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There are lots of programming and architecture patterns. Patterns allow to make code cleaner, reusable, maintainable, more testable & at last (but not at least) to feel the follower a real cool developer.
How do you rank these considerations? What does appeal you most when you decide to apply pattern?
I wonder how many times code reusability (especially for MVP, MVC patterns) was important? For example DAL library often shared between projects (it's reusable) but how often controllers/views (abstracted via interfaces) are reused?
I think you missed the single most important one from your list - more maintainable. Code that is well and consistently structured (as you get with easily reusable code) is much more easily maintained.
And as for reusablilty, then yes, on a number of occasions, usually something like : create a web page to save/update some record. Some months later - we need to expose this as a service for a third party to consume - if your code is structured well, this should be easy and low risk, as you're only adding a new front end.
I hope most people use patterns to learn how to solve design problems in certain context. All those non-functional requrements you mention can be really important depending on stakeholder needs for a project.
As for MVC etc. it is not meant only to be reused between projects, that is often not possible or a good idea. The benefits you get from MVC should be important in the project you use that architecture. You can change independently details in view and models, you can reuse views with controllers for different models, you should be able to change persistence details without affecting your controllers and views. All this is imho very important during development of a single project.
"Code reusability" as defined in many books is more or less a myth. Try to focus more on easy to read - easy to maintain. Don't start with "reusability" in mind, will be better if you will start to think first on testability and then to reuse something. Is important to deliver, to test, to have clean code, to refactor, to not repeat yourself and less important to build from the start components that can be reused between projects. Whatever is to be reused must be a natural process, more like a discovery: you see a repetition so you build something that can be reused in that specific situation.
Code complexity reduction ranks high, if I keep things simple, I can maintain the project better and work on it faster to add/change features.
Reusability is a tool, one that has its uses, but not in every place. I usually refactor for reusability those components that show a clear history of identical use in more than three places. Otherwise, I risk running into the need of specialized behavior in a place or two, and end up splitting a component in a couple of more specialized ones that share a similar structure, but would be hard to understand if kept together.
Testability is not something I personally put a lot of energy in. However it derives in many cases from the reduced code complexity: if there are not a lot of dependencies and intricate code paths, there will be less dangers to break tests or make them more difficult to perform.
As for showing-off-ability... well... the customer is interested in how well the app performs in terms of what he wants from it, not in terms of how "cool" my code is. 'nuff said
I remember when I was in DSA I was like wtf O(n) and wondering where would I use it other than in grad school or if you're not a PhD like Bloch. Somehow uses for it does pop up in business analysis, so I was wondering when have you guys had to call up your Big O skills to see how to write an algorithm, which data structure did you use to fit or whether you had to actually create a new ds (like your own implementation of a splay tree or trie).
Understanding Data Structures has been fundamental to many of the projects I've worked on, and that goes beyond the ten minute song 'n dance one does when asked such a question in an interview situation.
Granted that modern environments with all sorts of collection classes can make light work of storing and accessing large amounts of data, but having an understanding that a particular problem is best solved with a particular data structure can be a great timesaver. And by "timesaver" I mean "the difference between something working and not working".
Honestly, being able to answer that stuff is my biggest criterion for taking interviewees seriously in an interview. Knowing how basic data structures work, basic O(n) analysis, and some light theory is really crucial to being able to write large applications successfully.
It's important in the interview because it's important in the job. I've worked with techs in the past that were self taught, without taking the data structures course or reading a data structures book, and their code is occasionally bad in ways they should have seen coming.
If you don't know that n2 is going to run slowly compared to n log n, you've got more to learn.
As far as the later half of the data structures courses, it isn't generally applicable to most tech jobs, but if you ever do wind up needing it, you'll wish you had paid more attention.
Big-O notation is one of the basic notations used when describing algorithms implemented by a particular library. For example, all documentation on STL that I've seen describes various operations in terms of big-O, so naturally you have to e.g. understand the difference between O(1), O(log n) and O(n) to understand the implications of your choice of STL containers and algorithms. MSDN also does that for .NET classes, and IIRC Java documentation does that for standard Java classes. So, I'd say that knowing the notation is pretty much a requirement for understanding documentation of most popular frameworks out there.
Sure (even though I'm a humble MS in EE -- no PhD, no CS, differently from my colleague Joshua Block), I write a lot of stuff that needs to be highly scalable (or components that may need to be reused in highly scalable apps), so big-O considerations are most always at work in my design (and it's not hard to take them into account). The data structures I use are almost always from Python's simple but rich supply (which I did lend a hand developing;-), rarely is a totally custom one needed (rather than building on top of list, dict, etc); but when it does happen (e.g. the bitvectors in my open source project gmpy), no big deal.
