora of very powerful useful new concepts. Type classes add ad-hoc polymorphism, but type inference (yet another thing the languages you already know don't have) works like a charm. Algebraic data types are simply awesome, especially for modelling trees-like data structures, but work fine for enums or simple records, too. And monads... well, let's just say people use them to make exceptions, I/O, parsers, list comprehensions and much more - in purely functional ways!
Also, the whole topic is deep enough to keep one busy for years ;)
I currently program Clojure, Python, Java and PHP [...] What are languages that take a different approach and would be useful for either practical tool choosing or theoretical understanding?
C
There's a lot of C code lying around---it's definitely practical. If you learn C++ too, there's a big lot of more code around (and the leap is short once you know C and Java).
It also gives you (or forces you to have) a great understanding of some theoretical issues; for instance, each running program lives in a 4 GB byte array, in some sense. Pointers in C are really just indices into this array---they're just a different kind of integer. No different in Java, Python, PHP, except hidden beneath a surface layer.
Also, you can write object-oriented code in C, you just have to be a bit manual about vtables and such. Simon Tatham's Portable Puzzle Collection is a great example of fairly accessible object-oriented C code; it's also fairly well designed and well worth a read to a beginner/intermediate C programmer. This is what happens in Haskell too---type classes are in some sense "just another vtable".
Another great thing about C: engaging in Q&A with skilled C programmers will get you a lot of answers that explain C in terms of lower-level constructs, which builds your closer-to-the-iron knowledge base.
I may be missing OP's point---I think I am, judging by the other answers---but I think it might be a useful answer to other people who have a similar question and read this thread.
From Peter Norvig's site:
"Learn at least a half dozen programming languages. Include one language that supports class abstractions (like Java or C++), one that supports functional abstraction (like Lisp or ML), one that supports syntactic abstraction (like Lisp), one that supports declarative specifications (like Prolog or C++ templates), one that supports coroutines (like Icon or Scheme), and one that supports parallelism (like Sisal). "
http://norvig.com/21-days.html
I'm amazed that after 6 months and hundreds of votes, noone has mentioned SQL ...
In the types as theorems / advanced type systems: Coq ( I think Agda comes in this category too).
Coq is a proof assistant embedded into a functional programing language.
You can write mathematical proofs and Coq helps to build a solution.
You can write functions and prove properties about it.
It has dependent types, that alone blew my mind. A simple example:
concatenate: forall (A:Set)(n m:nat), (array A m)->(array A n)->(array A (n+m))
is the signature of a function that concatenates two arrays of size n and m of elements of A and returns an array of size (n+m). It won't compile if the function doesn't return that!
Is based on the calculus of inductive constructions, and it has a solid theory behind it.
I'm not smart enough to understand it all, but I think is worth taking a look, specially if you trend towards type theory.
EDIT: I need to mention: you write a function in Coq and then you can PROVE it is correct for any input, that is amazing!
One of the languages which i am interested for have a very different point of view (including a new vocabulary to define the language elements and a radical diff syntax) is J. Haskell would be the obvious choice for me, although it is a functional lang, cause its type system and other unique features open your mind and makes you rethink you previous knowledge in (functional) programming.
Just like fogus has suggested it to you in his list, I advise you too to look at the language OzML/Mozart
Many paradigms, mainly targetted at concurrency/multi agent programming.
Concerning concurrency, and distributed calculus, the equivalent of Lambda calculus (which is behind functionnal programming) is called the Pi Calculus.
I have only started begining to look at some implementation of the Pi calculus. But they already have enlarged my conceptions of computing.
Pict
Nomadic Pict
FunLoft. (this one is pretty recent, conceived at INRIA)
Dataflow programming, aka flow-based programming is a good step ahead on the road. Some buzzwords: paralell processing, rapid prototyping, visual programming (not as bad as sounds first).
Wikipedia's articles are good:
In computer science, flow-based
programming (FBP) is a programming
paradigm that defines applications as
networks of "black box" processes,
which exchange data across predefined
connections by message passing, where
the connections are specified
externally to the processes. These
black box processes can be reconnected
endlessly to form different
applications without having to be
changed internally. FBP is thus
naturally component-oriented.
http://en.wikipedia.org/wiki/Flow-based_programming
http://en.wikipedia.org/wiki/Dataflow_programming
http://en.wikipedia.org/wiki/Actor_model
Read JPM's book: http://jpaulmorrison.com/fbp/
(We've written a simple implementation in C++ for home automation purposes, and we're very happy with it. Documentation is under construction.)
