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I am interested in proving that some robot controller does not reach any faulty state, which I would define by a set of predicates. I know that there are open-source software tools to achieve that. For instance, I heard of BLAST (Berkeley Lazy Abstraction Software Verification Tool), but are you aware of any other that may be simpler to use and/or more targeted to my particular application?
Have you ever used BLAST or another such tool in one of your project, and do you think that the benefits outweigh the effort needed to deploy such tools?
You might find Frama-C useful.
For evaluations by people who are not Frama-C developers, see these two articles. Some engineers developing safety-critical code (e.g. DO-178B level A) have found formal annotations and analysis based on weakest precondition techniques worth the investment, but traditional tests are very expensive for them. This last link is about Caveat, a closed-source analyzer that Frama-C intends to replace in due time.
Your question makes it sound as if you might perhaps appreciate Frama-C's Aoraï plug-in.
Whether this is all time well spent in your case is probably more a matter of whether you consider learning about these techniques a joy or a chore.
I'm thoroughly intrigued by Scheme, and have started with some toy programming examples, and am reading through Paul Graham's On Lisp.
One thing I haven't been able to find is a book or website intended to teach Scheme to "OO people", i.e. people like myself who've done 99 % of their coding in c++/Java/Python.
I see that closures are sort of object-y, in the sense that they have local state, and offer one or more functions that have access to that state. But I don't want to learn Scheme only to port my existing habits on to it. This is why I'm learning Scheme rather than Common Lisp at the moment; I fear that CLOS might just serve as a crutch to my existing OO habits.
What would be ideal is a book or website that offers case studies of problems solved in both an OO language, and also in Scheme in a Schemey way. I suppose I would most appreciate scientific computing and/or computer graphics problems, but anything would do.
Any pedagogical leads would be much appreciated.
I doubt CLOS would serve as a crutch for old habits, I found it to be pretty different from the OO style in C++/Java/Python, and very interesting. I don't understand all the details, but I would recommend Peter Seibel's Practical Common Lisp. If you are reading On Lisp without much trouble, you should be able to dive into the chapters introducing CLOS in PCL. Also, I'd recommend his Google Tech Talk comparing Java and Common Lisp.
Here's a few more recommendations to make this a more full-fledged answer:
The classic text Structure and Interpretation of Computer Programs covers quite a few examples in chapter 3 of building modular systems using closures (and addresses issues with introducing state and mutability). Chapter 2 includes some generic and data/type-directed programming which could be helpful for motivating study of CLOS. This book really needs no introduction though, it's a towering work, and I've only been reading it slowly since the spring. Highly recommended if you are interested in Scheme.
While SICP is a great book, it's not without its flaws: A really interesting look at these is the essay "The Structure and Interpretation of the Computer Science Curriculum" which elaborates on a few criticism of SICP, and is written by the authors of How to Design Programs (I haven't read HTDP but I hear it's very good). While this essay won't teach you specifically what you are looking for - comparing functional and OO programming - it is really interesting anyway. Their freshman undergraduate course starts with a first semester introduction to functional programming using Scheme (I think, PLT/Racket) and is followed by a semester of OO programming with C++ or Java... at least that's the course they describe in the essay.
These slides from Peter Norvig address some of the design patterns common in OO programming and show why they are missing or unnecessary in dynamic, functional languages like Scheme and Lisp: http://norvig.com/design-patterns/
I cautiously recommend the book by the same authors as the Little Schemer books: A Little Java, A Few Patterns. I can't say for sure if this is a really a good book or not, it was incredibly strange and there are some really bad typesetting decisions (italic, serif, variable-width, superscript doesn't belong in a text on programming), but it might be interesting to take a look at. You can probably find it cheap, anyway. Don't take this recommendation that seriously. I think it would be better to stick to the Scheme texts.
p.s. I have to disagree with one comment stating that functional programming is not as complicated at OO programming, I think that's grossly misstating it. Functional programming in all its breadth is truly mind-boggling. When you go beyond map/filter/reduce and first-class functions, and take a look at other things in the functional realm like lazy evaluation, avoiding side effects and mutation, and the strong, static-typed languages, it gets pretty interesting, and is certainly just as complicated as traditional OO programming. I've only just scratched the surface myself but have discovered a great deal of new ideas. Programming is complicated business, whether OO or functional.
Congrat you, my friend ! Love cs, love functional programming.