I was able to use B-Trees right when I learned about them in algorithm class (that was about 15 years ago when there were much less open source implementations available). And even later the knowledge about the differences of e. g. container classes came in handy...
Absolutely: even though stacks, queues, etc. are pretty straightforward, it helps to have been introduced to them in a disciplined fashion.
B-Tree's and more advanced sorting are a bit more difficult so learning them early was a big benefit and I have indeed had to implement each of them at various points.
Finally, I created an algorithm for single-connected components a few years back that was significantly better than the one our signal-processing team was using but I couldn't convince them that it was better until I could show that it was O(n) complexity rather than O(nlogn).
...just to name a few examples.
Of course, if you are content to remain a CRUD-system hacker with no real desire to do more than collect a paycheck, then it may not be necessary...
I found my knowledge of data structures very useful when I needed to implement a customizable event-driven system about ten years ago. That's the biggie, but I use that sort of knowledge fairly frequently in lesser ways.
For me, knowing the exact algorithms has been... nice as background knowledge. However, the thing that's been the most useful is the more general background of having to pay attention to how different pieces of an algorithm interact. For instance, there can be places in code where moving one piece of code (ie, outside a loop) can make a huge difference in both time and space.
Its less of the specific knowledge the course taught and, rather, more that it acted like several years of experience. The course took something that might take years to encounter (have drilled into you) all the variations of in pure "real world experience" and condensed it.
The title of your question asks about data structures and algorithms, but the body of your question focuses on complexity analysis, so I'll focus on that too:
There are lots of programming jobs where being able to do complexity analysis is at least occasionally useful. See What career can I hope for if I like algorithms? for some examples of these.
I can think of several instances in my career where either I or a co-worker have discovered a a piece of code where the (usually time, sometimes space) complexity was higher that it should have been. eg: something that was quadratic or cubic when it could have been linear or nlog(n). Such code would work fine when given small inputs, but on larger inputs would quickly become really slow or consume all available memory. Knowing alternative algorithms and data structures, their complexities, and also how to analyze the complexity to build new algorithms is vital in being able to correct these problems (or avoid them in the first place).
Networking is all I've used it: in an implementation of traveling salesman.
Unfortunately I do a lot of "line of business" and "forms over data" apps, so most problems I work on can be solved by hammering together arrays, linked lists, and hash tables. However, I've had the chance to work my data structures magic here and there:
Due to weird complex business rules, I worked on an application which used a custom thread pool implemented as a leftist-heap.
My dev team struggled to write a complex multithreaded app. It was plagued with race conditions, dead locks, and lousy performance due to very fine-grained locking. We re-worked the code to share state between threads, opting to write a very light-weight wrapper to facilitate message passing. Next, we converting our linked lists and hash tables to immutable stacks and immutable style and immutable red-black trees, we had no more problems with thread safety or performance. The resulting code was immaculate and surprisingly readable.
Frequently, a business rules engine requires you to roll your own state machine, which is very naturally modelled as a graph where vertexes and states and edges are transitions between states.
If for no other reasons, I'm glad I took the time to readable about data structures and algorithms simply to be able picture novel problems a little differently, especially combinatorial problems and graph problems. Graph theory is no longer a synonym for "scary".
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I started my college two years ago, and since then I keep hearing "design your classes first". I really ask myself sometimes, should my solution to be a bunch of objects in the first place! Some say that you don't see its benefits because your codebase is very small - university projects. The project size excuse just don't go down my throat. If the solution goes well with the project, I believe it should be the right one also with the macro-version of that project.
I am not saying OOP is bad, I just feel it is abused in classrooms where students like me are told day and night that OOP is the right way.
IMHO, the proper answer shouldn't come from a professor, I prefer to hear it from real engineers in the field.
Is OOP the right approach always?
When is OOP the best approach?
When is OOP a bad approach?
This is a very general question. I am not asking for definite answers, just some real design experience from the field.
I don't care about performance. I am asking about design. I know it is engineering in real life.
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Thankful for all contributions. I chose Nosredna answer, because she addressed my questions in general and convinced me that I was wrong about the following :
If the solution goes well with the project, I believe it should be the right one also with the macro-version of that project.
The professors have the disadvantage that they can't put you on huge, nasty programs that go on for years, being worked on by many different programmers. They have to use rather unconvincing toy examples and try to trick you into seeing the bigger picture.
Essentially, they have to scare you into believing that when an HO gauge model train hits you, it'll tear your leg clean off. Only the most convincing profs can do it.
"If the solution goes well with the project, I believe it should be the right one also with the macro-version of that project."
That's where I disagree. A small project fits into your brain. The large version of it might not. To me, the benefit of OO is hiding enough of the details so that the big picture can still be crammed into my head. If you lack OO, you can still manage, but it means finding other ways to hide the complexity.
