In the database world, we have normalisation. You can start with a design, crank the steps and end up with a normal form of the database. This is done on the basis of the semantics of the data and can be thought of as a series of design refactorings.
In object orientated design, we have the SOLID principals and various other adhoc guidelines towards good design.
Do you think it is possible to define the equivalent of normal forms for OO, such that a series of refactoring steps could move a procedural piece of code (or poorly factored OO design) into a correct (in some well-defined sense) formulation with the same functionality?
(NB. Happy to make this community wiki)
It is a possibility, but highly unlikely.
Context
First, in the days when the Relational Model came out, people who worked in IT were more educated and they esteemed standards. Computer resources were expensive, and people were always looking for the best way to use those resources. People like Codd and Date were giants in an industry where people were high tech.
Codd did not invent Normalisation, we were Normalising our non-relational databases long before Relational came along. Normalisation is a theory and practice, published as the Principle of Full Normalisation. We were Normalising our programs, we considered accidental duplication of a subrotine (method) a serious error. Nowadays it is known as Never Duplicate Anything or Don't Repeat Yourself, but the recent versions do not acknowledge the sound academic theory behind, and are therefore its power is unreallised.
What Codd did (among many things) was define formal Normal Forms specifically for Relational Databases. And these have progressed and been refined since then. But they have also been hijacked by non-academics for the purpose of selling their gear.
The database modelling that was invented by Codd and Chen, and finished by Brown had a solid grounding. In the last 25 years, its has achieved Standardisation and been further refined and progressed by many others who had solid grounding.
The World Before OO
Let's take the programming world before OO. We had many standards and conventions, for modelling our programs, as well as for language- and platform-specific implementation. Your question simply would not apply in those days. The entire industry understood deeply that database design and program design were two different sciences, and used different modelling methodologies for them, plus whatever standards applied. People did not discuss if they implemented standards, they discussed the extent to which they complied with standards; they did not discuss if they modelled their data and programs, they discussed the extent to which they modelled their data and programs. That is how we put men on the Moon, notably in 1969.
Dawn of OO
OO came along and presented itself as if no other programming language or design methodology existed before it. Instead of using existing methodologies and extending or changing them, it denied their existence. So, not surprisingly, it has taken 20 years to formulate the new methodologies from scratch and slowly progress them to the point of SOLID and Agile, which is not mature; the reason for your question. It is telling that more than twenty such methodologies have flashed up and died during that time.
Even UML, which could have been an outright winner, applicable to any programming language suffered the same disease. It tried to be everything to everyone while denying that mature methodologies existed.
Demise of the Industry
With the advent of MS, the attitude of "anyone can do anything" (implication: you do not need formal education or qualifications), that quality and pride of profession has been lost. People now invent things from scratch as if no one on the planet has ever done it before. The IT industry today is very low tech. You kow, but most people reading these pages do not know, that there is one Relational Modelling methodology, and one Standard. They do not model, the implement. Then re-implement. And re-implement. Re-factoring as you say.
OO Proponents
The problem was that the people who came up with these OO methods were not giants among professionals; they were simply the most vocal of an un-academic lot. Famous due to publishing books, not due to peer acknowledgement. Unskilled and unaware. They had One Hammer in their toolkit, and every problem looked like a nail. Since they were not formally educated they did not know that actually database design and program design are two different sciences; that database design was quite mature, had strongly established methodologies and standards, and they simply applied their shiny new hammer to every problem, including databases.
Therefore, since they were ignoring both programming methodologies and database methodologies, inventing the wheel from scratch, those new methodologies have progressed very slowly. And with assistance from a similar crowd, without sound academic basis.
Programs today have hundreds of methods that are not used. We now have programs to detect that. Whereas with the mature methodologies, we prevent that. Thin client was not a goal to be achieved, we had a science that produced it. We now have programs to detect "dirty" data and to "clean" it. Whereas in the upper end of the database market, we simply do not allow "dirty" data into the database in the first place.
I accept that you see database design as a series of re-factorings, I understand what you mean. To me it is a science (methodology, standards) that eliminates ever having to re-factor. Even the acceptance of re-factoring is loud signal that the older programming methodologies are unknown; that the current OO methodologies are immature. The danger, what makes it annoying to work with OO people, is that the methodology itself fosters a confidence in the One Hammer mentality, and when the code breaks, they have not one leg to stand on; when the system breaks, the whole system breaks, it is not one small piece that can be repaired or replaced.
Take Scott Ambler and Agile. Ambler spend 20 years publicly and vociferously arguing with the giants of the database industry, against Normalisation. Now he has Agile, which although immature, has promise. But the secret behind it it Normalisation. He has switched tracks. And because of his past wars, he cannot come out and declare that honestly, and give others due credit, so it remains a secret, and you are left to figure out Agile without its fundaments being declared.