If you are python developer it takes 3-4 days to think in scheme
Here is the best simple tutorial I have ever met http://www.shido.info/lisp/idx_scm_e.html
I found this course http://cs.gettysburg.edu/~tneller/cs341/scheme-intro/index.html and it may be useful for you
One beginner's resource that is very helpful and geared very much toward the casual reader is "The Adventures of a Pythonista in Schemeland". It's written (obviously) from the point of view of a Python programmer taking first steps with Scheme. One especially nice thing about it is that it includes an overview of the current implementations and compatibility issues between each scheme implementation, which, unfortunately, can cause some headaches when you're just starting out.
With regards to object systems, these two documents (linked from here) give nice examples of very simple toy implementations using closures that I found helpful in understanding their use in capturing state.
If you are starting off with Scheme, have a look at How to Design Programs. This book presents the "Schemey" approach to problem solving. I don't think there is a book that compares OO and functional solutions to the same programming problems. But there is a nice presentation that shows how dynamic languages like Scheme could provide simple solutions to problems that demand complex design patterns in statically typed OOP languages.
I found this a very interesting read: http://www.devmaster.net/articles/oo-game-design/
The author repeatedly says "Wow, this could be great, if implemented carefully. This is the future!". Well, not very useful. I need code, and most of all, I need a proof that this kind of design actually works.
Do you know of an example which implements some of the concepts mentioned in this article? Maybe a small open source game one could study? Or, at least, a place where similar concepts are discussed?
Through the wise use of inheritance and over-ridden methods, and thoughtful careful design of the implied base classes
Good design is good, of course, but virtual methods are certainly no panacea, and have a significant performance cost, especially on game consoles.
Reusable in such a way that two entities created oblivious to each other could, utilizing such a development system, work together with NO changes to their code
No. Any given entity in a real game will almost invariably have certain details that tie it to that game. It will depend on certain global render state (lighting conditions, shaders, shader parameters, etc.), and will be intimately tied to the core objects used by the physics system.
This system is currently in a prototype stage, yet it has the capacity to produce mid-range quality games in as little as three months.
A number pulled entirely from the author's nether orifice.
At the very least, such a system can be used to prototype games extremely rapidly, which has its own benefits.
This may be true, but even prototyping in games is challenging. It's impossible to evaluate a rough draft of a game if it's running at half speed. Performance always matters.
In short, he's got some OK ideas in there, but it sure as hell isn't the One True Way to make games. What he describes is a massively decoupled and fine-grained architecture. That sounds nice in principle but will almost invariably lead to poor performance and an unmaintainable soup of tiny classes.
From what I understand, OOP is the most commonly used paradigm for large scale projects. I also know that some smaller subsets of big systems use other paradigms (e.g. SQL, which is declarative), and I also realize that at lower levels of computing OOP isn't really feasible. But it seems to me that usually the pieces of higher level solutions are almost always put together in a OOP fashion.
Are there any scenarios where a truly non-OOP paradigm is actually a better choice for a largescale solution? Or is that unheard of these days?
I've wondered this ever since I've started studying CS; it's easy to get the feeling that OOP is some nirvana of programming that will never be surpassed.
In my opinion, the reason OOP is used so widely isn't so much that it's the right tool for the job. I think it's more that a solution can be described to the customer in a way that they understand.
A CAR is a VEHICLE that has an ENGINE. That's programming and real world all in one!
It's hard to comprehend anything that can fit the programming and real world quite so elegantly.
Linux is a large-scale project that's very much not OOP. And it wouldn't have a lot to gain from it either.
I think OOP has a good ring to it, because it has associated itself with good programming practices like encapsulation, data hiding, code reuse, modularity et.c. But these virtues are by no means unique to OOP.
You might have a look at Erlang, written by Joe Armstrong.
Wikipedia:
"Erlang is a general-purpose
concurrent programming language and
runtime system. The sequential subset
of Erlang is a functional language,
with strict evaluation, single
assignment, and dynamic typing."
Joe Armstrong:
“Because the problem with
object-oriented languages is they’ve
got all this implicit environment that
they carry around with them. You
wanted a banana but what you got was a
gorilla holding the banana and the
entire jungle.”
The promise of OOP was code reuse and easier maintenance. I am not sure it delivered. I see things such as dot net as being much the same as the C libraries we used to get fro various vendors. You can call that code reuse if you want. As for maintenance bad code is bad code. OOP did not help.
