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  • Is dependency injection by hand a better alternative to composition and polymorphism?

    - by Drake Clarris
    First, I'm an entry level programmer; In fact, I'm finishing an A.S. degree with a final capstone project over the summer. In my new job, when there isn't some project for me to do (they're waiting to fill the team with more new hires), I've been given books to read and learn from while I wait - some textbooks, others not so much (like Code Complete). After going through these books, I've turned to the internet to learn as much as possible, and started learning about SOLID and DI (we talked some about Liskov's substitution principle, but not much else SOLID ideas). So as I've learned, I sat down to do to learn better, and began writing some code to utilize DI by hand (there are no DI frameworks on the development computers). Thing is, as I do it, I notice it feels familiar... and it seems like it is very much like work I've done in the past using composition of abstract classes using polymorphism. Am I missing a bigger picture here? Is there something about DI (at least by hand) that goes beyond that? I understand the possibility of having configurations not in code of some DI frameworks having some great benefits as far as changing things without having to recompile, but when doing it by hand, I'm not sure if it's any different than stated above... Some insight into this would be very helpful!

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  • Should all public methods in an abstract class be marked virtual?

    - by Justin Pihony
    I recently had to update an abstract base class on some OSS that I was using so that it was more testable by making them virtual (I could not use an interface as it combined two). This got me thinking whether I should mark all of the methods that I needed virtual, or if I should mark every public method/property virtual. I generally agree with Roy Osherove that every method should be made virtual, but I came across this article that got me thinking about whether this was necessary or not. I am going to limit this down to abstract classes for simplicity, however (whether all concrete public methods should be virtual is especially debatable, I am sure). I could see where you might want to allow a sub-class to use a method, but not want it overriding the implementation. However, as long as you trust that Liskov's Substitution Principle will be followed, then why would you not allow it to be overriden? By marking it abstract, you are forcing a certain override anyway, so, it seems to me that all public methods inside of an abstract class should indeed be marked virtual. However, I wanted to ask in case there was something I might not be thinking. Should all public methods within an abstract class be made virtual?

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  • Unit testing several implementation of the same trait/interface

    - by paradigmatic
    I program mostly in scala and java, using scalatest in scala and junit for unit testing. I would like to apply the very same tests to several implementations of the same interface/trait. The idea is to verify that the interface contract is enforced and to check Liskov substitution principle. For instance, when testing implementations of lists, tests could include: An instance should be empty, if and only if and only if it has zero size. After calling clear, the size sould be zero. Adding an element in the middle of a list, will increment by one the index of rhs elements. etc. What are the best practices ?

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  • What are the software design essentials? [closed]

    - by Craig Schwarze
    I've decided to create a 1 page "cheat sheet" of essential software design principles for my programmers. It doesn't explain the principles in any great depth, but is simply there as a reference and a reminder. Here's what I've come up with - I would welcome your comments. What have I left out? What have I explained poorly? What is there that shouldn't be? Basic Design Principles The Principle of Least Surprise – your solution should be obvious, predictable and consistent. Keep It Simple Stupid (KISS) - the simplest solution is usually the best one. You Ain’t Gonna Need It (YAGNI) - create a solution for the current problem rather than what might happen in the future. Don’t Repeat Yourself (DRY) - rigorously remove duplication from your design and code. Advanced Design Principles Program to an interface, not an implementation – Don’t declare variables to be of a particular concrete class. Rather, declare them to an interface, and instantiate them using a creational pattern. Favour composition over inheritance – Don’t overuse inheritance. In most cases, rich behaviour is best added by instantiating objects, rather than inheriting from classes. Strive for loosely coupled designs – Minimise the interdependencies between objects. They should be able to interact with minimal knowledge of each other via small, tightly defined interfaces. Principle of Least Knowledge – Also called the “Law of Demeter”, and is colloquially summarised as “Only talk to your friends”. Specifically, a method in an object should only invoke methods on the object itself, objects passed as a parameter to the method, any object the method creates, any components of the object. SOLID Design Principles Single Responsibility Principle – Each class should have one well defined purpose, and only one reason to change. This reduces the fragility of your code, and makes it much more maintainable. Open/Close Principle – A class should be open to extension, but closed to modification. In practice, this means extracting the code that is most likely to change to another class, and then injecting it as required via an appropriate pattern. Liskov Substitution Principle – Subtypes must be substitutable for their base types. Essentially, get your inheritance right. In the classic example, type square should not inherit from type rectangle, as they have different properties (you can independently set the sides of a rectangle). Instead, both should inherit from type shape. Interface Segregation Principle – Clients should not be forced to depend upon methods they do not use. Don’t have fat interfaces, rather split them up into smaller, behaviour centric interfaces. Dependency Inversion Principle – There are two parts to this principle: High-level modules should not depend on low-level modules. Both should depend on abstractions. Abstractions should not depend on details. Details should depend on abstractions. In modern development, this is often handled by an IoC (Inversion of Control) container.