You've learned a lot of languages. Now is the time to focus on one language, and master it.
perhaps you might want to try LabView for it's visual programming, although it's for engineering purposes.
nevertheless, you seem pretty interested in all that's out there, hence the suggestion
also, you could try the android appinventor for visually building stuff
Bruce A. Tate, taking a page from The Pragmatic Programmer wrote a book on exactly that:
Seven Languages in Seven Weeks: A Pragmatic Guide to Learning Programming Languages
In the book, he covers Clojure, Haskell, Io, Prolog, Scala, Erlang, and Ruby.
Mercury: http://www.mercury.csse.unimelb.edu.au/
It's a typed Prolog, with uniqueness types and modes (i.e. specifying that the predicate append(X,Y,Z) meaning X appended to Y is Z yields one Z given an X and Y, but can yield multiple X/Ys for a given Z). Also, no cut or other extra-logical predicates.
If you will, it's to Prolog as Haskell is to Lisp.
Programming does not cover the task of programmers.
New things are always interesting, but there are some very cool old stuff.
The first database system was dBaseIII for me, I was spending about a month to write small examples (dBase/FoxPro/Clipper is a table-based db with indexes). Then, at my first workplace, I met MUMPS, and I got headache. I was young and fresh-brained, but it took 2 weeks to understand the MUMPS database model. There was a moment, like in comics: after 2 weeks, a button has been switched on, and the bulb has just lighten up in my mind. MUMPS is natural, low level, and very-very fast. (It's an unbalanced, unformalized btree without types.) Today's trends shows the way back to it: NoSQL, key-value db, multidimensional db - so there are only some steps left, and we reach Mumps.
Here's a presentation about MUMPS's advantages: http://www.slideshare.net/george.james/mumps-the-internet-scale-database-presentation
A short doc on hierarchical db: http://www.cs.pitt.edu/~chang/156/14hier.html
An introduction to MUMPS globals (in MUMPS, local variables, short: locals are the memory variables, and the global variables, short: globals are the "db variables", setting a global variable goes to the disk immediatelly):
http://gradvs1.mgateway.com/download/extreme1.pdf (PDF)
Say you want to write a love poem...
Instead of using a hammer just because there's one already in your hand, learn the proper tools for the task: learn to speak French.
Once you've reached near-native speaking level, you're ready to start your poem.
While learning new languages on an academical level is an interesting hobby, IMHO you can't really learn to use one until you try to apply it to a real world problem. So, rather than looking for a new language to learn, I'd in your place first look for a new things to build, and only then I'd look for the right language to use for that one specific project. First pick the problem, then the tool, not the other way around..
For anyone who hasn't been around since the mid 80's, I'd suggest learning 8-bit BASIC. It's very low-level, very primitive and it's an interesting exercise to program around its holes.
On the same line, I'd pick an HP-41C series calculator (or emulator, although nothing beats real hardware). It's hard to wrap your brain around it, but well worth it. A TI-57 will do, but will be a completely different experience. If you manage to solve second degree equations on a TI-55, you'll be considered a master (it had no conditionals and no branches except a RST, that jumped the program back to step 0).
And last, I'd pick FORTH (it was mentioned before). It has a nice "build your language" Lisp-ish thing, but is much more bare metal. It will teach you why Rails is interesting and when DSLs make sense and you'll have a glipse on what your non-RPN calculator is thinking while you type.
PostScript. It is a rather interesting language as it's stack based, and it's quite practical once you want to put things on paper and you want either to get it done or troubleshoot why isn't it getting done.
Erlang. The intrinsic parallelism gives it a rather unusual feel and you can again learn useful things from that. I'm not so sure about practicality, but it can be useful for some fast prototyping tasks and highly redundant systems.
Try programming GPUs - either CUDA or OpenCL. It's just C/C++ extensions, but the mental model of the architecture is again completely different from the classic approach, and it definitely gets practical once you need to get some real number crunching done.
Erlang, Forth and some embedded work with assembly language. Really; buy an Arduino kit or something similar, and create a polyphonic beep in assembly. You'll really learn something.
There's also anic:
https://code.google.com/p/anic/
From its site:
Faster than C, Safer than Java, Simpler than *sh
anic is the reference implementation compiler for the experimental, high-performance, implicitly parallel, deadlock-free general-purpose dataflow programming language ANI.
It doesn't seem to be under active development anymore, but it seems to have some interesting concepts (and that, after all, is what you seem to be after).
While not meeting your requirement of "different" - I'd wager that Fantom is a language that a professional programmer should look at. By their own admission, the authors of fantom call it a boring language. It merely shores up the most common use cases of Java and C#, with some borrowed closure syntax from ruby and similar newer languages.
And yet it manages to have its own bootstrapped compiler, provide a platform that has a drop in install with no external dependencies, gets packages right - and works on Java, C# and now the Web (via js).
It may not widen your horizons in terms of new ways of programming, but it will certainly show you better ways of programming.
One thing that I see missing from the other answers: languages based on term-rewriting.