I'm the biggest fan of OOP, and I practice OOP every day.
It's the most natural way to write code, because it resembles the real life.
Though, I realize that the OOP's virtualization might cause performance issues.
Of course that depends on your design, the language and the platform you chose (systems written in Garbage collection based languages such as Java or C# might perform worse than systems which were written in C++ for example).
I guess in Real-time systems, procedural programming may be more appropriate.
Note that not all projects that claim to be OOP are in fact OOP. Sometimes the majority of the code is procedural, or the data model is anemic, and so on...
Zyx, you wrote, "Most of the systems use relational databases ..."
I'm afraid there's no such thing. The relational model will be 40 years old next year and has still never been implemented. I think you mean, "SQL databases." You should read anything by Fabian Pascal to understand the difference between a relational dbms and an SQL dbms.
" ... the relational model is usually chosen due to its popularity,"
True, it's popular.
" ... availability of tools,"
Alas without the main tool necessary: an implementation of the relational model.
" support,"
Yup, the relational model has fine support, I'm sure, but it's entirely unsupported by a dbms implementation.
" and the fact that the relational model is in fact a mathematical concept,"
Yes, it's a mathematical concept, but, not being implemented, it's largely restricted to the ivory towers. String theory is also a mathematical concept but I wouldn't implement a system with it.
In fact, despite it's being a methematical concept, it is certainly not a science (as in computer science) because it lacks the first requirement of any science: that it is falsifiable: there's no implementation of a relational dbms against which we can check its claims.
It's pure snake oil.
" ... contrary to OOP."
And contrary to OOP, the relational model has never been implemented.
Buy a book on SQL and get productive.
Leave the relational model to unproductive theorists.
See this and this. Apparently you can use C# with five different programming paradigms, C++ with three, etc.
Software construction is not akin to Fundamental Physics. Physics strive to describe reality using paradigms which may be challenged by new experimental data and/or theories. Physics is a science which searches for a "truth", in a way that Software construction doesn't.
Software construction is a business. You need to be productive, i.e. to achieve some goals for which someone will pay money. Paradigms are used because they are useful to produce software effectively. You don't need everyone to agree. If I do OOP and it's working well for me, I don't care if a "new" paradigm would potentially be 20% more useful to me if I had the time and money to learn it and later rethink the whole software structure I'm working in and redesign it from scratch.
Also, you may be using another paradigm and I'll still be happy, in the same way that I can make money running a Japanese food restaurant and you can make money with a Mexican food restaurant next door. I don't need to discuss with you whether Japanese food is better than Mexican food.
I doubt OOP is going away any time soon, it just fits our problems and mental models far too well.
What we're starting to see though is multi-paradigm approaches, with declarative and functional ideas being incorporated into object oriented designs. Most of the newer JVM languages are a good example of this (JavaFX, Scala, Clojure, etc.) as well as LINQ and F# on the .net platform.
It's important to note that I'm not talking about replacing OO here, but about complementing it.
JavaFX has shown that a declarative
solution goes beyond SQL and XSLT,
and can also be used for binding
properties and events between visual
components in a GUI
For fault tolerant and highly
concurrent systems, functional
programming is a very good fit,
as demonstrated by the Ericsson
AXD301 (programmed using Erlang)
So... as concurrency becomes more important and FP becomes more popular, I imagine that languages not supporting this paradigm will suffer. This includes many that are currently popular such as C++, Java and Ruby, though JavaScript should cope very nicely.
Using OOP makes the code easier to manage (as in modify/update/add new features) and understand. This is especially true with bigger projects. Because modules/objects encapsulate their data and operations on that data it is easier to comprehend the functionality and the big picture.
The benefit of OOP is that it is easier to discuss (with other developers/management/customer) a LogManager or OrderManager, each of which encompass specific functionality, then describing 'a group of methods that dump the data in file' and 'the methods that keep track of order details'.
So I guess OOP is helpful especially with big projects but there are always new concepts turning up so keep on lookout for new stuff in the future, evaluate and keep what is useful.
People like to think of various things as "objects" and classify them, so no doubt that OOP is so popular. However, there are some areas where OOP has not gained a bigger popularity. Most of the systems use relational databases rather than objective. Even if the second ones hold some notable records and are better for some types of tasks, the relational model is unsually chosen due to its popularity, availability of tools, support and the fact that the relational model is in fact a mathematical concept, contrary to OOP.