Keep your eye on the real goal--producing reliable code. OO works well in large programs because it helps you manage complexity. It also can aid in reusability.
But OO isn't the goal. Good code is the goal. If a procedural approach works and never gets complex, you win!
OOP is a real world computer concept that the university would be derelict to leave out of the curriculum. When you apply for jobs, you will be expected to be conversant in it.
That being said, pace jalf, OOP was primarily designed as a way to manage complexity. University projects written by one or two students on homework time are not a realistic setting for large projects like this, so the examples feel (and are) toy examples.
Also, it is important to realize that not everyone really sees OOP the same way. Some see it about encapsulation, and make huge classes that are very complex, but hide their state from any outside caller. Others want to make sure that a given object is only responsible for doing one thing and make a lot of small classes. Some seek an object model that closely mirrors real world abstractions that the program is trying to relate to, others see the object model as about how to organize the technical architecture of the problem, rather than the real world business model. There is no one true way with OOP, but at its core it was introduced as a way of managing complexity and keeping larger programs more maintainable over time.
OOP is the right approach when your data can be well structured into objects.
For instance, for an embedded device that's processing an incoming stream of bytes from a sensor, there might not be much that can be clearly objectified.
Also in cases where ABSOLUTE control over performance is critical (when every cycle counts), an OOP approach can introduce costs that might be nontrivial to compute.
In the real world, most often, your problem can be VERY well described in terms of objects, although the law of leaky abstractions must not be forgotten!
Industry generally resolves, eventually, for the most part, to using the right tool for the job, and you can see OOP in many many places. Exceptions are often made for high-performance and low-level. Of course, there are no hard and fast rules.
You can hammer in a screw if you stick at it long enough...
My 5 cents:
OOP is just one instance of a larger pattern: dealing with complexity by breaking down a big problem into smaller ones. Our feeble minds are limited to a small number of ideas they can handle at any given time. Even a moderately sized commercial application has more moving parts than most folks can fully maintain a complete mental picture of at a time. Some of the more successful design paradigms in software engineering capitalize on the notion of dealing with complexity. Whether it's breaking your architecture into layers, your program into modules, doing a functional breakdown of actions, using pre-built components, leveraging independent web services, or identifying objects and classes in your problem and solution spaces. Those are all tools for taming the beast that is complexity.
OOP has been particularly successful in several classes of problems. It works well when you can think about the problem in terms of "things" and the interactions between them. It works quite well when you're dealing with data, with user interfaces, or building general purpose libraries. The prevalence of these classes of apps helped make OOP ubiquitous. Other classes of problems call for other or additional tools. Operating systems distinguish kernel and user spaces, and isolate processes in part to avoid the complexity creep. Functional programming keeps data immutable to avoid the mesh of dependencies that occur with multithreading. Neither is your classic OOP design and yet they are crucial and successful in their own domains.
In your career, you are likely to face problems and systems that are larger than you could tackle entirely on your own. Your teacher are not only trying to equip you with the present tools of the trade. They are trying to convey that there are patterns and tools available for you to use when you are attempting to model real world problems. It's in your best interest to accumulate a collection of tools for your toolbox and choose the right tool(s) for the job. OOP is a powerful tool to have, but by far not the only one.
No...OOP is not always the best approach.
(A true) OOP design is the best approach when your problem can best be modeled as a set of objects that can accomplish your goals by communicating/using one another.
Good question...but I'm guessing Scientific/Analytic applications are probably the best example. The majority of their problems can best be approached by functional programming rather than object oriented programming.
...that being said, let the flaming begin. I'm sure there are holes and I'd love to learn why.
Is OOP the right approach always?
Nope.
When OOP is the best approach?
When it helps you.
When OOP is a bad approach?
When it impedes you.
That's really as specific as it gets. Sometimes you don't need OOP, sometimes it's not available in the language you're using, sometimes it really doesn't make a difference.
I will say this though, when it comes to technique and best practices continue to double check what your professors tell you. Just because they're teachers doesn't mean they're experts.
It might be helpful to think of the P of OOP as Principles rather than Programming. Whether or not you represent every domain concept as an object, the main OO principles (encapsulation, abstraction, polymorphism) are all immensely useful at solving particular problems, especially as software gets more complex. It's more important to have maintainable code than to have represented everything in a "pure" object hierarchy.
My experience is that OOP is mostly useful on a small scale - defining a class with certain behavior, and which maintains a number of invariants. Then I essentially just use that as yet another datatype to use with generic or functional programming.
Trying to design an entire application solely in terms of OOP just leads to huge bloated class hierarchies, spaghetti code where everything is hidden behind 5 layers of indirection, and even the smallest, most trivial unit of work ends up taking three seconds to execute.