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  • Is there a language or design pattern that allows the *removal* of object behavior or properties in a class hierarchy?

    - by Sebastien Diot
    A well-know shortcoming of traditional class hierarchies is that they are bad when it comes to model the real world. As an example, trying to represent animals species with classes. There are actually several problems when doing that, but one that I never saw a solution to is when a sub-class "looses" a behavior or properties that was defined in a super-class, like a penguin not being able to fly (there are probably better examples, but that's the first one that comes to my mind, having seen "Madagascar 2" recently). On the one hand, you don't want to define for every property and behavior some flag that specifies if it is at all present, and check it every time before accessing that behavior or property. You would just like to say that birds can fly, simply and clearly, in the Bird class. But then it would be nice if one could define "exceptions" afterward, without having to use some horrible hacks everywhere. This often happens when a system has been productive for a while. You suddenly find an "exception" that doesn't fit in the original design at all, and you don't want to change a large portion of your code to accommodate it. So, is there some language or design patterns that can cleanly handle this problem, without requiring major changes to the "super-class", and all the code that uses it? Even if a solution only handle a specific case, several solutions might together form a complete strategy. [EDIT] Forgot about the Liskov Substitution Principle. That is why you can't do it. Assuming you define "traits/interfaces" for all major "feature groups", you can freely implement traits in different branches of the hierarchy, like the Flying trait could be implemented by Birds, and some special kind of squirrels and fish. So my question could amount to "How could I un-implement a trait?" If your super-class is a Java Serializable, you have to be one too, even if there is no way for you to serialize your state, for example if you contained a "Socket". So one way to do it is to always define all your traits in pair from the start: Flying and NotFlying (which would throw UnsupportedOperationExceiption, if not checked against). The Not-trait would not define any new interface, and could be simply checked for. Sounds like a "cheap" solution, in particular if used from the start.

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  • Is throwing an error in unpredictable subclass-specific circumstances a violation of LSP?

    - by Motti Strom
    Say, I wanted to create a Java List<String> (see spec) implementation that uses a complex subsystem, such as a database or file system, for its store so that it becomes a simple persistent collection rather than an basic in-memory one. (We're limiting it specifically to a List of Strings for the purposes of discussion, but it could extended to automatically de-/serialise any object, with some help. We can also provide persistent Sets, Maps and so on in this way too.) So here's a skeleton implementation: class DbBackedList implements List<String> { private DbBackedList() {} /** Returns a list, possibly non-empty */ public static getList() { return new DbBackedList(); } public String get(int index) { return Db.getTable().getRow(i).asString(); // may throw DbExceptions! } // add(String), add(int, String), etc. ... } My problem lies with the fact that the underlying DB API may encounter connection errors that are not specified in the List interface that it should throw. My problem is whether this violates Liskov's Substitution Principle (LSP). Bob Martin actually gives an example of a PersistentSet in his paper on LSP that violates LSP. The difference is that his newly-specified Exception there is determined by the inserted value and so is strengthening the precondition. In my case the connection/read error is unpredictable and due to external factors and so is not technically a new precondition, merely an error of circumstance, perhaps like OutOfMemoryError which can occur even when unspecified. In normal circumstances, the new Error/Exception might never be thrown. (The caller could catch if it is aware of the possibility, just as a memory-restricted Java program might specifically catch OOME.) Is this therefore a valid argument for throwing an extra error and can I still claim to be a valid java.util.List (or pick your SDK/language/collection in general) and not in violation of LSP? If this does indeed violate LSP and thus not practically usable, I have provided two less-palatable alternative solutions as answers that you can comment on, see below. Footnote: Use Cases In the simplest case, the goal is to provide a familiar interface for cases when (say) a database is just being used as a persistent list, and allow regular List operations such as search, subList and iteration. Another, more adventurous, use-case is as a slot-in replacement for libraries that work with basic Lists, e.g if we have a third-party task queue that usually works with a plain List: new TaskWorkQueue(new ArrayList<String>()).start() which is susceptible to losing all it's queue in event of a crash, if we just replace this with: new TaskWorkQueue(new DbBackedList()).start() we get a instant persistence and the ability to share the tasks amongst more than one machine. In either case, we could either handle connection/read exceptions that are thrown, perhaps retrying the connection/read first, or allow them to throw and crash the program (e.g. if we can't change the TaskWorkQueue code).