Another area where I have never seen OOP is the software building process. All the configuration and make scripts are procedural, partially because of the lack of the support for OOP in shell languages, partially because OOP is too complex for such tasks.
Slightly controversial opinion from me but I don't find OOP, at least of a kind that is popularly applied now, to be that helpful in producing the largest scale software in my particular domain (VFX, which is somewhat similar in scene organization and application state as games). I find it very useful on a medium to smaller scale. I have to be a bit careful here since I've invited some mobs in the past, but I should qualify that this is in my narrow experience in my particular type of domain.
The difficulty I've often found is that if you have all these small concrete objects encapsulating data, they now want to all talk to each other. The interactions between them can get extremely complex, like so (except much, much more complex in a real application spanning thousands of objects):
And this is not a dependency graph directly related to coupling so much as an "interaction graph". There could be abstractions to decouple these concrete objects from each other. Foo might not talk to Bar directly. It might instead talk to it through IBar or something of this sort. This graph would still connect Foo to Bar since, albeit being decoupled, they still talk to each other.
And all this communication between small and medium-sized objects which make up their own little ecosystem, if applied to the entire scale of a large codebase in my domain, can become extremely difficult to maintain. And it becomes so difficult to maintain because it's hard to reason about what happens with all these interactions between objects with respect to things like side effects.
Instead what I've found useful is to organize the overall codebase into completely independent, hefty subsystems that access a central "database". Each subsystem then inputs and outputs data. Some other subsystems might access the same data, but without any one system directly talking to each other.
... or this:
... and each individual system no longer attempts to encapsulate state. It doesn't try to become its own ecosystem. It instead reads and writes data in the central database.
Of course in the implementation of each subsystem, they might use a number of objects to help implement them. And that's where I find OOP very useful is in the implementation of these subsystems. But each of these subsystems constitutes a relatively medium to small-scale project, not too large, and it's at that medium to smaller scale that I find OOP very useful.
"Assembly-Line Programming" With Minimum Knowledge
This allows each subsystem to just focus on doing its thing with almost no knowledge of what's going on in the outside world. A developer focusing on physics can just sit down with the physics subsystem and know little about how the software works except that there's a central database from which he can retrieve things like motion components (just data) and transform them by applying physics to that data. And that makes his job very simple and makes it so he can do what he does best with the minimum knowledge of how everything else works. Input central data and output central data: that's all each subsystem has to do correctly for everything else to work. It's the closest thing I've found in my field to "assembly line programming" where each developer can do his thing with minimum knowledge about how the overall system works.
Testing is still also quite simple because of the narrow focus of each subsystem. We're no longer mocking concrete objects with dependency injection so much as generating a minimum amount of data relevant to a particular system and testing whether the particular system provides the correct output for a given input. With so few systems to test (just dozens can make up a complex software), it also reduces the number of tests required substantially.
Breaking Encapsulation
The system then turns into a rather flat pipeline transforming central application state through independent subsystems that are practically oblivious to each other's existence. One might sometimes push a central event to the database which another system processes, but that other system is still oblivious about where that event came from. I've found this is the key to tackling complexity at least in my domain, and it is effectively through an entity-component system.
Yet it resembles something closer to procedural or functional programming at the broad scale to decouple all these subsystems and let them work with minimal knowledge of the outside world since we're breaking encapsulation in order to achieve this and avoid requiring the systems to talk to each other. When you zoom in, then you might find your share of objects being used to implement any one of these subsystems, but at the broadest scale, the systems resembles something other than OOP.
Global Data
I have to admit that I was very hesitant about applying ECS at first to an architectural design in my domain since, first, it hadn't been done before to my knowledge in popular commercial competitors (3DS Max, SoftImage, etc), and second, it looks like a whole bunch of globally-accessible data.
I've found, however, that this is not a big problem. We can still very effectively maintain invariants, perhaps even better than before. The reason is due to the way the ECS organizes everything into systems and components. You can rest assured that an audio system won't try to mutate a motion component, e.g., not even under the hackiest of situations. Even with a poorly-coordinated team, it's very improbable that the ECS will degrade into something where you can no longer reason about which systems access which component, since it's rather obvious on paper and there are virtually no reasons whatsoever for a certain system to access an inappropriate component.