Prognosis
That is why I say, given the evidenced small progress in the OO world over the last 20 years; the 20 or so OO methodologies that have failed; the shallowness of the approach, it is highly unlikely that the current OO methodologies will achieve the maturity and acceptance of the (singular) database design methodology. It will take at least another 10 years, more likely 20, and it will be over taken by some replacement for OO.
For it to be a possibility two things need to happen:
The OO proponents need formal tertiary education. A good grounding in the science of programming. Sure, anyone can do anything, but to do great things, we need a great grounding. That will lead to the understanding that re-factoring is not necessary, that it can be eliminated by science.
They need to break their denial of other programming methodologies and standards. That will open the door to either building OO on top of that, or taking the fundaments of that and merging it into OO. That will lead to a solid and complete OO methodology.
Real World OO
Obviously I speak from experience. On our large projects we use the mature analysis and design methodologies, one for database and another for function. When we get to the code-cutting stage, we let the OO team use whatever they like, for their objects only, which usually means UML. No problems with architecture or structure or performance or bloatware or One Hammer or hundreds of unused objects, because all that was taken care of outside OO. And later, during UAT, no problems with finding the source of bugs or making the required changes quickly, because the entire structure, has documented structure; the blocks can be changed.
I think this is an interesting question, because it presumes that Codd's Normal Forms are actually the definition of "correct" design. Not trying to start a flame war with that statement, but I guess my point is that there are very good reasons that many DB's aren't fully normalized (e.g. join performance) leads me to think that the real-world equivalent of normalization in OO space is probably design patterns or (as you said) SOLID. In both cases you're talking about idealized guidelines that have to be applied with a suitably critical eye, rather than slavishly followed as dogma.
Not only do I fully agree with Paul, but I will go a step further.
Models are just that - only models. The Normalization models used by Relational Databases are only one approach to storing and managing data. In fact, note that while RDBMS's are common for Data Manipulation operations (the standard CRUD), we have now evolved the DataWarehouse for consolidation, analysis, and reporting. And it most definitely does NOT adhere to the normalization models found in DML land.
Now we also have Google with their BigTable architecture, and Apache with Hadoop. These newer modeling systems reflect a change in the landscape, driven by the idea of the DISTRIBUTED database. Normalization need not apply for this club either.
We can apply a successful model ony to the point at which it becomes not-so-successful, or is supplanted by an model which better suits the needs of the designer. Note the many ways we humans have modelled our universe through physics/Astronomy what have you. Modelling attmpts to describe a system in discreet terms, but as the system, or the needs of the system change, so must the model.
OOP is and has been a very, very successfulk way to model computer applications. However, the needs of the application designer are different from thos eof Database designers. MOST of the time, there is a point at which the designer of an application must consider that his program will be interacted with by humans. Unlike the database designer, whose work will (mostly) be expected to interact with other code, the programmer's job is to take the machine and make it accessible to a much more random human-being. This art does not map quite so well to such standards like normalization.
All that said, n-tier, MVC, MVVC, and other paradims DO establish some guidelines. But in the end, the problem-space of application design is usually not as easy to fit into such discrete modelling steps as a relational databse.
Wow. Apologies for the length. If this is a breach of ettiquette here, do let me know . . .
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Are design patterns really language weaknesses?
After spending years pouring over books on OOP and the techniques of OOP, and recently getting involved more and more in Functional Styles of programming, would it be fair to extrapolate that design patterns are pointers to systemic problems with Object Oriented programming as a whole. Is there a fundamental flaw in Object Oriented Programming (not to be confused with Design), in that in the treatment of state through encapsulation, has led to more and more patterns to resolve the problems with such a paradigm.
I have not come to any conclusions on this, but my "gut" feeling is that there might be something more seriously wrong with the paradigm of OOP.
Is the very idea of encapsulation causing more problems than they solve.
A very good question and something that I have thought about some time ago. This is my conclusion \ opinion:
The idea of object oriented programming is not without flaws, but does provide the most complete design paradigm. If the problem domain is expressed properly, clearly defined object, who knows their responsibilities, can interact in a fairly elegant way, that closely resembles the real world interaction of the objects. (or ideas).
To make some of the more abstract concepts, specific, OOP makes some assertive statements. (Like encapsulation, not expose more than you have to and object responsibility).
Like all generic assumptions, there would be exceptions, when what normally would be a good idea, may not fit a particular problem in hand. It is also not helped by the fact that OOP, covers almost all problem conceived ( unlike AOP or even the more complex semantic modeling, that caters to a specific kind of problem).
So in situations, when you need to make exceptions and move away from OOP assertions, the designers needed a way to keep in bounds of good design, so that they do not stray far too much from accepted design practices.