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  • What is the best practice, point of view of well experienced developers

    - by Damien MIRAS
    My manager pushes me to use his self defined best practices. All of these practices are based on is own assumptions. I disagree with them and I would like to have some feedback of well experienced people about these practices. I would prefer answers from people involved in the maintenance of huge products and people whom have maintained the product for years. Developers with 15+ years of experience are preferred because my manager has that much experience himself. I have 7 years of experience. Here are the practices he wants me to use: never extends classes, use composition and interface instead because extending classes are unmaintainable and difficult to debug. What I think about that Extend when needed, respect "Liskov's Substitution Principle" and you'll never be stuck with a problem, but prefer composition and decoration. I don't know any serious project which has banned inheriting, sometimes it's impossible to not use that, i.e. in a UI framework. Design patterns are just unusable. In PHP, for simple use cases (for example a user needs a web interface to view a database table), his "best practice" is: copy some random php code wich we own, paste and modify it, put html and php code in same file, never use classes in PHP, it doesn't work well for small jobs, and in fact it doesn't work well at all, there is no good tool to work with. Copy & paste PHP code is good practice for maintenance because scripts are independent, if you have a bug somewhere you can fix it without side effects. What I think about that: NEVER EVER COPY code or do it because you have five minutes to deliver something, you will do some refactoring after that. Copy & paste code is a beginners error, if you have errors you'll have the error everywhere any time you have pasted it's a nightmare to maintain. If you repsect the "Open Close Principle" you'll rarely get edge effects, use unit test if you are afraid of that. For small jobs in PHP use at least something you get or write the HTML separately from the PHP code and reuse it any time you need it. Classes in PHP are mature, not as mature as other languages like python or java, but they are usable. There is tools to work with classes in PHP like Zend Studio that work very well. The use of classes or not depends not on the language you use but the programming paradigm you have choosen. I'm a OOP developer, I use PHP5, why do I have to shoot myself in the foot? When you find a simple bug in the code, and you can fix it simply, if you are not working on the code where you have found it, never fix it, even if it takes 5 seconds. He says to me his "best practices" are driven by the fact that he has a lot of experience in maintaining software in production (14 years) and he now knows what works and what doesn't work, what the community says is a fad, and the people advocating such principles as never copy & paste code, are not evolved in maintaining applications. What I think about that: If you find a bug fix it if you can do it quickly inform the people who've touched that code before, check if you have not introduced a new bug, ideally add a unit test for it. I currently work on a web commerce project, which serves 15k unique users per day. The code base has to be maintained and has been maintained this way since 2005. Ideally you include a short description of your position and experience in terms of years effectively maintaining an application which has been in production for real.

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  • An Alphabet of Eponymous Aphorisms, Programming Paradigms, Software Sayings, Annoying Alliteration

