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  • Techniques to re-factor garbage and maintain sanity?

    - by Incognito
    So I'm sitting down to a nice bowl of c# spaghetti, and need to add something or remove something... but I have challenges everywhere from functions passing arguments that doesn't make sense, someone who doesn't understand data structures abusing strings, redundant variables, some comments are red-hearings, internationalization is on a per-every-output-level, SQL doesn't use any kind of DBAL, database connections are left open everywhere... Are there any tools or techniques I can use to at least keep track of the "functional integrity" of the code (meaning my "improvements" don't break it), or a resource online with common "bad patterns" that explains a good way to transition code? I'm basically looking for a guidebook on how to spin straw into gold. Here's some samples from the same 500 line function: protected void DoSave(bool cIsPostBack) { //ALWAYS a cPostBack cIsPostBack = true; SetPostBack("1"); string inCreate ="~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"; parseValues = new string []{"","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""}; if (!cIsPostBack) { //....... //.... //.... if (!cIsPostBack) { } else { } //.... //.... strHPhone = StringFormat(s1.Trim()); s1 = parseValues[18].Replace(encStr," "); strWPhone = StringFormat(s1.Trim()); s1 = parseValues[11].Replace(encStr," "); strWExt = StringFormat(s1.Trim()); s1 = parseValues[21].Replace(encStr," "); strMPhone = StringFormat(s1.Trim()); s1 = parseValues[19].Replace(encStr," "); //(hundreds of lines of this) //.... //.... SQL = "...... lots of SQL .... "; SqlCommand curCommand; curCommand = new SqlCommand(); curCommand.Connection = conn1; curCommand.CommandText = SQL; try { curCommand.ExecuteNonQuery(); } catch {} //.... } I've never had to refactor something like this before, and I want to know if there's something like a guidebook or knowledgebase on how to do this sort of thing, finding common bad patterns and offering the best solutions to repair them. I don't want to just nuke it from orbit,

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  • NetBeans IDE 7.3 Knows Null

    - by Geertjan
    What's the difference between these two methods, "test1" and "test2"? public int test1(String str) {     return str.length(); } public int test2(String str) {     if (str == null) {         System.err.println("Passed null!.");         //forgotten return;     }     return str.length(); } The difference, or at least, the difference that is relevant for this blog entry, is that whoever wrote "test2" apparently thinks that the variable "str" may be null, though did not provide a null check. In NetBeans IDE 7.3, you see this hint for "test2", but no hint for "test1", since in that case we don't know anything about the developer's intention for the variable and providing a hint in that case would flood the source code with too many false positives:  Annotations are supported in understanding how a piece of code is intended to be used. If method return types use @Nullable, @NullAllowed, @CheckForNull, the value is considered to be "strongly possible to be null", as well as if the variable is tested to be null, as shown above. When using @NotNull, @NonNull, @Nonnull, the value is considered to be non-null. (The exact FQNs of the annotations are ignored, only simple names are checked.) Here are examples showing where the hints are displayed for the non-null hints (the "strongly possible to be null" hints are not shown below, though you can see one of them in the screenshot above), together with a comment showing what is shown when you hover over the hint: There isn't a "one size fits all" refactoring for these various instances relating to null checks, hence you can't do an automated refactoring across your code base via tools in NetBeans IDE, as shown yesterday for class member reordering across code bases. However, you can, instead, go to Source | Inspect and then do a scan throughout a scope (e.g., current file/package/project or combinations of these or all open projects) for class elements that the IDE identifies as potentially having a problem in this area: Thanks to Jan Lahoda, who reports that this currently also works in NetBeans IDE 7.3 dev builds for fields but that may need to be disabled since right now too many false positives are returned, for help with the info above and any misunderstandings are my own fault!

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  • Google I/O 2012 - YouTube API + Cloud Rendering = Happy Mobile Gamers

    Google I/O 2012 - YouTube API + Cloud Rendering = Happy Mobile Gamers Jarek Wilkiewicz, Danny Hermes YouTube is one of the top destinations for gamers. Many console developers already incorporate video recording and uploading directly into their titles, but uploading to YouTube from a mobile game presents a unique set of challenges. Come and learn how the YouTube API combined with cloud computing can help enable video uploads in your mobile game. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 100 0 ratings Time: 57:05 More in Science & Technology

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  • Content API for Shopping Office Hours - June 12, 2012