To the contrary it often removed many of the former temptations for hacky things with the data wide open since a lot of the hacky things done in our former codebase under loose coordination and crunch time was done in hasty attempts to x-ray abstractions and try to access the internals of the ecosystems of objects. The abstractions started to become leaky as a result of people, in a hurry, trying to just get and do things with the data they wanted to access. They were basically jumping through hoops trying to just access data which lead to interface designs degrading quickly.
There is something vaguely resembling encapsulation still just due to the way the system is organized since there's often only one system modifying a particular type of components (two in some exceptional cases). But they don't own that data, they don't provide functions to retrieve that data. The systems don't talk to each other. They all operate through the central ECS database (which is the only dependency that has to be injected into all these systems).
Flexibility and Extensibility
This is already widely-discussed in external resources about entity-component systems but they are extremely flexible at adapting to radically new design ideas
in hindsight, even concept-breaking ones like a suggestion for a creature which is a mammal, insect, and plant that sprouts leaves under sunlight all at once.
One of the reasons is because there are no central abstractions to break. You introduce some new components if you need more data for this or just create an entity which strings together the components required for a plant, mammal, and insect. The systems designed to process insect, mammal, and plant components then automatically pick it up and you might get the behavior you want without changing anything besides adding a line of code to instantiate an entity with a new combo of components. When you need whole new functionality, you just add a new system or modify an existing one.
What I haven't found discussed so much elsewhere is how much this eases maintenance even in scenarios when there are no concept-breaking design changes that we failed to anticipate. Even ignoring the flexibility of the ECS, it can really simplify things when your codebase reaches a certain scale.
Turning Objects Into Data
In a previous OOP-heavy codebase where I saw the difficulty of maintaining a codebase closer to the first graph above, the amount of code required exploded because the analogical Car in this diagram:
... had to be built as a completely separate subtype (class) implementing multiple interfaces. So we had an explosive number of objects in the system: a separate object for point lights from directional lights, a separate object for a fish eye camera from another, etc. We had thousands of objects implementing a few dozen abstract interfaces in endless combinations.
When I compared it to ECS, that required only hundreds and we were able to do the exact same things before using a small fraction of the code, because that turned the analogical Car entity into something that no longer requires its class. It turns into a simple collection of component data as a generalized instance of just one Entity type.
OOP Alternatives
So there are cases like this where OOP applied in excess at the broadest level of the design can start to really degrade maintainability. At the broadest birds-eye view of your system, it can help to flatten it and not try to model it so "deep" with objects interacting with objects interacting with objects, however abstractly.
Comparing the two systems I worked on in the past and now, the new one has more features but takes hundreds of thousands of LOC. The former required over 20 million LOC. Of course it's not the fairest comparison since the former one had a huge legacy, but if you take a slice of the two systems which are functionally quite equal without the legacy baggage (at least about as close to equal as we might get), the ECS takes a small fraction of the code to do the same thing, and partly because it dramatically reduces the number of classes there are in the system by turning them into collections (entities) of raw data (components) with hefty systems to process them instead of a boatload of small/medium objects.
Are there any scenarios where a truly non-OOP paradigm is actually a
better choice for a largescale solution? Or is that unheard of these
days?
It's far from unheard of. The system I'm describing above, for example, is widely used in games. It's quite rare in my field (most of the architectures in my field are COM-like with pure interfaces, and that's the type of architecture I worked on in the past), but I've found that peering over at what gamers are doing when designing an architecture made a world of difference in being able to create something that still remains very comprehensible at it grows and grows.
That said, some people consider ECS to be a type of object-oriented programming on its own. If so, it doesn't resemble OOP of a kind most of us would think of, since data (components and entities to compose them) and functionality (systems) are separated. It requires abandoning encapsulation at the broad system level which is often considered one of the most fundamental aspects of OOP.
High-Level Coding
But it seems to me that usually the pieces of higher level solutions
are almost always put together in a OOP fashion.
If you can piece together an application with very high-level code, then it tends to be rather small or medium in scale as far as the code your team has to maintain and can probably be assembled very effectively using OOP.