So design patterns, for me is just case studies of problems, that will not be served by some of the core assertion of OOP. Apart from collaboration and collation of solution, design pattern also helps augment OOP. (especially for newbie designers).
Note: Most of the time, design patterns are not needed. There needs to be, clear justification for using patterns. I know, some greenhorns, trying to implement some design pattern, just because they know them ( and sometime not so greenhorns ;)). Its square peg, round hole problem
Good question, I started wondering about this my self a few weeks ago whilst getting more into Python and Scala.
I think yes and no. There are definitely some intrinsic problems with OOP and the encapsulation of state, but it's not to say that OOP itself is inherently a bad way of doing things. I think the problem is that when all you have is a hammer everything becomes a nail. OOP is great for some things, GUIs come to mind first but functional programming has very clear benefits as well.
It's worth noting that the newer functional programming languages like Scala haven't thrown objects away.
I haven't thought about the issue in great detail but I certainly agree that OOP has some issues that I haven't seen addressed, other than in the form of design patters, which really are addressing the symptoms rather than the disease.
No. Although you see slightly different design patterns, you certainly still see design patterns in functional code as well. The basic difference has little (if anything) to do with lack of state. Rather, it stems primarily from (most) functional languages providing enough more versatility in creating functions that what would be a "design pattern" in another language simply becomes a function in a functional language.
If you provide a (roughly) similar level of versatility in a language that has state, you can get the same effect. Just for example, most of the introduction to Modern C++ Design is defending the position that a design pattern can be encoded as a template (and most of the book is design patterns implemented as templates).
I think there will invariably be problems when you try to apply a single programming paradigm to a problem. That's why I like C++: it's multi-paradigm; it doesn't force you into a single set of solutions.
I am repeating a basic theory of mine, but models are just that - only models. The model defined by OOP is a very effective way to structure a program, and for many application programming domains, is entirely appropriate. For some problem spaces, the model may become decreasingly effective (or less efficient, or both).
A potential metaphore exists with physics. For many, many years Newtonian physics did (and in fact, still does) a remarkable job of modelling the laws of motion, time, and space (with some help from euclidian and sperical geometry). But when science began probing into the micro-and macro aspects of the problem space, Newtonian physics (AND euclidian/Spherical geometry) begin to break down. Hence we now have Relativity and quantumn mechanics. These do a fantastic job of modelling the universe at the macro and micro levels respectively, but are overly convoluted for use as descriptors of every-day, human-scale events.
OOP is very effective for application programming in a lot of cases, when considered in the context of the complexity involved with modelling real-world problems and human interactions for consumption and processing by a linear machine. As someone above observed, there are no silver bullets. And my impression (having never used C++) is that languages which attempt to be multi-paradigm also become more complex, and not necessarily as efficient for smaller problems more easily handled with a higher-level, more targeted language. Much like Quantumn mechanics and/or relativity theories (I mean, really, is anyone interested in the relationship between mass and velocity when travelling at 60 MPH on the freeway? OR the probability of Los angeles being where you expect it to be when you arrive?).
In my impression, adherence to qa specific model is important, so long as the model is suited to the problem space. At the point when this stops being true, the model may need to evolve, and there will be resistence to this. There will be attempts to force the problem space into a model not suited (review the history of physics again, or check into the evolution of the helio-centric model of the solar system, and include the key word "epicycles").
All of the above is simply MY best understanding of the state of things, and if I have missed the mark somewhere, I am happy to hear some contrary news.
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Don't get me wrong - OOP currently is the best thing to structure large code bases.
But why do people try to stuff anything into an OO view?
For example: each text book about OOP contains an "introducing example" that tries to express a small view of our real world in an OO inheritance and composition and aggregation construct. And - meanwhile - we all know that it never results in the almighty OO construct that the OO model itself promised! The authors just created an illusion.
My personal opinion is, that OO is nice to structure code, but it is not suited to represent real world data and its relations. IMHO the relational model is superior, probably any other model is superior.
In OO design it became practice to recommend composition over inheritance - whenever possible. So that mighty looking model of an all-inheritance-based-world-of-objects thing that first class books suggest is just an illusion. So, OO itself may be an illusion? And current composition-centric OO models are nothing more than plain data structures with some standardized syntactic sugar - that's not much different than in pre-OOP approaches.
Another example: imagine a really f***ing complex model of our real-world. Besides anything else, there are stone blocks and humans. In an OO model, humans are mammals are animals are organic lifeforms and so on (the strictly rigid inheritance hierarchy OO imposes, you know..). The stone blocks are non-organic things, maybe they are rigid bodies or whatever, it doesn't matter.