    Content API for Shopping Office Hours - June 12, 2012 Hangout discussing Product Listing Ads (PLAs) and the Google Affiliate Network (GAN) with guest Mark Coppin (GAN) and Claire Hugo (PLAs) of Google. In the Hangout, we reference the video "How to create a new Product Listing Ads campaign" (www.youtube.com which can be found in the Getting Starting page on the Shopping/Ads integration site (www.google.com Also, check out the GAN site to learn more: www.google.com From: GoogleDevelopers Views: 703 6 ratings Time: 31:23 More in Science & Technology

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  • Google I/O 2012 - A Master Class in Map Styling

    Google I/O 2012 - A Master Class in Map Styling Scott Shawcroft, Jonah Jones Custom Styled Maps allow developers to customize the look and feel of the underlying Google Maps tiles. This makes it really easy to make a great looking map. You can tailor your map to your message, to your color scheme, or to help emphasize your data. In this class, master maps designers will help you build beautiful, elegant styles that make your maps work for you. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 23 0 ratings Time: 38:21 More in Science & Technology

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  • Google I/O 2012 - Android Design for Success

    Google I/O 2012 - Android Design for Success Rachel Garb, Jens Nagel, Nate Streu, Matias Duarte You have a great idea for an Android app. You want it to stand out among hundreds of thousands. You want your users to love it and tell everyone they know. The Android User Experience team is here to help. We'll talk about the Android Design guide and other tricks of the trade for creating apps that delight users and help them accomplish their goals. No design background is required. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 46 5 ratings Time: 01:03:04 More in Science & Technology

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  • Google I/O 2012 - New YouTube Android Player Tools

    Google I/O 2012 - New YouTube Android Player Tools Ross McIlroy, Anton Hansson If you are building Android smartphone, tablet or Google TV applications and want to incorporate high-quality YouTube video playback in your product this session will rock your world. For all I/O 2012 sessions, go to developers.google.com From: GoogleDevelopers Views: 1719 24 ratings Time: 51:10 More in Science & Technology

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  • Google+ Platform Office Hours for June 13th, 2012

    Google+ Platform Office Hours for June 13th, 2012 Here are the show notes for this week's office hours. This week was devoted to your questions and our answers. We covered a wide breadth of topics. 0:43 - Introductions 2:54 - About Tabletop Forge's KickStarter - goo.gl 10:00 - Can I run multiple Hangout Apps at the same time? 12:28 - Is Google looking into adding more powerful Hangout moderation controls? 13:47 - How do you use Hangout Apps with Hangouts on Air? - +Fraser Cain's tips and tricks for Hangouts on Air: goo.gl 23:40 - I have an Android game. How do I port it to the Hangouts API? 27:57 - Pre-hangout Apps, Hangouts on Air pre-rolls, scheduling hangouts and other ways to help viewers find your Hangouts on Air 33:55 - How do I bookmark useful Google+ posts with Google+? 38:13 - Can you add a host ID field to the Hangouts API? When will the overlay garbage collection improve? 40:17 - Hand movement tracking as part of the Hangouts API Thanks to everyone who joined the hangout and asked questions on Google+! From: GoogleDevelopers Views: 698 18 ratings Time: 44:16 More in Science & Technology

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  • Geo for Good Summit Highlights

    Geo for Good Summit Highlights The last week of September, Google hosted the Geo for Good User Summit, for nonprofit mapping and technology specialists to update the nonprofit community about new and special features of Google's mapping products. In this week's Maps Developers Live event, Mano Marks from Maps Developer Relations and Raleigh Seamster, Program Manager with the Google Earth Outreach team will talk about the highlights of the Summit and show off some great examples of people using Maps to help the world. From: GoogleDevelopers Views: 0 0 ratings Time: 00:00 More in Education

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  • parallel_for_each from amp.h – part 1