    - by Brian Schroer
    Malcolm Anderson blogged about “Einstein’s Razor” yesterday, which reminded me of my favorite software development “law”, the name of which I can never remember. It took much Wikipedia-ing to find it (Hofstadter’s Law – see below), but along the way I compiled the following list: Amara’s Law: We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run. Brook’s Law: Adding manpower to a late software project makes it later. Clarke’s Third Law: Any sufficiently advanced technology is indistinguishable from magic. Law of Demeter: Each unit should only talk to its friends; don't talk to strangers. Einstein’s Razor: “Make things as simple as possible, but not simpler” is the popular paraphrase, but what he actually said was “It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience”, an overly complicated quote which is an obvious violation of Einstein’s Razor. (You can tell by looking at a picture of Einstein that the dude was hardly an expert on razors or other grooming apparati.) Finagle's Law of Dynamic Negatives: Anything that can go wrong, will—at the worst possible moment. - O'Toole's Corollary: The perversity of the Universe tends towards a maximum. Greenspun's Tenth Rule: Any sufficiently complicated C or Fortran program contains an ad hoc, informally-specified, bug-ridden, slow implementation of half of Common Lisp. (Morris’s Corollary: “…including Common Lisp”) Hofstadter's Law: It always takes longer than you expect, even when you take into account Hofstadter's Law. Issawi’s Omelet Analogy: One cannot make an omelet without breaking eggs - but it is amazing how many eggs one can break without making a decent omelet. Jackson’s Rules of Optimization: Rule 1: Don't do it. Rule 2 (for experts only): Don't do it yet. Kaner’s Caveat: A program which perfectly meets a lousy specification is a lousy program. Liskov Substitution Principle (paraphrased): Functions that use pointers or references to base classes must be able to use objects of derived classes without knowing it Mason’s Maxim: Since human beings themselves are not fully debugged yet, there will be bugs in your code no matter what you do. Nils-Peter Nelson’s Nil I/O Rule: The fastest I/O is no I/O.    Occam's Razor: The simplest explanation is usually the correct one. Parkinson’s Law: Work expands so as to fill the time available for its completion. Quentin Tarantino’s Pie Principle: “…you want to go home have a drink and go and eat pie and talk about it.” (OK, he was talking about movies, not software, but I couldn’t find a “Q” quote about software. And wouldn’t it be cool to write a program so great that the users want to eat pie and talk about it?) Raymond’s Rule: Computer science education cannot make anybody an expert programmer any more than studying brushes and pigment can make somebody an expert painter.  Sowa's Law of Standards: Whenever a major organization develops a new system as an official standard for X, the primary result is the widespread adoption of some simpler system as a de facto standard for X. Turing’s Tenet: We shall do a much better programming job, provided we approach the task with a full appreciation of its tremendous difficulty, provided that we respect the intrinsic limitations of the human mind and approach the task as very humble programmers.  Udi Dahan’s Race Condition Rule: If you think you have a race condition, you don’t understand the domain well enough. These rules didn’t exist in the age of paper, there is no reason for them to exist in the age of computers. When you have race conditions, go back to the business and find out actual rules. Van Vleck’s Kvetching: We know about as much about software quality problems as they knew about the Black Plague in the 1600s. We've seen the victims' agonies and helped burn the corpses. We don't know what causes it; we don't really know if there is only one disease. We just suffer -- and keep pouring our sewage into our water supply. Wheeler’s Law: All problems in computer science can be solved by another level of indirection... Except for the problem of too many layers of indirection. Wheeler also said “Compatibility means deliberately repeating other people's mistakes.”. The Wrong Road Rule of Mr. X (anonymous): No matter how far down the wrong road you've gone, turn back. Yourdon’s Rule of Two Feet: If you think your management doesn't know what it's doing or that your organisation turns out low-quality software crap that embarrasses you, then leave. Zawinski's Law of Software Envelopment: Every program attempts to expand until it can read mail. Zawinski is also responsible for “Some people, when confronted with a problem, think 'I know, I'll use regular expressions.' Now they have two problems.” He once commented about X Windows widget toolkits: “Using these toolkits is like trying to make a bookshelf out of mashed potatoes.”

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  • Book Review: Brownfield Application Development in .NET