You could take a look at Pure - http://code.google.com/p/pure-lang/ .
Mathematica is also rewriting based, although it's not so easy to figure out what's going on, as it's rather closed.
APL, Forth and Assembly.
Have some fun. Pick up a Lego Mindstorm robot kit and CMU's RobotC and write some robotics code. Things happen when you write code that has to "get dirty" and interact with the real world that you cannot possibly learn in any other way. Yes, same language, but a very different perspective.
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I am currently doing a dissertation about the implications or dangers that today's software development practices or teachings may have on the long term effects of programming.
Just to make it clear: I am not attacking the use abstractions in programming. Every programmer knows that abstractions are the bases for modularity.
What I want to investigate with this dissertation are the positive and negative effects abstractions can have in software development. As regards the positive, I am sure that I can find many sources that can confirm this. But what about the negative effects of abstractions? Do you have any stories to share that talk about when certain abstractions failed on you?
The main concern is that many programmers today are programming against abstractions without having the faintest idea of what the abstraction is doing under-the-covers. This may very well lead to bugs and bad design. So, in you're opinion, how important is it that programmers actually know what is going below the abstractions?
Taking a simple example from Joel's Back to Basics, C's strcat:
void strcat( char* dest, char* src )
{
while (*dest) dest++;
while (*dest++ = *src++);
}
The above function hosts the issue that if you are doing string concatenation, the function is always starting from the beginning of the dest pointer to find the null terminator character, whereas if you write the function as follows, you will return a pointer to where the concatenated string is, which in turn allows you to pass this new pointer to the concatenation function as the *dest parameter:
char* mystrcat( char* dest, char* src )
{
while (*dest) dest++;
while (*dest++ = *src++);
return --dest;
}
Now this is obviously a very simple as regards abstractions, but it is the same concept I shall be investigating.
Finally, what do you think about the issue that schools are preferring to teach Java instead of C and Lisp ?
Can you please give your opinions and your says as regards this subject?
Thank you for your time and I appreciate every comment.
First of all, abstractions are inevitable because they help us to deal with the mind-blowing complexity of things.
Abstractions are also inevitable because it is more and more required of an individual to undertake more tasks or even complete projects. To address the problem, one uses libraries which wrap lower-level concepts and expose more complex behavior.
Naturally, a developer has less and less time to know the intrinsics of the things. The latest concern I heard about on SO pages is starting to learn JavaScript with jQuery library, ignoring the raw JavaScript at all.
The issue is about the balance between:
Know the little tiniest details of some technology and be a master of it, but at the same time being unable to work with anything else.
Superficial knowledge of a wide variety of technologies and tools which however proves sufficient for common everyday tasks which allows an individual to perform in multiple areas possibly covering all sides of some (moderately big) project.
Take your pick.
Some work requires the one, another position requires the other.
So, in you're opinion, how important is it that programmers actually know what is going below the abstractions?
It would be nice if people knew what is happening behind the scenes. This knowledge comes with time and practice, up to a certain degree. Depends on what kind of tasks you have. You certainly shouldn't blame people for not knowing everything. If you wish a person to be able to perform in a variety of fields, it is inevitable he won't have time to cover each up to the last bit.
What is essential, is the knowledge of the basic building blocks. Data structures, algorithms, complexity. That should provide a basis for everything else.
Knowing tiniest details of some particular technology is good, but not essential. Anyway, you can't learn them all. They're too many and they keep coming.
Finally, what do you think about the issue that schools are preferring to teach Java instead of C and Lisp ?
Schools shouldn't be teaching programming languages at all. They're to teach basics of theoretical and practical CS, social skills, communication, team work. To cover a vast variety of topics and problems to provide a wide angle view for their graduates. This will help them to find their way. Whatever they need to know in details, they'll do it on their own.
An example where abstraction has failed:
In this case, a piece of software was needed to communicate to many different third party data processors. The communication was done through various messaging protocols; the transport method/protocol is not important in this case. Just assume everyone communicated through messaging.
The idea was to abstract the features of each of these third parties into a single, unified message format. It seemed relatively straightforward because each of the third parties performed a similar service. The problem was that some third parties used different terms to explain similar features. It was also found that some third parties had additional features that other third parties did not have.
The designers of the abstraction did not see through the difference of third party terms nor did they think it was reasonable to limit the scope of the unified features to only support the common features of the third parties. Instead, a single, monolithic message schema was developed to support any and all features of the third parties considered at the time. In what was probably considered a future-proofing move, they added a means of also passing an infinite number of name/value pairs along with the monolithic message in case there were future data elements that the monolithic message could not handle.