OOP is useful --- when combined with other approaches.
But ultimately, every program is about doing, not about being. And OOP is about "being". About expressing that "this is a car. The car has 4 wheels. The car is green".
It's not interesting to model a car in your application. It's interesting to model *the car doing stuff. Processes are what's interesting, and in a nutshell, they are what your program should be organized around. Individual classes are there to help you express what your processes should do (if you want to talk about car things, it's easier to have a car object than having to talk about all the individual components it is made up of, but the only reason you want to talk about the car at all is because of what is happening to it. The user is driving it, or selling it, or you are modelling what happens to it if someone hits it with a hammer)
So I prefer to think in terms of functions. Those functions might operate on objects, sure, but the functions are the ones my program is about. And they don't have to "belong" to any particular class.
Like most questions of this nature, the answer is "it depends."
Frederick P. Brooks said it the best in "The Mythical Man-Month" that "there is no single strategy, technique or trick that will exponentially raise the productivity of programmers." You wouldn't use a broad sword to make a surgical incision and you wouldn't use a scalpel in a sword fight.
There are amazing benefits to OOP, but you need to be comfortable with the pattern to take advantage of these benefits. Knowing and understanding OOP also allows you to create a cleaner procedural implementation for your solutions because of the underlying concepts of separation of concerns.
I've seen some of the best results of using OOP when adding new functionality to a system or maintaining/improving a system. Unfortunately, it's not easy to get that kind of experience while attending a university.
I have yet to work on a project in the industry that was not a combination of both functional and OOP. It really comes down to your requirements and what are the best (maybe cheapest?) solutions for them.
OOP is not always the best approach. However it is the best approach in the majority of applications.
OOP is the best approach in any system that lend itself to objects and the interaction of objects. Most business applications are best implemented in an object-oriented way.
OOP is a bad approach for small 1 off applications where the cost of developing an framework of objects would exceed the needs of the moment.
Learning OOA, OOD & OOP skills will benefit the most programmers, so it is definately useful for Universities to teach it.
The relevance and history of OOP runs back to the Simula languages back in the 1960s as a way to engineer software conceptually, where the developed code defines both the structure of the source and general permissible interactions with it. Obvious advantages are that a well-defined and well-created object is self-justifying and consistently repeatable as well as reliable; ideally also able to be extended and overridden.
The only time I know of that OOP is a 'bad approach' is during an embedded system programming efforts where resource availability is restricted; of course that's assuming your environment gives you access to them at all (as was already stated).
The title asks one question, and the post asks another. What do you want to know?
OOP is a major paradigm, and it gets major attention. If metaprogramming becomes huge, it will get more attention. Java and C# are two of the most used languages at the moment (see: SO tags by number of uses). I think it's ignorant to state either way that OOP is a great/terrible paradigm.
I think your question can best be summarized by the old adage: "When the hammer is your tool, everything looks like a nail."
OOP is usually an excellent approach, but it does come with a certain amount of overhead, at least conceptual. I don't do OO for small programs, for example. However, it's something you really do need to learn, so I can see requiring it for small programs in a University setting.
If I have to do serious planning, I'm going to use OOP. If not, I won't.
This is for the classes of problems I've been doing (which includes modeling, a few games, and a few random things). It may be different for other fields, but I don't have experience with them.
My opinion, freely offered, worth as much...
OOD/OOP is a tool. How good of a tool depends on the person using it, and how appropriate it is to use in a particular case depends on the problem. If I give you a saw, you'll know how to cut wood, but you won't necessarily be able to build a house.
The buzz that I'm picking up on is that functional programming is the wave of the future because it's extremely friendly to multi-threaded environments, so OO might be obsolete by the time you graduate. ;-)
I was just looking at this question about SQL, and followed a link about DAO to wikipedia. And it mentions as a disadvantage:
"As with many design patterns, a design pattern increases the complexity of the application." -Wikipedia
Which suddenly made me wonder where this idea came from (because it lacks a citation). Personally I always considered patterns reduce to complexity of an application, but I might be delusional, so I'm wondering if this complexity is based on something or not.
Thanks.
If the person reading the code is aware of design patterns and their concept and is able to identify design patterns in practical use (not just the book examples) then they really do reduce the complexity.
However I've found with a lot of junior developers, which haven't heard much about design patterns or weren't aware of them at all, that they believe their use increases the complexity of the code.
I can understand it: You suddenly have more classes or code to go through to solve what seems to be a simple problem at first. If you're not aware of the benefits of design patterns, hacked solutions always look better.
I love design patterns, but they (apart from simple ones like Singleton) definitely add complexity to an application. They add some dimension to a design that is not intuitively obvious to a novice designer (and not part of the features of the programming language).