If you are an artist and you have to find a stone block that makes a good "template" (?) for a statue of a human with given width, height an thickness, then you have to write a bunch of special case OO code to retrieve these attributes from the human model and from the stone block model. Or alternatively, your whole world model was build to support geometric queries - then it would be easy! But that leads to the conclusion that OOP sucks at representing data in a way that allows us to use it in different use cases. OOP just allows us to represent data for exactly those use cases that we have designed beforehand. Not much more. Any use besides those predeterminated cases can only be done with much fiddling. The relational model at least tries to represent data in a re-useable way. (re-useable: OOP once even occupied that word)
Why all that hate?
I work on a project that uses an ORM - and it just sucks. It started when modeling the database (because of ORM limitations), then came the time to learn the ins and outs of the ORM (and its bugs and further limitations), then came the fear of implicitly happening stuff (new thing(); thing->save() creates a new row, but where is "thing" rooted? why do people try to make objects as "independent" as possible but in the back create much more deeply rooted dependencies on freaking per-table-singletons, that communicate with connection-singletons.. oh my god .. I digress).
So many things that could have been done in a few lines of SQL and a sweet tiny query API were done in hundreds or maybe thousands of lines of "business logic" code (of course in the application layer, not in the database where the data is, and where aggregation functions like count() or sum() would be cheap). I think the people just feel better when they can work in OOP. But that is just stupid.
And the creators of ORM just want to keep the users away from the "dirty stuff". But exactly those people should not write ORM - the perfect example: I strongly believe that the ORM-creator type of people do not even know that a database table can contain compound primary keys! ;-)
So, why is OOP so occupying? It is just a half-baked abstraction, but people swear on it for everything, if you ask some, they may even tell you that OOP will create world peace.
Why is OOP so f***ing occupying/monopolizing?
It seems to me that if your business logic implementation is driving the design of your database, then somebody put the cart before the horse. I thought the idea was to develop a rational (no, I didn't say relational) data model and then implement whatever logic queries and updates the data.
My experience has been that although relational databases are great for storing and querying data from the user's perspective, trying to extend the relational model into a structured programming or OOP paradigm is difficult in the extreme. There's always a translation layer. Today everybody thinks that ORM is the solution. And although technically any translation layer that sits between a relational data store and an object oriented data access layer is ORM, when I see people talking about ORM these days they seem to be talking about some automated way of generating that ORM layer.
I'm not convinced that a generalized ORM solution exists. Every one I've seen is fraught with peril. It's a pain in the neck, but the only reliable ORM layers I've ever seen were hand coded. Granted, I haven't worked much with database stuff in the last five years or so, so things may have changed.
I'll agree with you that OOP is largely just a lot of syntactic sugar around a solid structured design. However, it's good syntactic sugar. It formalizes a lot of things that were considered "best practices" in structured programming, and adds some things (inheritance, interfaces, polymorphism, among others) that were very difficult or impossible to express in purely structured languages. We certainly could have added some or all of those features to structured languages without going all the way to OOP, but why? OOP was the obvious next step in the evolution of procedural programming languages.
OOP is just another tool in the toolbox, but keep in mind that OO has been the focus of mainstream programming languages for over 20 years now - starting with C++ and moving on to Java and C#. This probably has more to do with why the model is currently "so occupying" than anything else.
The point of OOP isn't necessarily to represent every single facet of every single object in the world. The point is to represent the stuff you care about. For instance, assume there's a house. Someone doing real estate stuff would care about the location, selling price, etc. A builder might care about the blueprint ID or something, i dunno. The point is, you model the important stuff and ignore the rest. Add to it later if you find you need more info.
Yes, this makes a "House" class tailored to the app being built, and possibly unsuitable for others. OOP's overall goal isn't to reuse classes, though sometimes that happens. The point is to bundle data with the actions that can affect that data, and to thus reduce a problem conceptually from hundreds of variables and functions to a few objects with known and tested interfaces and related behaviors.
The Human and StoneBlock should both inherit from MaterialObject, which has height width and depth attributes and even implements a biggerThan() method, when given another MaterialObject.
OO is "occupying" because it has shown to be an excellent choice for many programming problems. Similarly, the relational model has shown to be an excellent choice for many data storage and retrieval problems. When I say "many" I mean "so many that all other pale in comparison". In fact, both coupled together are an excellent combo, but there is complexity in mapping where these two paradigms meet, thus ORM.
I almost thought your question was insincere but then decided it was lack of experience (not intended as an insult, just guessing from the questions/assertions). You will find that there are problems so complex that OO is the only feasible way to model them. Not everything is a database-backed web site or reporting tool. Many systems are mostly "business logic" where the best solution is an OO solution (example from my experience: controlling and monitoring robotic aircraft and their various payloads). That being said, many of the popular database-backed web frameworks are OO+RDBMS (Rails, Grails, Java+Spring+Hibernate, etc.) because the combination is so powerful.