In my field in VFX, we often have to do things that are relatively low-level like raytracing, image processing, mesh processing, fluid dynamics, etc, and can't just piece these together from third party products since we're actually competing more in terms of what we can do at the low-level (users get more excited about cutting-edge, competitive production rendering improvements than, say, a nicer GUI). So there can be lots and lots of code ranging from very low-level shuffling of bits and bytes to very high-level code that scripters write through embedded scripting languages.
Interweb of Communication
But there comes a point with a large enough scale with any type of application, high-level or low-level or a combo, that revolves around a very complex central application state where I've found it no longer useful to try to encapsulate everything into objects. Doing so tends to multiply complexity and the difficulty to reason about what goes on due to the multiplied amount of interaction that goes on between everything. It no longer becomes so easy to reason about thousands of ecosystems talking to each other if there isn't a breaking point at a large enough scale where we stop modeling each thing as encapsulated ecosystems that have to talk to each other. Even if each one is individually simple, everything taken in as a whole can start to more than overwhelm the mind, and we often have to take a whole lot of that in to make changes and add new features and debug things and so forth if you try to revolve the design of an entire large-scale system solely around OOP principles. It can help to break free of encapsulation at some scale for at least some domains.
At that point it's not necessarily so useful anymore to, say, have a physics system encapsulate its own data (otherwise many things could want to talk to it and retrieve that data as well as initialize it with the appropriate input data), and that's where I found this alternative through ECS so helpful, since it turns the analogical physics system, and all such hefty systems, into a "central database transformer" or a "central database reader which outputs something new" which can now be oblivious about each other. Each system then starts to resemble more like a process in a flat pipeline than an object which forms a node in a very complex graph of communication.
I've been studying OOP for quite a while now and I have a good grasp of the theory. I read the Head First book on OOP and, while it reinforced a lot of the theory, I found the case studies to be somewhat trivial.
I find that I'm applying OOP principles to my code each day, but I'm not sure if I'm applying them correctly. I need to get to the point where I am able to look at my code and know whether I'm using inheritance appropriately, whether my object is cohesive enough, etc.
Does anyone have any good recommendations (books, online guides, blogs, walk-throughs, etc.) for taking the next step in developing solid OOP skills?
I am working primarily in .NET (visual basic), but I welcome suggestions that incorporate various platforms.
Read Refactoring by Martin Fowler, and apply it to your own work.
It will take you through a litany of malodorous characteristics of software code that describe how to detect improperly constructed classes, and even more importantly, how to fix them.
Consider looking into Design Patterns. Although it seems like they aren't commonly used in enterprise applications (I've seen them more commonly used in API's and Frameworks than embedded into enterprise code), they could be applied to make software simpler or more robust in a lot of situations if only developers knew how to apply them.
The key is to understand the design patterns first, then with experience you'll learn how to apply them.
There is a Head First book on design patterns that teaches the concept pretty simply, although if you want a book that really covers design patterns in detail, check out the Gang of Four design patterns book, which is basically what made design patterns mainstream and is referred to almost every time the topic is brought up.
Design patterns can be applied in pretty much any object-oriented language to some degree or another, although some patterns can be overkill or over engineering in some cases.
EDIT:
I also want to add, you should check out the book Code Complete 2. It's a very influential book in the world of software development. It covers a lot of different concepts and theories. I learn something new every time I read it. It's such a good book that if I read it every 6 months to a year, I look at it from a different perspective that makes me a better programmer just by re-reading it. No matter how much you might think you know, this book will make you realize just how little you really know. It's really a great book. I can't stress how much you should own this book.
If you already have the basics, I believe only experience will get you further. You say you are not sure if you are applying the principles correctly, but there is no one correct way. Code you write today, you'll look at in 6 months time, and wonder why you wrote it that way, and probably know of a better, cleaner way of doing it. I also guarantee that after 10 years, you'll still be learning new techniques and tricks. Don't worry too much about it, it will come, just read as much as you can, and try and apply what you read in small chunks.
I am currently half-way through the following book:
http://www.amazon.com/Applying-UML-Patterns-Introduction-Object-Oriented/dp/0131489062
I cannot recommend this book strongly enough in terms of learning a real-life, professional-grade, practical approach to drafting and applying a well-formed and iterative design strategy before diving into code.
I, too, read the "Head First" book and felt that I was much better off for having read it.
After having a few years of working-world experience, I now view the Craig Larman book that I am recommending to be a perfect "next step" for me.