Some people might feel patterns reduce complexity because of the benefits they bring in terms of the software's non-functional requirements such as maintainability, extendability, reusability, etc. However, I disagree and see the benefits as a return on complexity investment. Perhaps in some cases patterns reduce complexity, but a theoretical discussion like that sheds more heat than light. Almost none of the answers so far used concrete examples, save https://stackoverflow.com/a/760968/1168342.
To be specific, many patterns increase accidental complexity of a design by introducing new structures (interfaces, methods, etc.) that weren't present in the design before the pattern was applied.
Let's use Visitor as an example.
Visitor is a way of separating operations from an object structure on which they operate.
Before the solution with Visitor, the operations are hard-coded into each Element of the object structure. The challenge for the developer is that adding new operations involves modifying the code in the various elements.
After the application of the Visitor pattern, there is an additional class hierarchy of visitors, which encapsulate the operations.
The flow of
control in the solution is definitely more complex, and will be harder
to debug (anyone who has implemented Visitor and tried to follow the
program flow of double-dispatched calls with accept/visit will know this).
Understanding and maintaining Visitor functions in terms of
cohesive units is less complicated than the alternative of coding
functions into each of the Elements in the fixed structure that is
visited. This is the benefit of the pattern.
It's difficult to say quantitatively how much increase there is in accidental complexity or how much easier it is to add new operations. I certainly don't agree with answers that make a blanket statement saying in the long-term, complexity is reduced with applying a pattern. It's not like your design "forgets" the double-dispatch added by Visitor's approach, just because you have code which more easily allows operations to be added. The complexity is a price (or tax) you pay to get the benefit in maintainability.
Patterns still have to be applied
Regardless of one's supposed familiarity with patterns, any given pattern must be applied to a solution. That application is going to be different every time (Martin Fowler said patterns are only half-baked solutions). Developers will always have to understand what classes are playing what roles in the existing design, which is subject to the essential complexity (the application problem's complexity) that is often non-trivial.
In the best case, understanding a design pattern applied in an application that's already complex may be trivial, but it's not 0 effort:
Patterns aren't always applied the same way. There are many variants of patterns -- Proxy comes to mind. I'm not sure that everyone agrees about how any given pattern should be applied.
Introducing one pattern (e.g., Strategy to encapsulate algorithms) often leads to other patterns to manage things properly (e.g., Factory to instantiate the concrete Strategies).
Introducing a pattern often leads to more responsibilities. Object cleanup when a Factory is used is not trivial (and also not documented in GoF). How many know about the so-called Lapsed-listener problem?
What happens if there is a change in the assumptions made about the need for the pattern (e.g., there is no longer a need to have multiple encapsulated algorithms provided by the Strategy pattern)? It's going to be extra work to remove the pattern later. If you don't remove it, new developers could be duped by its presence when they come on board. Patterns are intertwined between the classes playing the roles in the pattern. Removal is not trivial.
Erich Gamma gave an anecdote at ECOOP 2006 that designers in one case decided to remove the Abstract Factory pattern from a commercial multi-platform GUI widget framework (the classic Abstract Factory example!). As I remember the anecdote, the multiple-levels of indirection (polymorphic calls) in complex GUIs was a significant performance hit in the client code. Customers complained about GUIs being sluggish, and the "optimization" was to remove the indirections. In this case, performance trumped maintainability; the pattern was only making the coders happy, not the end users.
DAO example
In terms of the DAO example you cite in the question, if you're coding an application that will never need to run with varying databases, then the DAO pattern is an unneeded level of complexity. In general, if your code doesn't need the benefit that a pattern is supposed to provide, applying that pattern will increase your application's complexity unnecessarily.
Revolving door metaphor
Using buildings as a metaphor, let's consider a revolving door as a building design pattern. The following image comes from Wikipedia:
You can "see" the additional complexity in such a door. The benefits of revolving doors are in energy savings. They attempt to solve the problem where there are people frequently going in and out of a building, and opening/closing a standard door allows too much air to be exchanged between the inside the outside of the building each time.
It probably wouldn't make sense to install a revolving door as the entrance of a two-bedroom house, because there is not enough traffic to justify the additional complexity. The revolving door would still work in a two-bedroom house. The benefits in terms of energy savings would be small (and might actually be worse because of size and air-tightness relative to a conventional door). The door would surely cost more and would take up more space than a traditional door.
Design patterns often lead to additional levels of abstraction around a problem, and if not handled correctly then too much abstraction can lead to complexity.
However, since design patterns provide a common vocabulary to communicate ideas they also reduce complexity and increase maintainability.
At the end of the day it's a double-edged sword, but I can't imagine a situation where I'd avoid using a design pattern...
There's an infamous disease known as "Small Boy With A Pattern Syndrome" that usually strikes someone who has recently read the GoF book for the first time and suddenly sees patterns everywhere. That can add complexity and unnecessary abstraction.