While there are certainly fads and sticky but outmoded paradigms, I suggest that when there are many choices (OO, functional programming, RDBMS-centric, etc.) people almost always choose what is the most productive. For at least 10 years, that has been OO for a large portion of software problems.
Because at the end of the day it's a good approximation of the way we ourselves model things. The counter that is often raised to this is that computers have no concept of objects, it's all 1s and 0s, but that analysis is as empty as saying all human thought is nothing more than neurons and electrical impulses (which it probably is, but it's just not a useful way of looking at things).
So you don't like inheritance? Nor do I. Inheritance of behaviour is the poor man's code reuse. Inheritance of interface, on the other hand, is great as it gives us polymorphism.
You don't like ORMs? No one is forcing you to use them. There is a conflict between OOP and RDMS that I don't think is easily fixed and most ORMs attempts to resolve this are quite naive. The limitations of ORM are not a flaw in OOP.
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.
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I asked a question previously about Dataset vs Business Objects
.NET Dataset vs Business Object : Why the debate? Why not combine the two?
and I want to generalize the question here: where is the proof that OOP is really suitable for very complex problems ? Let's take a MMO Game Engine for example. I'm not specialist at all but as I read this article, it clearly stands that OOP is far from being enough:
http://t-machine.org/index.php/2007/11/11/entity-systems-are-the-future-of-mmog-development-part-2/
It concludes:
Programming well with Entity Systems is very close to programming with a Relational Database. It would not be unreasonable to call ES’s a form of “Relation Oriented Programming”.
So isn't OOP trying to get rid off something that is here to stay ?
OOP is non-linear, Relational is linear, both are necessary depending on the part of a system so why try to eliminate Relational just because it isn't "pure" Object. Is OOP an end by itself ?
My question is not is OOP usefull. OOP is usefull, my question is rather why the purists want to do "pure" OOP ?
As the author of the linked post, I thought I'd throw in a couple of thoughts.
FYI: I started seriously (i.e. for commercial work) using OOP / ORM / UML in 1997, and it took me about 5 years of day to day usage to get really good at it IMHO. I'd been programming in ASM and non-OOP languages for about 5 years by that point.
The question may be imperfectly phrased, but I think it's a good question to be asking yourself and investigating - once you understand how to phrase it better, you'll have learnt a lot useful about how this all hangs together.
"So isn't OOP trying to get rid off something that is here to stay ?"
First, read Bjarne's paper here: http://www.stroustrup.com/oopsla.pdf
IMHO, no-one should be taught any OOP without reading that paper (and re-reading after they've "learnt" OOP). So many many people misunderstand what they're dealing with.
IME, many university courses don't teach OOP well; they teach people how to write methods, and classes, and how to use objects. They teach poorly why you would do these things, where the ideas come from, etc. I think much of the mis-usage comes from that: almost a case of the blind leading the blind (they aren't blind in "how" to use OOP, they're just blind in "why" to use OOP).
To quote from the final paragraphs of the paper:
"how you support good programming techniques and good design techniques matters more than labels and buzz words. The fundamental idea is simply to improve design and programming through abstraction. You want to hide details, you want to exploit any commonality in a system, and you want to make this affordable.
I would like to encourage you not to make object-oriented a meaningless term. The notion of ‘‘object-oriented’’ is too frequently debased:
– by equating it with good,
– by equating it with a single language, or
– by accepting everything as object-oriented.
I have argued that there are–and must be–useful techniques beyond object-oriented programming and design. However, to avoid being totally misunderstood, I would like to emphasize that I wouldn’t attempt a serious project using a programming lan-
guage that didn’t at least support the classical notion of object-oriented programming. In addition to facilities that support object-oriented programming, I want –and C++ provides features that go beyond those in their support for direct expression of concepts and relationships."
Now ... I'd ask you ... of all the OOP programmers and OOP projects you've seen, how many of them can honestly claim to have adhered to what Bjarne requests there?
IME, less than the majority.
Bjarne states that:
"The fundamental idea is simply to improve design and programming through abstraction"
...and yet many people invent for themselves a different meaning, something like:
"The fundamental idea is that OOP is good, and everything-not-OOP is inferior"
Programmers who have programmed sequentially with ASM, then later ASM's, then pascal, then C, then C++, and have been exposed to the chaos that was programming pre-encapsulation etc tend to have better understanding of this stuff. They know why OOP came about, what it was trying to solve.
Funnily enough, OOP was not trying to solve every programming problem. Who'd have htought it, to say how it's talked about today?
It was aimed at a small number of problems that were hugely dangerous the bigger your project got, and which it turned out to be somewhere between "good" and "very good" at solving.
But even some of them it isn't any better than merely "good" at solving; there are other paradigms that are better...