Early on, it became clear that changing the monolithic message was going to be difficult due to so many people using it in mission critical systems. The use of the name/value pairs increased. Each name that could be used was documented inside a large spreadsheet, and developers were required to consult the spreadsheet to avoid duplication of name value function. The list got so large, however, that it was found that there were frequently collisions in purposes of name values.
The majority of the monolithic message's fields now have no purpose and are kept mainly for backwards compatibility. There are name values that can be used to replace fields in the monolithic message. The majority of the interfacing is now done through the name/value pairs. In cases where the client is intending to communicate with more than one third party, each client needs to reconcile the name values available for each third party. It would be almost simpler to interface directly to the third party themselves.
I believe this illustrates that, from a consumer of the monolithic message perspective, that it is important that developers of the consuming code not know what is happening under the covers. If the designers had considered that the consumers of the monolithic message should not have to understand the abstraction in great detail, the monolithic message and it's associated name/value pairs might never have happened. Documenting the abstraction with assertions regarding input and expected output would make life so much simpler.
As for colleges not teaching C and Lisp....they are cheating the students. You get a better understanding of what is going on with the machine and OS with C. You get a bit of a different perspective on processing data and approaching problems with Lisp. I have used some of the ideas I learned using Lisp in programs written in C, C++, .Net, and Java. Learning Java after knowing even just C is not very difficult. The OO part is really not programming language specific, so perhaps using Java for that is acceptable.
An understanding of fundamentals of algorithms (e.g. time complexity) and some knowledge about the metal is essential to designing/writing smells-good code.
I would suggest, though, that just as important is education in modern abstractions and profiling. I feel that modern abstractions make me so much more productive than I would be without them that they are at least as important as good fundamentals, if not more so.
An important element that lacked in my education was the use of profilers. When used routinely and correctly, profilers can help mitigate problems with poor fundamentals.
Since you quote Joel Spolsky, I take it your aware of his "Law of Leaky Abstractions"? I'll mention it for future readers. http://www.joelonsoftware.com/articles/LeakyAbstractions.html
Green & Blackwell's Ironies of Abstractions talks a bit about the effort of learning the abstraction. http://homepage.ntlworld.com/greenery/workStuff/Papers/index.html
The term "astronaut architecture" is a reaction to over-abstraction.
I know I certainly curse abstraction when I haven't touched Java or C# in a while and i want to write to a file, but have to instance a Stream...Writer...Adaptor....Handler....
Also, Patterns, as in Gang Of Four. Seemed great when I first read about them in the mid-90's, but can never remember factory, facade, interface, helper, worker, flyweight....
I remember when I was in DSA I was like wtf O(n) and wondering where would I use it other than in grad school or if you're not a PhD like Bloch. Somehow uses for it does pop up in business analysis, so I was wondering when have you guys had to call up your Big O skills to see how to write an algorithm, which data structure did you use to fit or whether you had to actually create a new ds (like your own implementation of a splay tree or trie).
Understanding Data Structures has been fundamental to many of the projects I've worked on, and that goes beyond the ten minute song 'n dance one does when asked such a question in an interview situation.
Granted that modern environments with all sorts of collection classes can make light work of storing and accessing large amounts of data, but having an understanding that a particular problem is best solved with a particular data structure can be a great timesaver. And by "timesaver" I mean "the difference between something working and not working".
Honestly, being able to answer that stuff is my biggest criterion for taking interviewees seriously in an interview. Knowing how basic data structures work, basic O(n) analysis, and some light theory is really crucial to being able to write large applications successfully.
It's important in the interview because it's important in the job. I've worked with techs in the past that were self taught, without taking the data structures course or reading a data structures book, and their code is occasionally bad in ways they should have seen coming.
If you don't know that n2 is going to run slowly compared to n log n, you've got more to learn.
As far as the later half of the data structures courses, it isn't generally applicable to most tech jobs, but if you ever do wind up needing it, you'll wish you had paid more attention.
Big-O notation is one of the basic notations used when describing algorithms implemented by a particular library. For example, all documentation on STL that I've seen describes various operations in terms of big-O, so naturally you have to e.g. understand the difference between O(1), O(log n) and O(n) to understand the implications of your choice of STL containers and algorithms. MSDN also does that for .NET classes, and IIRC Java documentation does that for standard Java classes. So, I'd say that knowing the notation is pretty much a requirement for understanding documentation of most popular frameworks out there.
Sure (even though I'm a humble MS in EE -- no PhD, no CS, differently from my colleague Joshua Block), I write a lot of stuff that needs to be highly scalable (or components that may need to be reused in highly scalable apps), so big-O considerations are most always at work in my design (and it's not hard to take them into account). The data structures I use are almost always from Python's simple but rich supply (which I did lend a hand developing;-), rarely is a totally custom one needed (rather than building on top of list, dict, etc); but when it does happen (e.g. the bitvectors in my open source project gmpy), no big deal.