Patterns are best added to code as a discovery or refactoring to solve a particular problem, in my opinion.
In the short term, design patterns will often increase the complexity of the code. They add extra abstractions in places they might not be strictly necessary. However, in the long term they reduce complexity because future enhancements and changes fit into the patterns in a simple way. Without the patterns, these changes would be much more intrusive and complexity would likely be much higher.
Take for example a decorator pattern. The first time you use it, it will add complexity because now you have to define an interface for the object and create another class to add the decoration. This could likely be done with a simple property and be done with. However, when you get to 5 or 20 decoarations, the complexity with a decorator is much less than with properties.
As Grover said, the power of Design patterns is dependent on the ability of programmers to recognize them when they see them. It's like reducing a mathematical problem to a simpler problem, and them solving the simpler one. To someone who doesn't realize this, though, it seems like you've just created another problem.
I think it's always a good idea to document explicitly, using comments and/or descriptive names, when you're using a pattern to solve a problem. This might even educate another programmer who comes across it about the pattern if he wasn't aware of this.
I think it depends on the "audience" i.e. the maintaining developers of the code base. If they are design pattern illiterate then yes it can increase complexity, because most things one doesn't understand are "complex".
If the team is design pattern literate, i.e. they understand the basics and understand the premise behind why design patterns are useful (and as important when they're not) then I think they reduce complexity.
After all Computer Science maybe a fledging science but it's got decades of experience under its belt. The chances are somebody has already solved your problem once before. Whether the answer is a design pattern, data structure or algorithm.
I rather like this humorous explanation by Dylan Beattie. I recommend the read (if nothing else to waste five minutes on a Friday morning!)
Design Patterns work like algorithms for Object Oriented Programming. Shows you how to put together objects in a meaningful relationship that performs a particular task. So I would say yes they reduce complexity by allowing you to understand the design of the software better. The same way with algorithms in procedural programs.
If I told you that X was using a Linked List with a Bubble Sort it would be a lot easier to following along with what the programmer was doing.
Design patterns increase the code and divide it into multiple parts. If the design pattern and concept is known then it doesn't sound complex but code based on design pattern you don't know then it looks complex.
I made some research about this topic in the scope of GoF patterns. I observed OO Metric value fluctuations after the design pattern refactorings, you can check it out using this link.
Design patterns definitely adds complexity upfront in return for more modular, maintainable, flexible and extensible code in the long run. Perhaps the iterator pattern epitomizes it best.
Using index to iterate a list is clearly very simple and intuitive thing to do yet design patterns encapsulates it an iterator with is definitely more complex than a simple index.
The trade off is that it will be fair to say it has taken away the (implementation) simplicity but in return has made it more flexible removing iteration responsibility from the container and objectifying it which can be reusable.
That's design patterns for you.
Design Patterns dont increase complexity, it can make things alot easier to read and maintain. It can be harder to new comers to integrate your developer team, but this effort will be benefitial as a whole.
Problem are Design Patterns as a whole, but the its abuse
They should neither increase nor decrease the complexity.
You should always use an appropriate design for your code. This may use common design patterns or not.
The main benefits to design patterns are
By learning them you have added more design tools to your toolbox
By learning their names, if you use them and put a comment stating the pattern you're using, it helps readers understand your design intent more concisely
When I teach patterns at Hopkins, the two big things I stress are:
Patterns are all about Communication of Intent
Don't use any of the specific patterns as a Golden Hammer; lock them in your toolbox and only pull them out if it makes sense for your application.
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One thing I struggle with is planning an application's architecture before writing any code.
I don't mean gathering requirements to narrow in on what the application needs to do, but rather effectively thinking about a good way to lay out the overall class, data and flow structures, and iterating those thoughts so that I have a credible plan of action in mind before even opening the IDE. At the moment it is all to easy to just open the IDE, create a blank project, start writing bits and bobs and let the design 'grow out' from there.
I gather UML is one way to do this but I have no experience with it so it seems kind of nebulous.
How do you plan an application's architecture before writing any code? If UML is the way to go, can you recommend a concise and practical introduction for a developer of smallish applications?
I appreciate your input.
I consider the following:
what the system is supposed to do, that is, what is the problem that the system is trying to solve
who is the customer and what are their wishes
what the system has to integrate with
are there any legacy aspects that need to be considered
what are the user interractions
etc...
Then I start looking at the system as a black box and:
what are the interactions that need to happen with that black box
what are the behaviours that need to happen inside the black box, i.e. what needs to happen to those interactions for the black box to exhibit the desired behaviour at a higher level, e.g. receive and process incoming messages from a reservation system, update a database etc.