All IMHO, of course ;)
Systems of any notable complexity are not linear. Even if you worked really hard to make a system one linear process, you're still relying on things like disks, memory and network connections that can be flaky, so you'll need to work around that.
I don't know that anyone thinks OOP is the final answer. It's just a way of dealing with complexity by trying to keep various problems confined to the smallest possible sphere so the damage they do when they blow up is minimized. My problem with your question is that it assumes perfection is possible. If it were, I could agree OOP isn't necessary. It is for me until someone comes up with a better way for me to minimize the number of mistakes I make.
Just read yr article about Entity Systems, which compares ES to OOP, and it is flagrantly wrong about several aspects of OOP. for e.g., When there are 100 instances of a class, OOP does not mandate that there be 100 copies of the classes methods loaded in memory, only one is necessary. Everything that ES purports to be able to do "better" than OOP because it has "Components", and "Systems", OOP supports as well using interfaces and static classes, (and/or Singletons).
And OOP more naturally fits with the real-world, as any real or imagined Problem Domain, consisting of multiple physical and/or non-physical items and abstractions, and the relationships between them, can be modeled with an appropriately designed hiearchical OOP class structure.
What we try to do is put an OO style on top of a relational system. In C# land this gets us a strongly typed system so that everything from end to end can be compiled and tested. The database has a hard time being tested, refactored, etc. OOP allows us to organize our application into layers and hiearchies which relational doesn't allow.
Well you've got a theoretical question.
Firstly let me agree with you that OOP is not a solve-all solution. It's good for somethings, it's not good for others. But that doesn't mean it doesn't scale up. Some horribly complex and huge systems have been designed using OOP.
I think OOP is so popular because it deserves to be. It solves some problems rather wonderfully, it is easy to think in terms of Objects because we can do that without re-programming ourselves.
So until we can all come up with a better alternatives that actually works in practical life, I think OOP is a pretty good idea and so are relational databases.
There is really no limit to what OOP can deal with - just as there is no real limit to what C can deal with, or assembler for that matter. All are Turing-complete, which is all you really need.
OOP simply gives you a higher-level way of breaking down the program, just as C is a higher-level than assembler.
The article about entity systems does not say that OO cannot do this - in fact, it sounds like they are using OOP to implement their Entities, Components, etc. In any complex domain there will be different ways of breaking it down, and using OOP you can break it down to the object/class level at some point. This does not preclude having higher-level conceptual frameworks which are used to design the OOP system.
The problem isn't the object oriented approach in most situations, the problem is performance and actual development of the underlying hardware.
The OO paradigm approach software development by providing us with a metaphor of the real world, were we have concepts which defines the common accepted and expected properties and behaivour of real objects in the world. Is the way that humans model things and we're able to solve most of the problems with it.
In theory you can define every aspect of a game, system or whatever using OO. In practice if you do, your program will simply behave too slow so the paradigm is messed up by optimizations which trade the simplicity of the model from performance.
In that way, relational databases are not object oriented so we build an object oriented layer between our code and the database... by doing so you lost some of the performance of the database and some of its expressiveness because, from the point of view of OO paradigm a relational database is a full class, is an very complex object that provides information.
From my point of view OO is an almost perfect approach in the theoretical sense of the word, as it maps closely to the way we, humans, think, but it doesn't fit well with the limited resources of the computational development... so we take shortcuts. At the and, performance is far more important than theoretical organization or clearness so this shortcuts become standards or usual practices.
That is, we are adapting the theoretical model to our current limitations. In the times of cobol in the late 70's object oriented was simply impossible... it would imply to many aspects and too little performance so we used a simplified approach, so simplified you didn't have objects or class, you had variables ... but the concept was, in that time, the same. Groups of variables described related concepts, properties that today will feet into an object. Control sequences based on a variable value where used to replace class hierarchies and so on.
I think we've been using OOP for a long time and that we'll continue using it for a long time. As hardware capabilities improve we'll be able to unsimplify the model so that it becomes more adaptable. If I describe perfectly (almost) the concept of a cat (which involves a lot of describing for a lot of concepts involved) that concept will be able to be reused everywhere... the problem here is not, as I've said, with the paradigm itself but with our limitations to implement it.
EDIT: To answer the question about why use pure OO. Every "science" wants to have a complete model to represent things. We have two physic models to describe nature, one at the microscopic level and one for the macroscopic one, and we want to have just one because it simplifies things it provides us with a better way to prove, test and develop things. With OO the same process applies. You can't analytically test and prove a system if the system doesn't follow a precise set of rules. If you are changing between paradigms in a program then your program cannot be properly analized, it has to be disected in each one, analized and then analized again to see that the interactions are correct. It makes a lot more difficult to understand a system because in fact you have two or three system that interact in different ways.