I was able to use B-Trees right when I learned about them in algorithm class (that was about 15 years ago when there were much less open source implementations available). And even later the knowledge about the differences of e. g. container classes came in handy...
Absolutely: even though stacks, queues, etc. are pretty straightforward, it helps to have been introduced to them in a disciplined fashion.
B-Tree's and more advanced sorting are a bit more difficult so learning them early was a big benefit and I have indeed had to implement each of them at various points.
Finally, I created an algorithm for single-connected components a few years back that was significantly better than the one our signal-processing team was using but I couldn't convince them that it was better until I could show that it was O(n) complexity rather than O(nlogn).
...just to name a few examples.
Of course, if you are content to remain a CRUD-system hacker with no real desire to do more than collect a paycheck, then it may not be necessary...
I found my knowledge of data structures very useful when I needed to implement a customizable event-driven system about ten years ago. That's the biggie, but I use that sort of knowledge fairly frequently in lesser ways.
For me, knowing the exact algorithms has been... nice as background knowledge. However, the thing that's been the most useful is the more general background of having to pay attention to how different pieces of an algorithm interact. For instance, there can be places in code where moving one piece of code (ie, outside a loop) can make a huge difference in both time and space.
Its less of the specific knowledge the course taught and, rather, more that it acted like several years of experience. The course took something that might take years to encounter (have drilled into you) all the variations of in pure "real world experience" and condensed it.
The title of your question asks about data structures and algorithms, but the body of your question focuses on complexity analysis, so I'll focus on that too:
There are lots of programming jobs where being able to do complexity analysis is at least occasionally useful. See What career can I hope for if I like algorithms? for some examples of these.
I can think of several instances in my career where either I or a co-worker have discovered a a piece of code where the (usually time, sometimes space) complexity was higher that it should have been. eg: something that was quadratic or cubic when it could have been linear or nlog(n). Such code would work fine when given small inputs, but on larger inputs would quickly become really slow or consume all available memory. Knowing alternative algorithms and data structures, their complexities, and also how to analyze the complexity to build new algorithms is vital in being able to correct these problems (or avoid them in the first place).
Networking is all I've used it: in an implementation of traveling salesman.
Unfortunately I do a lot of "line of business" and "forms over data" apps, so most problems I work on can be solved by hammering together arrays, linked lists, and hash tables. However, I've had the chance to work my data structures magic here and there:
Due to weird complex business rules, I worked on an application which used a custom thread pool implemented as a leftist-heap.
My dev team struggled to write a complex multithreaded app. It was plagued with race conditions, dead locks, and lousy performance due to very fine-grained locking. We re-worked the code to share state between threads, opting to write a very light-weight wrapper to facilitate message passing. Next, we converting our linked lists and hash tables to immutable stacks and immutable style and immutable red-black trees, we had no more problems with thread safety or performance. The resulting code was immaculate and surprisingly readable.
Frequently, a business rules engine requires you to roll your own state machine, which is very naturally modelled as a graph where vertexes and states and edges are transitions between states.
If for no other reasons, I'm glad I took the time to readable about data structures and algorithms simply to be able picture novel problems a little differently, especially combinatorial problems and graph problems. Graph theory is no longer a synonym for "scary".
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This is definitely subjective, but I'd like to try to avoid it becoming argumentative. I think it could be an interesting question if people treat it appropriately.
The idea for this question came from the comment thread from my answer to the "What are five things you hate about your favorite language?" question. I contended that classes in C# should be sealed by default - I won't put my reasoning in the question, but I might write a fuller explanation as an answer to this question. I was surprised at the heat of the discussion in the comments (25 comments currently).
So, what contentious opinions do you hold? I'd rather avoid the kind of thing which ends up being pretty religious with relatively little basis (e.g. brace placing) but examples might include things like "unit testing isn't actually terribly helpful" or "public fields are okay really". The important thing (to me, anyway) is that you've got reasons behind your opinions.
Please present your opinion and reasoning - I would encourage people to vote for opinions which are well-argued and interesting, whether or not you happen to agree with them.
Programmers who don't code in their spare time for fun will never become as good as those that do.
I think even the smartest and most talented people will never become truly good programmers unless they treat it as more than a job. Meaning that they do little projects on the side, or just mess with lots of different languages and ideas in their spare time.
(Note: I'm not saying good programmers do nothing else than programming, but they do more than program from 9 to 5)
The only "best practice" you should be using all the time is "Use Your Brain".
Too many people jumping on too many bandwagons and trying to force methods, patterns, frameworks etc onto things that don't warrant them. Just because something is new, or because someone respected has an opinion, doesn't mean it fits all :)
EDIT:
Just to clarify - I don't think people should ignore best practices, valued opinions etc. Just that people shouldn't just blindly jump on something without thinking about WHY this "thing" is so great, IS it applicable to what I'm doing, and WHAT benefits/drawbacks does it bring?