Then this will start to give you a view of the system that consists of various internal black boxes, each of which can be broken down further in the same manner.
UML is very good to represent such behaviour. You can describe most systems just using two of the many components of UML, namely:
class diagrams, and
sequence diagrams.
You may need activity diagrams as well if there is any parallelism in the behaviour that needs to be described.
A good resource for learning UML is Martin Fowler's excellent book "UML Distilled" (Amazon link - sanitised for the script kiddie link nazis out there (-: ). This book gives you a quick look at the essential parts of each of the components of UML.
Oh. What I've described is pretty much Ivar Jacobson's approach. Jacobson is one of the Three Amigos of OO. In fact UML was initially developed by the other two persons that form the Three Amigos, Grady Booch and Jim Rumbaugh
I really find that a first-off of writing on paper or whiteboard is really crucial. Then move to UML if you want, but nothing beats the flexibility of just drawing it by hand at first.
You should definitely take a look at Steve McConnell's Code Complete-
and especially at his giveaway chapter on "Design in Construction"
You can download it from his website:
http://cc2e.com/File.ashx?cid=336
If you're developing for .NET, Microsoft have just published (as a free e-book!) the Application Architecture Guide 2.0b1. It provides loads of really good information about planning your architecture before writing any code.
If you were desperate I expect you could use large chunks of it for non-.NET-based architectures.
I'll preface this by saying that I do mostly web development where much of the architecture is already decided in advance (WebForms, now MVC) and most of my projects are reasonably small, one-person efforts that take less than a year. I also know going in that I'll have an ORM and DAL to handle my business object and data interaction, respectively. Recently, I've switched to using LINQ for this, so much of the "design" becomes database design and mapping via the DBML designer.
Typically, I work in a TDD (test driven development) manner. I don't spend a lot of time up front working on architectural or design details. I do gather the overall interaction of the user with the application via stories. I use the stories to work out the interaction design and discover the major components of the application. I do a lot of whiteboarding during this process with the customer -- sometimes capturing details with a digital camera if they seem important enough to keep in diagram form. Mainly my stories get captured in story form in a wiki. Eventually, the stories get organized into releases and iterations.
By this time I usually have a pretty good idea of the architecture. If it's complicated or there are unusual bits -- things that differ from my normal practices -- or I'm working with someone else (not typical), I'll diagram things (again on a whiteboard). The same is true of complicated interactions -- I may design the page layout and flow on a whiteboard, keeping it (or capturing via camera) until I'm done with that section. Once I have a general idea of where I'm going and what needs to be done first, I'll start writing tests for the first stories. Usually, this goes like: "Okay, to do that I'll need these classes. I'll start with this one and it needs to do this." Then I start merrily TDDing along and the architecture/design grows from the needs of the application.
Periodically, I'll find myself wanting to write some bits of code over again or think "this really smells" and I'll refactor my design to remove duplication or replace the smelly bits with something more elegant. Mostly, I'm concerned with getting the functionality down while following good design principles. I find that using known patterns and paying attention to good principles as you go along works out pretty well.
http://dn.codegear.com/article/31863
I use UML, and find that guide pretty useful and easy to read. Let me know if you need something different.
UML is a notation. It is a way of recording your design, but not (in my opinion) of doing a design. If you need to write things down, I would recommend UML, though, not because it's the "best" but because it is a standard which others probably already know how to read, and it beats inventing your own "standard".
I think the best introduction to UML is still UML Distilled, by Martin Fowler, because it's concise, gives pratical guidance on where to use it, and makes it clear you don't have to buy into the whole UML/RUP story for it to be useful
Doing design is hard.It can't really be captured in one StackOverflow answer. Unfortunately, my design skills, such as they are, have evolved over the years and so I don't have one source I can refer you to.
However, one model I have found useful is robustness analysis (google for it, but there's an intro here). If you have your use-cases for what the system should do, a domain model of what things are involved, then I've found robustness analysis a useful tool in connecting the two and working out what the key components of the system need to be.
But the best advice is read widely, think hard, and practice. It's not a purely teachable skill, you've got to actually do it.
I'm not smart enough to plan ahead more than a little. When I do plan ahead, my plans always come out wrong, but now I've spend n days on bad plans. My limit seems to be about 15 minutes on the whiteboard.
Basically, I do as little work as I can to find out whether I'm headed in the right direction.
I look at my design for critical questions: when A does B to C, will it be fast enough for D? If not, we need a different design. Each of these questions can be answer with a spike. If the spikes look good, then we have the design and it's time to expand on it.
I code in the direction of getting some real customer value as soon as possible, so a customer can tell me where I should be going.