Guys, isn't the question more about ORM than OOP? OOP is a style of programming - the thing that actually gets compared is a Relational Database mapped onto objects.
OOP is actually more than just the ORM! It's also not just the inheritance and polymorphism! It's an extremly wide range of design patterns and above all it's the way we think about programming itself.
Jorge: it's ok that you've pointed out the opitimization part - what you didn't add is that this step should be done last and in 99% cases the slow part is not the OOP.
Now plain and simple: the OOP style with all the principals added to it (clean code, use of design patterns, not to deep inheritance structures and let's not forget unit testing!) it a way to make more people understand what you wrote. That in turn is needed for companies to keep their bussiness secure. That's also a recepie for small teams to have better understanding with the community. It's like a common meta language on top of the programming language itself.
It's always easier to talk about concepts from a purists point of view. Once you're faced with a real life problem things get trickier and the world is no longer just black and white. Just like the author of the article is very thorough in pointing out that they're not doing OOP the "OOP purist" tells you that OOP is the only way to go. The truth is somewhere in between.
There is no single answer, as long as you understand the different ways (OOP, entity systems, functional programming and many more) of doing things and can give good reason for why you're choosing one over the other in any given situation you're more likely to succeed.
About Entity Systems. It's an interesting conception but it brings nothing really new. For example it states:
OOP style would be for each Component to have zero or more methods, that some external thing has to invoke at some point. ES style is for each Component to have no methods but instead for the continuously running system to run it’s own internal methods against different Components one at a time.
But isn't it same as Martin Fowler's anti-pattern called "Anemic Domain Model" (which is extensively used nowadays, in fact) link ?
So basically ES is an "idea on the paper". For people to accept it, it MUST be proven with working code examples. There is not a single word in the article on how to implement this idea on practice. Nothing said about scalability concerns. Nothing said about fault tolerance...
As for your actual question I don't see how Entity Systems described in article can be similar to relational databases. Relational databases have no such thing as "aspects" that are described in the article. In fact, relational - based on tables data structure - is very limited when it comes to working with hierarchical data, for example. More limited than for example object databases...
Could you clarify what exactly you are trying to compare and prove here? OOP is a programming paradigm, one of the many. It's not perfect. It's not a silver bullet.
What does "Relation Oriented Programming" mean? Data-centric? Well, Microsoft was moving towards more data-centric style of programming until they given up on Linq2Sql and fully focused on their O/RM EntityFramework.
Also relational databases isn't everything. There is many different kinds of database architectures: hierarchical databases, network databases, object databases ect. And those can be even more efficient than relational. Relational are so popular for nearly the same reasons why OOP is so popular: it's simple, very easy to understand and most often efficient enough.
Ironically when oo programming arrived made it much easier to build larger systems, this was reflected in the ramp up in software to market.
Regarding scale and complexity, with good design you can build pretty complex systems.
see ddd Eric Evans for some principle patterns on handling complexity in oo.
However not all problem domains are best suited to all languages, if you have the freedom to choose a language choose one that suits your problem domain. or build a dsl if that's more appropriate.
We are software engineers after all, unless there is someone telling you how to do your job, just use the best tools for the job, or write them :)
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So...
I teach formal methods in software engineering. I also teach "agile methodologies". Most people seem to think this is contradictory. I think it makes a lot of sense... I also work for a company, where we need to actually get things done :) While I can apply my earned skill points on "specification" in a day-to-day basis, my colleagues typically flee away from the word "formal".
I used to think that this was due to the intrinsic way we learn how to program: we are usually driven to find a working solution, not to understand the problem. Then I thought this was due to the fact that most people in the formal community are not engineers, but mathematicians or computer scientists. Nowadays, I wonder if it just because the formal-methods community hide behind some kind of "obfuscation" law to use all the available UNICODE symbols, actively develop rude, unesthetic tools, and laugh in the face of standards.
Yes, I've been moving from a "blame them" to a "blame us" perspective ;-)
So, my question is: do you use any kind of formal methods in your company? Have you introduced them, or were they pre-requisites? What techniques do you use to clear the fog of mathematics from people's fears and incite them to use formal methods? What do you think current tools are lacking for a more general usage?
The key to getting people to buy into any methods or methodologies is to show them how it solves problems they are having. If they can see it will make their lives better you have a much improved chance of getting them to adopt the techniques.
And if you can't show them that, perhaps you wanted to adopt the methods based on philosophy rather than practicality. Unless the others share your philosophy then you're not going to get anywhere. And perhaps you shouldn't.
Over the decades there have been a great many methodologies. Newer ones always address the shortcomings of the old ones, yet projects still get in trouble and fail. Why? Because the rock stars that come up with new methodologies are rock stars, and have made a new methodology precisely because they understand the underlying issues and how to apply them. Those who come after tend to blindly follow the recipe, and it doesn't work so well.