    - by Daniel Moth
    This posts assumes that you've read my other C++ AMP posts on index<N> and extent<N>, as well as about the restrict modifier. It also assumes you are familiar with C++ lambdas (if not, follow my links to C++ documentation). Basic structure and parameters Now we are ready for part 1 of the description of the new overload for the concurrency::parallel_for_each function. The basic new parallel_for_each method signature returns void and accepts two parameters: a grid<N> (think of it as an alias to extent) a restrict(direct3d) lambda, whose signature is such that it returns void and accepts an index of the same rank as the grid So it looks something like this (with generous returns for more palatable formatting) assuming we are dealing with a 2-dimensional space: // some_code_A parallel_for_each( g, // g is of type grid<2> [ ](index<2> idx) restrict(direct3d) { // kernel code } ); // some_code_B The parallel_for_each will execute the body of the lambda (which must have the restrict modifier), on the GPU. We also call the lambda body the "kernel". The kernel will be executed multiple times, once per scheduled GPU thread. The only difference in each execution is the value of the index object (aka as the GPU thread ID in this context) that gets passed to your kernel code. The number of GPU threads (and the values of each index) is determined by the grid object you pass, as described next. You know that grid is simply a wrapper on extent. In this context, one way to think about it is that the extent generates a number of index objects. So for the example above, if your grid was setup by some_code_A as follows: extent<2> e(2,3); grid<2> g(e); ...then given that: e.size()==6, e[0]==2, and e[1]=3 ...the six index<2> objects it generates (and hence the values that your lambda would receive) are:    (0,0) (1,0) (0,1) (1,1) (0,2) (1,2) So what the above means is that the lambda body with the algorithm that you wrote will get executed 6 times and the index<2> object you receive each time will have one of the values just listed above (of course, each one will only appear once, the order is indeterminate, and they are likely to call your code at the same exact time). Obviously, in real GPU programming, you'd typically be scheduling thousands if not millions of threads, not just 6. If you've been following along you should be thinking: "that is all fine and makes sense, but what can I do in the kernel since I passed nothing else meaningful to it, and it is not returning any values out to me?" Passing data in and out It is a good question, and in data parallel algorithms indeed you typically want to pass some data in, perform some operation, and then typically return some results out. The way you pass data into the kernel, is by capturing variables in the lambda (again, if you are not familiar with them, follow the links about C++ lambdas), and the way you use data after the kernel is done executing is simply by using those same variables. In the example above, the lambda was written in a fairly useless way with an empty capture list: [ ](index<2> idx) restrict(direct3d), where the empty square brackets means that no variables were captured. If instead I write it like this [&](index<2> idx) restrict(direct3d), then all variables in the some_code_A region are made available to the lambda by reference, but as soon as I try to use any of those variables in the lambda, I will receive a compiler error. This has to do with one of the direct3d restrictions, where only one type can be capture by reference: objects of the new concurrency::array class that I'll introduce in the next post (suffice for now to think of it as a container of data). If I write the lambda line like this [=](index<2> idx) restrict(direct3d), all variables in the some_code_A region are made available to the lambda by value. This works for some types (e.g. an integer), but not for all, as per the restrictions for direct3d. In particular, no useful data classes work except for one new type we introduce with C++ AMP: objects of the new concurrency::array_view class, that I'll introduce in the post after next. Also note that if you capture some variable by value, you could use it as input to your algorithm, but you wouldn’t be able to observe changes to it after the parallel_for_each call (e.g. in some_code_B region since it was passed by value) – the exception to this rule is the array_view since (as we'll see in a future post) it is a wrapper for data, not a container. Finally, for completeness, you can write your lambda, e.g. like this [av, &ar](index<2> idx) restrict(direct3d) where av is a variable of type array_view and ar is a variable of type array - the point being you can be very specific about what variables you capture and how. So it looks like from a large data perspective you can only capture array and array_view objects in the lambda (that is how you pass data to your kernel) and then use the many threads that call your code (each with a unique index) to perform some operation. You can also capture some limited types by value, as input only. When the last thread completes execution of your lambda, the data in the array_view or array are ready to be used in the some_code_B region. We'll talk more about all this in future posts… (a)synchronous Please note that the parallel_for_each executes as if synchronous to the calling code, but in reality, it is asynchronous. I.e. once the parallel_for_each call is made and the kernel has been passed to the runtime, the some_code_B region continues to execute immediately by the CPU thread, while in parallel the kernel is executed by the GPU threads. However, if you try to access the (array or array_view) data that you captured in the lambda in the some_code_B region, your code will block until the results become available. Hence the correct statement: the parallel_for_each is as-if synchronous in terms of visible side-effects, but asynchronous in reality.   That's all for now, we'll revisit the parallel_for_each description, once we introduce properly array and array_view – coming next. Comments about this post by Daniel Moth welcome at the original blog.

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  • What makes a project big?

    - by Jonny
    Just out of curiosity what's the difference between a small, medium and large size project? Is it measured by lines of code or complexity or what? Im building a bartering system and so far have about 1000 lines of code for login/registration. Even though there's lots of LOC i wouldnt consider it a big project because its not that complex though this is my first project so im not sure. How is it measured?

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  • Load Balance and Parallel Performance

    Load balancing an application workload among threads is critical to performance. However, achieving perfect load balance is non-trivial, and it depends on the parallelism within the application, workload, the number of threads, load balancing policy, and the threading implementation.

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