Because I always get things wrong, I rely on refactoring to help me get them right. Refactoring is risky, so I have to write unit tests as I go. Writing unit tests after the fact is hard because of coupling, so I write my tests first. Staying disciplined about this stuff is hard, and a different brain sees things differently, so I like to have a buddy coding with me. My coding buddy has a nose, so I shower regularly.
Let's call it "Extreme Programming".
"White boards, sketches and Post-it notes are excellent design
tools. Complicated modeling tools have a tendency to be more
distracting than illuminating." From Practices of an Agile Developer
by Venkat Subramaniam and Andy Hunt.
I'm not convinced anything can be planned in advance before implementation. I've got 10 years experience, but that's only been at 4 companies (including 2 sites at one company, that were almost polar opposites), and almost all of my experience has been in terms of watching colossal cluster********s occur. I'm starting to think that stuff like refactoring is really the best way to do things, but at the same time I realize that my experience is limited, and I might just be reacting to what I've seen. What I'd really like to know is how to gain the best experience so I'm able to arrive at proper conclusions, but it seems like there's no shortcut and it just involves a lot of time seeing people doing things wrong :(. I'd really like to give a go at working at a company where people do things right (as evidenced by successful product deployments), to know whether I'm a just a contrarian bastard, or if I'm really as smart as I think I am.
I beg to differ: UML can be used for application architecture, but is more often used for technical architecture (frameworks, class or sequence diagrams, ...), because this is where those diagrams can most easily been kept in sync with the development.
Application Architecture occurs when you take some functional specifications (which describe the nature and flows of operations without making any assumptions about a future implementation), and you transform them into technical specifications.
Those specifications represent the applications you need for implementing some business and functional needs.
So if you need to process several large financial portfolios (functional specification), you may determine that you need to divide that large specification into:
a dispatcher to assign those heavy calculations to different servers
a launcher to make sure all calculation servers are up and running before starting to process those portfolios.
a GUI to be able to show what is going on.
a "common" component to develop the specific portfolio algorithms, independently of the rest of the application architecture, in order to facilitate unit testing, but also some functional and regression testing.
So basically, to think about application architecture is to decide what "group of files" you need to develop in a coherent way (you can not develop in the same group of files a launcher, a GUI, a dispatcher, ...: they would not be able to evolve at the same pace)
When an application architecture is well defined, each of its components is usually a good candidate for a configuration component, that is a group of file which can be versionned as a all into a VCS (Version Control System), meaning all its files will be labeled together every time you need to record a snapshot of that application (again, it would be hard to label all your system, each of its application can not be in a stable state at the same time)
I have been doing architecture for a while. I use BPML to first refine the business process and then use UML to capture various details! Third step generally is ERD! By the time you are done with BPML and UML your ERD will be fairly stable! No plan is perfect and no abstraction is going to be 100%. Plan on refactoring, goal is to minimize refactoring as much as possible!
I try to break my thinking down into two areas: a representation of the things I'm trying to manipulate, and what I intend to do with them.
When I'm trying to model the stuff I'm trying to manipulate, I come up with a series of discrete item definitions- an ecommerce site will have a SKU, a product, a customer, and so forth. I'll also have some non-material things that I'm working with- an order, or a category. Once I have all of the "nouns" in the system, I'll make a domain model that shows how these objects are related to each other- an order has a customer and multiple SKUs, many skus are grouped into a product, and so on.
These domain models can be represented as UML domain models, class diagrams, and SQL ERD's.
Once I have the nouns of the system figured out, I move on to the verbs- for instance, the operations that each of these items go through to commit an order. These usually map pretty well to use cases from my functional requirements- the easiest way to express these that I've found is UML sequence, activity, or collaboration diagrams or swimlane diagrams.
It's important to think of this as an iterative process; I'll do a little corner of the domain, and then work on the actions, and then go back. Ideally I'll have time to write code to try stuff out as I'm going along- you never want the design to get too far ahead of the application. This process is usually terrible if you think that you are building the complete and final architecture for everything; really, all you're trying to do is establish the basic foundations that the team will be sharing in common as they move through development. You're mostly creating a shared vocabulary for team members to use as they describe the system, not laying down the law for how it's gotta be done.
I find myself having trouble fully thinking a system out before coding it. It's just too easy to only bring a cursory glance to some components which you only later realize are much more complicated than you thought they were.
One solution is to just try really hard. Write UML everywhere. Go through every class. Think how it will interact with your other classes. This is difficult to do.
What I like doing is to make a general overview at first. I don't like UML, but I do like drawing diagrams which get the point across. Then I begin to implement it. Even while I'm just writing out the class structure with empty methods, I often see things that I missed earlier, so then I update my design. As I'm coding, I'll realize I need to do something differently, so I'll update my design. It's an iterative process. The concept of "design everything first, and then implement it all" is known as the waterfall model, and I think others have shown it's a bad way of doing software.
Try Archimate.