About the Presence of "UML" in this Book Title:
Whether you have positive feelings or negative feelings about UML notation, please do not let that influence your decision to buy the book (ISBN 0131489062) in either direction.
The prominence of "UML" in the title is misleading. While the author does use and explain UML notation, these explanations are extremely well-woven into relevant design discussions, and at no time does this book read like a boring UML spec.
In fact, here is a quote taken directly from the book:
What's important is knowing how to think and design in objects, which is a very different and much more valuable skill than knowing UML notation. While drawing a diagram, we need to answer key questions: What are the responsibilities of the object? Who does it collaborate with? What design patterns should be applied? Far more important than knowing the difference between UML 1.4 and 2.0 !
This book at times seems like it is "speaking to" a lead architect or a project manager. What I mean to say by that is that it assumes that the reader has significant control over the planning and direction of a software project.
Nonetheless, even if you are only responsible for some very small piece of your company's projects and products, I would still recommend this book and encourage you to apply some "scaled down" modifications of the book's advice to your piece of the project.
My OOP epiphany came from Grady Booch's book, way long time ago. Suddenly I realized why objects were good.
While polymorphism is cool, encapsulation is 75% of why objects are cool. It is sort of like an interface: you see the buttons but not the wiring. Before objects, only the most disciplined coders kept their grubby fingers off the internal bits of other people's procedures (it was called "structured programming").
Object make it easy to Do the Right Thing. Inheritance and polymorphism are little bonuses.
One way to learn about objects is to read other peoples' code. I learned a lot by reading the source code for the Delphi VCL framework. Even just looking at the documentation for Java will help you see what a single object class should do and how it is designed to be used by other objects.
Start a project of your own and pay attention when you want to sub-class your own classes and find that you have to go back and break up some protected methods so you can override just one piece of a process instead of replacing all of it. See how ancestors talk to descendants by calling abstract functions. In other words, go make a lot of mistakes and learn from them.
Enjoy!
Frankly, re-reading old David Parnas papers on information hiding helps me get in the right state of mind. The case studies may not be directly applicable but you should be able to get some useful generalizations out of them.
My epiphany happened when I tried to implement a very OO problem (dynamically and recursively building SQL statements) in VB6. The best way to understand polymorphism or inheritance is to need it and not be able to use it.
One thing that will definitely help you is working on a well-known, respected open source project. Either dig through the source code and see how things are done or try to make some additions / modifications. You'll find that there isn't one style or one right answer for most problems, but by looking at several projects, you'll be able to get a wide view of how things can be done. From there, you'll begin to develop your own style and will hopefully make some contributions to open source in the process.
I think you have to attempt and fail at implementing OO solutions. That's how I did it anyway. What I mean by fail is that you end up writing smelly code while successfully delivering a working solution. After it's written you'll get a feel for where things didn't quite feel right. You may have some epiphanies, and/or you may go and hunt for a slicker solution from other programmers. Undoubtedly you'll implement some variation of standard design patterns by accident. In hindsight, a light will click on (oh! so that's what a visitor is for), and then understanding will accelerate.
As others have said, I think tooling through some good OO open source code is a good idea. So is working with more experienced programmers who would be willing to critique your work. However understanding comes through doing.
You might want to try to read (and write) some Smalltalk for a while. Squeak is a free implementation that can show you the power of a fully object-oriented environment (unlike java or .net). All library code source is included. The language itself is incredibly simple. You'll find that java and c# are slowly adding the features well-known to Smalltalk since 1980.
Tortoise HG is extrodanarily well designed piece of OO open source software (written in Python).
If you already understand the basics, building something from scratch in a fully object oriented language will be a good step in fully understanding OOP software architecture. If you don't know Python, Python Essential Reference will take you through the language in full in a few days to a week.
After you understand the language take a look through the software above and you'll have all sorts of epiphanies.
To understand basically anything thoroughly, you need to have a decent knowledge of at least one abstraction level above and one level below it. In the case of OO, others have mentioned design patterns as the layer above OO. This helps a lot to illustrate why OO is useful.
As far as the layer below OO, try to play around with higher-order functions/late binding for a while and get a feel for how these relatively simple constructs are used. Also, try to understand how OO is implemented under the hood (vtables, etc.) and how it can be done in pure C. Once you grok the value of using higher order functions and late binding, you'll quickly realize that OO is just a convenient syntax for passing around a set of related functions and the data they operate on.