So I think the best thing is to teach about the underlying problems and then show how various methods attempt to deal with those problems. The differences in companies, projects, and teams is so great that no one methodology can be applied successfully to all combinations. Learning to choose an appropriate tool and apply it well is crucial.
Thank you for all contributions. They are very insightful. Allow me to flame a bit (don't take it personal, though :-)
Most people seem to think that formal methods are just about program verification. Or critical systems. This may be true if we pursue the ultimate cliche: to prove we are doing the program right (v.s. validation, which asks, as a contributor said, if we are doing the right program).
But consider model finding/checking tools, such as Alloy. Learning to use a tool like this takes a negligable ammount of time for anyone used to UML and OO. Still, it can give you immediate insight over your model. It usually takes no more than 10 minutes to find a counter-example over a small enough subset of the model one's trying to use (and that includes describing the model in Alloy in the first place).
Take requirements engineering as an example. One usually draw a lot of UML. Few people use OCL, though, and many business rules are informally annoted in natural language. Why? Time constraints?
Now consider the fact that the majority just uses her/his gut-feeling to prove that a model is satisfiable. Again, why? I can take the same amount of time (probably even less, since I don't need to care about drawing aesthetics) to write that model in Alloy, and just check for satisfiability? And what kind of mathematics do I need to now? "Predicates"? Fancy name for IFs and booleans ;-) Quantifiers? Fancy names for ForEachs()...
What about big information systems? They don't need to be critical... Just try to analyze in your head a conceptual (not implementation!) diagram with over 600 classes. I see many people banging their head in the wall with easy-to-make model mistakes because they missed some constraint, or the model allows stupid things to happen.
The fact is, one does not need to use formal approaches from head to tail. Granted, I could prove a whole application in Coq, and certify that it is 100% compliant with some specification. This may be the Computer Scientist/Mathematician approach.
Still, with a GTD philisophy, why can't I delegate some tasks for the computer and allow it to help improving my development? Is it really a matter of "time", or plain, simple lack of technical abilities and will to learn/inovate?
Working with line of business IT development in an enterprise means having to transfer knowledge about the business from actual business people into the heads of developers. While I myself find abstract maths to be one of the greatest pastimes there is, it's a terrible communications tool. And communications is what it's all about. While I might conceivably have some success convincing IT people to embrace more abstract notations, I basically have no chance with the business people.
While there are some areas where I can see a role for formal methods in an enterprise (math- and logic-heavy specialist software, significant need for provable properties as in safety critical software) they provide little help with getting correct requirements on e.g. how to fulfil a customer order by issuing one or more supply orders to a set of possible external or internal providers.
I think the jury is still out on model based approaches and domain specific languages. I think they will succeed or fail depending on whether they provide quicker feedback from IT to the wishes and needs of the business side, and whether they presume business people will have to do any significant studying.
Technology is easy. Communication is hard. Formal methods may help us do things right, but those I've seen do nothing to help us do the right things. (Yes, these are cliches, but that's because they're inescapably and painfully true.)
I'm taking a course on 'Specification and Verification'. As part of the course structure we are doing the following-
1. Learning tools like PVS(Prototype Verification System) http://pvs.csl.sri.com/ and SMV(Software Modeling and Verification) http://www.cs.cmu.edu/~modelcheck/smv.html
2. Apart from that we do dissect accidents which happened because of software failures. For e.g. - Failure of Ariane V
I feel formal methods are more applicable to scenarios where the failure cost is more than the design cost. And it seems apt to use them for softwares being used in critical systems. I guess it is used in avionics, chip design etc. and the current automobile industry is also drafting it into practice.
I have tried to get people to embrace formal specification methods a few times (Z and Alloy) and have made the same expirience that you have: Most people, while feeling that they serve a useful purpose, are very uncomfortable using them for actual work.
Funny enough, the same people are more than happy to produce utterly useless UML diagrams in ginormous quantities.
I think there are two main reasons for this:
a.) Many developers are uncomfortable with the level of abstraction required by a formal approach. The fact that most entry-level mathematics education is all calculus and non discrete-mathematics might have to do something with this.
b.) Formal methods require a very bottom up design aproach where you design your core model from the ground up and make it airtight and then connect it up to the actual user requirements by providing an interface on top of it. Since we tend to have requirements drive development efforts, a top-down approach feels more natural although it often leads to inconsistent models. It's like retrofitting a basement underneath your house after it has already been built.
Formal methods make no sense in systems where the cost of failure is low.
In a production web application, you've got multiple front-end boxes, multiple back-end boxes, multiple database boxes - if a program on any one of them fails, it's a non-event. Hardware is so cheap that you can build these systems for far less than the cost of formally specifying all your software.