"Googling it" is okay!
Yes, I know it offends some people out there that their years of intense memorization and/or glorious stacks of programming books are starting to fall by the wayside to a resource that anyone can access within seconds, but you shouldn't hold that against people that use it.
Too often I hear googling answers to problems the result of criticism, and it really is without sense. First of all, it must be conceded that everyone needs materials to reference. You don't know everything and you will need to look things up. Conceding that, does it really matter where you got the information? Does it matter if you looked it up in a book, looked it up on Google, or heard it from a talking frog that you hallucinated? No. A right answer is a right answer.
What is important is that you understand the material, use it as the means to an end of a successful programming solution, and the client/your employer is happy with the results.
(although if you are getting answers from hallucinatory talking frogs, you should probably get some help all the same)
Most comments in code are in fact a pernicious form of code duplication.
We spend most of our time maintaining code written by others (or ourselves) and poor, incorrect, outdated, misleading comments must be near the top of the list of most annoying artifacts in code.
I think eventually many people just blank them out, especially those flowerbox monstrosities.
Much better to concentrate on making the code readable, refactoring as necessary, and minimising idioms and quirkiness.
On the other hand, many courses teach that comments are very nearly more important than the code itself, leading to the this next line adds one to invoiceTotal style of commenting.
XML is highly overrated
I think too many jump onto the XML bandwagon before using their brains...
XML for web stuff is great, as it's designed for it. Otherwise I think some problem definition and design thoughts should preempt any decision to use it.
My 5 cents
Not all programmers are created equal
Quite often managers think that DeveloperA == DeveloperB simply because they have same level of experience and so on. In actual fact, the performance of one developer can be 10x or even 100x that of another.
It's politically risky to talk about it, but sometimes I feel like pointing out that, even though several team members may appear to be of equal skill, it's not always the case. I have even seen cases where lead developers were 'beyond hope' and junior devs did all the actual work - I made sure they got the credit, though. :)
I fail to understand why people think that Java is absolutely the best "first" programming language to be taught in universities.
For one, I believe that first programming language should be such that it highlights the need to learn control flow and variables, not objects and syntax
For another, I believe that people who have not had experience in debugging memory leaks in C / C++ cannot fully appreciate what Java brings to the table.
Also the natural progression should be from "how can I do this" to "how can I find the library which does that" and not the other way round.
If you only know one language, no matter how well you know it, you're not a great programmer.
There seems to be an attitude that says once you're really good at C# or Java or whatever other language you started out learning then that's all you need. I don't believe it- every language I have ever learned has taught me something new about programming that I have been able to bring back into my work with all the others. I think that anyone who restricts themselves to one language will never be as good as they could be.
It also indicates to me a certain lack of inquistiveness and willingness to experiment that doesn't necessarily tally with the qualities I would expect to find in a really good programmer.
Performance does matter.
Print statements are a valid way to debug code
I believe it is perfectly fine to debug your code by littering it with System.out.println (or whatever print statement works for your language). Often, this can be quicker than debugging, and you can compare printed outputs against other runs of the app.
Just make sure to remove the print statements when you go to production (or better, turn them into logging statements)
Your job is to put yourself out of work.
When you're writing software for your employer, any software that you create is to be written in such a way that it can be picked up by any developer and understood with a minimal amount of effort. It is well designed, clearly and consistently written, formatted cleanly, documented where it needs to be, builds daily as expected, checked into the repository, and appropriately versioned.
If you get hit by a bus, laid off, fired, or walk off the job, your employer should be able to replace you on a moment's notice, and the next guy could step into your role, pick up your code and be up and running within a week tops. If he or she can't do that, then you've failed miserably.
Interestingly, I've found that having that goal has made me more valuable to my employers. The more I strive to be disposable, the more valuable I become to them.
1) The Business Apps farce:
I think that the whole "Enterprise" frameworks thing is smoke and mirrors. J2EE, .NET, the majority of the Apache frameworks and most abstractions to manage such things create far more complexity than they solve.
Take any regular Java or .NET ORM, or any supposedly modern MVC framework for either which does "magic" to solve tedious, simple tasks. You end up writing huge amounts of ugly XML boilerplate that is difficult to validate and write quickly. You have massive APIs where half of those are just to integrate the work of the other APIs, interfaces that are impossible to recycle, and abstract classes that are needed only to overcome the inflexibility of Java and C#. We simply don't need most of that.
How about all the different application servers with their own darned descriptor syntax, the overly complex database and groupware products?