    - by DotNetBlues
    I recently finished reading the book Brownfield Application Development in .NET by Kyle Baley and Donald Belcham.  The book is available from Manning.  First off, let me say that I'm a huge fan of Manning as a publisher.  I've found their books to be top-quality, over all.  As a Kindle owner, I also appreciate getting an ebook copy along with the dead tree copy.  I find ebooks to be much more convenient to read, but hard-copies are easier to reference. The book covers, surprisingly enough, working with brownfield applications.  Which is well and good, if that term has meaning to you.  It didn't for me.  Without retreading a chunk of the first chapter, the authors break code bases into three broad categories: greenfield, brownfield, and legacy.  Greenfield is, essentially, new development that hasn't had time to rust and is (hopefully) being approached with some discipline.  Legacy applications are those that are more or less stable and functional, that do not expect to see a lot of work done to them, and are more likely to be replaced than reworked. Brownfield code is the gray (brown?) area between the two and the authors argue, quite effectively, that it is the most likely state for an application to be in.  Brownfield code has, in some way, been allowed to tarnish around the edges and can be difficult to work with.  Although I hadn't realized it, most of the code I've worked on has been brownfield.  Sometimes, there's talk of scrapping and starting over.  Sometimes, the team dismisses increased discipline as ivory tower nonsense.  And, sometimes, I've been the ignorant culprit vexing my future self. The book is broken into two major sections, plus an introduction chapter and an appendix.  The first section covers what the authors refer to as "The Ecosystem" which consists of version control, build and integration, testing, metrics, and defect management.  The second section is on actually writing code for brownfield applications and discusses object-oriented principles, architecture, external dependencies, and, of course, how to deal with these when coming into an existing code base. The ecosystem section is just shy of 140 pages long and brings some real meat to the matter.  The focus on "pain points" immediately sets the tone as problem-solution, rather than academic.  The authors also approach some of the topics from a different angle than some essays I've read on similar topics.  For example, the chapter on automated testing is on just that -- automated testing.  It's all well and good to criticize a project as conflating integration tests with unit tests, but it really doesn't make anyone's life better.  The discussion on testing is more focused on the "right" level of testing for existing projects.  Sometimes, an integration test is the best you can do without gutting a section of functional code.  Even if you can sell other developers and/or management on doing so, it doesn't actually provide benefit to your customers to rewrite code that works.  This isn't to say the authors encourage sloppy coding.  Far from it.  Just that they point out the wisdom of ignoring the sleeping bear until after you deal with the snarling wolf. The other sections take a similarly real-world, workable approach to the pain points they address.  As the section moves from technical solutions like version control and continuous integration (CI) to the softer, process issues of metrics and defect tracking, the authors begin to gently suggest moving toward a zero defect count.  While that really sounds like an unreasonable goal for a lot of ongoing projects, it's quite apparent that the authors have first-hand experience with taming some gruesome projects.  The suggestions are grounded and workable, and the difficulty of some situations is explicitly acknowledged. I have to admit that I started getting bored by the end of the ecosystem section.  No matter how valuable I think a good project manager or business analyst is to a successful ALM, at the end of the day, I'm a gear-head.  Also, while I agreed with a lot of the ecosystem ideas, in theory, I didn't necessarily feel that a lot of the single-developer projects that I'm often involved in really needed that level of rigor.  It's only after reading the sidebars and commentary in the coding section that I had the context for the arguments made in favor of a strong ecosystem supporting the development process.  That isn't to say that I didn't support good product management -- indeed, I've probably pushed too hard, on occasion, for a strong ALM outside of just development.  This book gave me deeper insight into why some corners shouldn't be cut and how damaging certain sins of omission can be. The code section, though, kept me engaged for its entirety.  Many technical books can be used as reference material from day one.  The authors were clear, however, that this book is not one of these.  The first chapter of the section (chapter seven, over all) addresses object oriented (OO) practices.  I've read any number of definitions, discussions, and treatises on OO.  None of the chapter was new to me, but it was a good review, and I'm of the opinion that it's good to review the foundations of what you do, from time to time, so I didn't mind. The remainder of the book is really just about how to apply OOP to existing code -- and, just because all your code exists in classes does not mean that it's object oriented.  That topic has the potential to be extremely condescending, but the authors miraculously managed to never once make me feel like a dolt or that they were wagging their finger at me for my prior sins.  Instead, they continue the "pain points" and problem-solution presentation to give concrete examples of how to apply some pretty academic-sounding ideas.  That's a point worth emphasizing, as my experience with most OO discussions is that they stay in the academic realm.  This book gives some very, very good explanations of why things like the Liskov Substitution Principle exist and why a corporate programmer should even care.  Even if you know, with absolute certainty, that you'll never have to work on an existing code-base, I would recommend this book just for the clarity it provides on OOP. This book goes beyond just theory, or even real-world application.  It presents some methods for fixing problems that any developer can, and probably will, encounter in the wild.  First, the authors address refactoring application layers and internal dependencies.  Then, they take you through those layers from the UI to the data access layer and external dependencies.  Finally, they come full circle to tie it all back to the overall process.  By the time the book is done, you're left with a lot of ideas, but also a reasonable plan to begin to improve an existing project structure. Throughout the book, it's apparent that the authors have their own preferred methodology (TDD and domain-driven design), as well as some preferred tools.  The "Our .NET Toolbox" is something of a neon sign pointing to that latter point.  They do not beat the reader over the head with anything resembling a "One True Way" mentality.  Even for the most emphatic points, the tone is quite congenial and helpful.  With some of the near-theological divides that exist within the tech community, I found this to be one of the more remarkable characteristics of the book.  Although the authors favor tools that might be considered Alt.NET, there is no reason the advice and techniques given couldn't be quite successful in a pure Microsoft shop with Team Foundation Server.  For that matter, even though the book specifically addresses .NET, it could be applied to a Java and Oracle shop, as well.

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