The point of this is not that complexity==bad, it's that unnecessary complexity==bad. I've worked in massive enterprise installations where some of it was necessary, but even in most cases a few home-grown scripts and a simple web frontend is all that's needed to solve most use cases.
I'd try to replace all of these enterprisey apps with simple web frameworks, open source DBs, and trivial programming constructs.
2) The n-years-of-experience-required:
Unless you need a consultant or a technician to handle a specific issue related to an application, API or framework, then you don't really need someone with 5 years of experience in that application. What you need is a developer/admin who can read documentation, who has domain knowledge in whatever it is you're doing, and who can learn quickly. If you need to develop in some kind of language, a decent developer will pick it up in less than 2 months. If you need an administrator for X web server, in two days he should have read the man pages and newsgroups and be up to speed. Anything less and that person is not worth what he is paid.
3) The common "computer science" degree curriculum:
The majority of computer science and software engineering degrees are bull. If your first programming language is Java or C#, then you're doing something wrong. If you don't get several courses full of algebra and math, it's wrong. If you don't delve into functional programming, it's incomplete. If you can't apply loop invariants to a trivial for loop, you're not worth your salt as a supposed computer scientist. If you come out with experience in x and y languages and object orientation, it's full of s***. A real computer scientist sees a language in terms of the concepts and syntaxes it uses, and sees programming methodologies as one among many, and has such a good understanding of the underlying philosophies of both that picking new languages, design methods, or specification languages should be trivial.
Getters and Setters are Highly Overused
I've seen millions of people claiming that public fields are evil, so they make them private and provide getters and setters for all of them. I believe this is almost identical to making the fields public, maybe a bit different if you're using threads (but generally is not the case) or if your accessors have business/presentation logic (something 'strange' at least).
I'm not in favor of public fields, but against making a getter/setter (or Property) for everyone of them, and then claiming that doing that is encapsulation or information hiding... ha!
UPDATE:
This answer has raised some controversy in it's comments, so I'll try to clarify it a bit (I'll leave the original untouched since that is what many people upvoted).
First of all: anyone who uses public fields deserves jail time
Now, creating private fields and then using the IDE to automatically generate getters and setters for every one of them is nearly as bad as using public fields.
Many people think:
private fields + public accessors == encapsulation
I say (automatic or not) generation of getter/setter pair for your fields effectively goes against the so called encapsulation you are trying to achieve.
Lastly, let me quote Uncle Bob in this topic (taken from chapter 6 of "Clean Code"):
There is a reason that we keep our
variables private. We don't want
anyone else to depend on them. We want
the freedom to change their type or
implementation on a whim or an
impulse. Why, then, do so many
programmers automatically add getters
and setters to their objects, exposing
their private fields as if they were
public?
UML diagrams are highly overrated
Of course there are useful diagrams e.g. class diagram for the Composite Pattern, but many UML diagrams have absolutely no value.
Opinion: SQL is code. Treat it as such
That is, just like your C#, Java, or other favorite object/procedure language, develop a formatting style that is readable and maintainable.
I hate when I see sloppy free-formatted SQL code. If you scream when you see both styles of curly braces on a page, why or why don't you scream when you see free formatted SQL or SQL that obscures or obfuscates the JOIN condition?
Readability is the most important aspect of your code.
Even more so than correctness. If it's readable, it's easy to fix. It's also easy to optimize, easy to change, easy to understand. And hopefully other developers can learn something from it too.
If you're a developer, you should be able to write code
I did quite a bit of interviewing last year, and for my part of the interview I was supposed to test the way people thought, and how they implemented simple-to-moderate algorithms on a white board. I'd initially started out with questions like:
Given that Pi can be estimated using the function 4 * (1 - 1/3 + 1/5 - 1/7 + ...) with more terms giving greater accuracy, write a function that calculates Pi to an accuracy of 5 decimal places.
It's a problem that should make you think, but shouldn't be out of reach to a seasoned developer (it can be answered in about 10 lines of C#). However, many of our (supposedly pre-screened by the agency) candidates couldn't even begin to answer it, or even explain how they might go about answering it. So after a while I started asking simpler questions like:
Given the area of a circle is given by Pi times the radius squared, write a function to calculate the area of a circle.
Amazingly, more than half the candidates couldn't write this function in any language (I can read most popular languages so I let them use any language of their choice, including pseudo-code). We had "C# developers" who could not write this function in C#.
I was surprised by this. I had always thought that developers should be able to write code. It seems that, nowadays, this is a controversial opinion. Certainly it is amongst interview candidates!
Edit:
There's a lot of discussion in the comments about whether the first question is a good or bad one, and whether you should ask questions as complex as this in an interview. I'm not going to delve into this here (that's a whole new question) apart from to say you're largely missing the point of the post.
Yes, I said people couldn't make any headway with this, but the second question is trivial and many people couldn't make any headway with that one either! Anybody who calls themselves a developer should be able to write the answer to the second one in a few seconds without even thinking. And many can't.
The use of hungarian notation should be punished with death.
That should be controversial enough ;)
Design patterns are hurting good design more than they're helping it.
IMO software design, especially good software design is far too varied to be meaningfully captured in patterns, especially in the small number of patterns people can actually remember - and they're far too abstract for people to really remember more than a handful. So they're not helping much.
And on the other hand, far too many people become enamoured with the concept and try to apply patterns everywhere - usually, in the resulting code you can't find the actual design between all the (completely meaningless) Singletons and Abstract Factories.
Less code is better than more!
If the users say "that's it?", and your work remains invisible, it's done right. Glory can be found elsewhere.
PHP sucks ;-)
The proof is in the pudding.
Unit Testing won't help you write good code
The only reason to have Unit tests is to make sure that code that already works doesn't break. Writing tests first, or writing code to the tests is ridiculous. If you write to the tests before the code, you won't even know what the edge cases are. You could have code that passes the tests but still fails in unforeseen circumstances.
And furthermore, good developers will keep cohesion low, which will make the addition of new code unlikely to cause problems with existing stuff.
In fact, I'll generalize that even further,
Most "Best Practices" in Software Engineering are there to keep bad programmers from doing too much damage.
They're there to hand-hold bad developers and keep them from making dumbass mistakes. Of course, since most developers are bad, this is a good thing, but good developers should get a pass.
Write small methods. It seems that programmers love to write loooong methods where they do multiple different things.
I think that a method should be created wherever you can name one.
It's ok to write garbage code once in a while
Sometimes a quick and dirty piece of garbage code is all that is needed to fulfill a particular task. Patterns, ORMs, SRP, whatever... Throw up a Console or Web App, write some inline sql ( feels good ), and blast out the requirement.
Code == Design
I'm no fan of sophisticated UML diagrams and endless code documentation. In a high level language, your code should be readable and understandable as is. Complex documentation and diagrams aren't really any more user friendly.
Here's an article on the topic of Code as Design.
Software development is just a job
Don't get me wrong, I enjoy software development a lot. I've written a blog for the last few years on the subject. I've spent enough time on here to have >5000 reputation points. And I work in a start-up doing typically 60 hour weeks for much less money than I could get as a contractor because the team is fantastic and the work is interesting.
But in the grand scheme of things, it is just a job.
It ranks in importance below many things such as family, my girlfriend, friends, happiness etc., and below other things I'd rather be doing if I had an unlimited supply of cash such as riding motorbikes, sailing yachts, or snowboarding.
I think sometimes a lot of developers forget that developing is just something that allows us to have the more important things in life (and to have them by doing something we enjoy) rather than being the end goal in itself.
I also think there's nothing wrong with having binaries in source control.. if there is a good reason for it. If I have an assembly I don't have the source for, and might not necessarily be in the same place on each devs machine, then I will usually stick it in a "binaries" directory and reference it in a project using a relative path.
Quite a lot of people seem to think I should be burned at the stake for even mentioning "source control" and "binary" in the same sentence. I even know of places that have strict rules saying you can't add them.
Every developer should be familiar with the basic architecture of modern computers. This also applies to developers who target a virtual machine (maybe even more so, because they have been told time and time again that they don't need to worry themselves with memory management etc.)
Software Architects/Designers are Overrated
As a developer, I hate the idea of Software Architects. They are basically people that no longer code full time, read magazines and articles, and then tell you how to design software. Only people that actually write software full time for a living should be doing that. I don't care if you were the worlds best coder 5 years ago before you became an Architect, your opinion is useless to me.
How's that for controversial?
Edit (to clarify): I think most Software Architects make great Business Analysts (talking with customers, writing requirements, tests, etc), I simply think they have no place in designing software, high level or otherwise.
There is no "one size fits all" approach to development
I'm surprised that this is a controversial opinion, because it seems to me like common sense. However, there are many entries on popular blogs promoting the "one size fits all" approach to development so I think I may actually be in the minority.
Things I've seen being touted as the correct approach for any project - before any information is known about it - are things like the use of Test Driven Development (TDD), Domain Driven Design (DDD), Object-Relational Mapping (ORM), Agile (capital A), Object Orientation (OO), etc. etc. encompassing everything from methodologies to architectures to components. All with nice marketable acronyms, of course.
People even seem to go as far as putting badges on their blogs such as "I'm Test Driven" or similar, as if their strict adherence to a single approach whatever the details of the project project is actually a good thing.
It isn't.
Choosing the correct methodologies and architectures and components, etc., is something that should be done on a per-project basis, and depends not only on the type of project you're working on and its unique requirements, but also the size and ability of the team you're working with.
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?