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  • Where to implement storable items

    - by James Hay
    I'm creating a multiplayer online trading game. The things that are traded range from raw items to complex products. For example Steel is a raw item. Mechanical Assembly is a more complex item that requires 2x Steel and maybe 1x Rubber. Then Hydraulics is an item that contains 2x Mechanical Assemblies and 1x Electronics (which is another complex item). So and so forth. These items will be created by me, players can't create their own items, so it doesn't need to be able to handle arbitrary layers of complexity for items. If my example isn't clear, think Minecraft. You have wooden planks, which can be made into sticks. From there the sticks - combined with metals - can be made into tools. My game is nothing to do with minecraft or any sandbox building game, but it uses a similar progressive complexity to creating items that I want to have in my game. My question is basically, how do you store something like this assuming that I will want to add more items in the future? Do you store it in a database or in a seperate library that the game uses? EDIT None of the items actually "do" anything, they are simply there to either sell, purchase, or combine with other items to make a more complex item, which can then be sold, purchased or combined... you get the idea. The items themselves would not have any properties, but the instances of the items would. For example an item that one player has would have a certain "quality" and if they were selling it a certain "price". An instance of that same item that a different player had would need to have a different "quality" and "price" if they were selling it. I think the price part will not be required on an individual item because instead I would have a "sale" object which was for a price and contained certain items.

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  • Collision planes confusion

    - by Jeffrey
    I'm following this tutorial by thecplusplusguy and in the linked video he explain that for example for the world basement and walls we need to create the actual rendered (shown to the player) walls and then duplicate them, place them in the same coordinates as the rendered walls and call them collision (by defining their material to collision). Then it defines in the Object loader function that those objects with material == collision are collision planes and should not be rendered but just used to check collision. Now I'm pretty confused. Why would we add this kind of complexity to a problem that can easily be solved by a simple loadObject(string plane_object, bool check_collision);: Creating only the walls object (by loading .obj file in plane_object) Define them also as collision planes whenever the check_collision is set to true In this case we have lowered the complexity of his method and make it more flexible and faster to develop (faster because we don't always have to make a copy for each plane and flexible because we don't hardcode the Object loader). The only case in which this method could not work is when we need hidden collision planes, and for that we could modify the loadObject() function like this: loadObject(string plane_object, bool check_collision = true, bool hide_object = false); Creating only the walls object (by loading .obj file in plane_object) Define them also as collision planes whenever the check_collision is set to true And add the ability to actually show the object or hide it based on hide_object. The final question is: am I right? What would the possible problem encountered with my solution versus his?

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  • Do you think Scala will be the dominant JVM langauge, ie be the next Java? [on hold]

    - by user1037729
    From what I've read about Scala do far I think it has some nice features but I do not think it should be "the next Java". It might however end up being the next Java (due to fashion rather than fact) but lets not hope it does not... To me adds a lot of complexity over Java which is a simple and scalable language. Scala Pattern matching allows you to perform some type/value checking in a more concise way, this is possible in Java, Scala's pattern matching has a limit to it, you cannot continuously match deeper and deeper down the object graph, so why not just stick to Java and use decent invariants? Scala provides tuples, easy enough to make in Java, create a static factory method and it all reads nicely too. Scala provides mixins, why not just use composition? I believe Scala implicit's are bad, they can lead to code becoming complex and hard to maintain, explicitness is good. Scala provides closures, well they will be in Java 8 too. Scala has lazy keyword for lazy instantiation, this is easy enough to do in Java by calling a getter which creates the instance when needed, no hidden magic here. Scala can be used with AKKA, well so can Java, there is an Java AKKA implementation. Scala offers addition functional features but these can all be created in Java, there are many frameworks with have implemented functional features in Java. All in all Scala seems to offer is addition complexity and thats it...

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  • The Enterprise Side of JavaFX: Part Two

    - by Janice J. Heiss
    A new article, part of a three-part series, now up on the front page of otn/java, by Java Champion Adam Bien, titled “The Enterprise Side of JavaFX,” shows developers how to implement the LightView UI dashboard with JavaFX 2. Bien explains that “the RESTful back end of the LightView application comes with a rudimentary HTML page that is used to start/stop the monitoring service, set the snapshot interval, and activate/deactivate the GlassFish monitoring capabilities.”He explains that “the configuration view implemented in the org.lightview.view.Browser component is needed only to start or stop the monitoring process or set the monitoring interval.”Bien concludes his article with a general summary of the principles applied:“JavaFX encourages encapsulation without forcing you to build models for each visual component. With the availability of bindable properties, the boundary between the view and the model can be reduced to an expressive set of bindable properties. Wrapping JavaFX components with ordinary Java classes further reduces the complexity. Instead of dealing with low-level JavaFX mechanics all the time, you can build simple components and break down the complexity of the presentation logic into understandable pieces. CSS skinning further helps with the separation of the code that is needed for the implementation of the presentation logic and the visual appearance of the application on the screen. You can adjust significant portions of an application's look and feel directly in CSS files without touching the actual source code.”Check out the article here.

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  • How to Assure an Effective Data Model

    As a general rule in my opinion the effectiveness of a data model can be directly related to the accuracy and complexity of a project’s requirements. For example there is no need to work on very detailed data models when the details surrounding a specific data model have not been defined or even clarified. Developing data models when the clarity of project requirements is limited tends to introduce designed issues because the proper details to create an effective data model are not even known. One way to avoid this issue is to create data models that correspond to the complexity of the existing project requirements so that when requirements are updated then new data models can be created based any new discoveries regarding requirements on a fine grain level.  This allows for data models to be composed of general entities to be created initially when a project’s requirements are very vague and then the entities are refined as new and more substantial requirements are defined or redefined. This promotes communication amongst all stakeholders within a project as they go through the process of defining and finalizing project requirements.In addition, here are some general tips that can be applied to projects in regards to data modeling.Initially model all data generally and slowly reactor the data model as new requirements and business constraints are applied to a project.Ensure that data modelers have the proper tools and training they need to design a data model accurately.Create a common location for all project documents so that everyone will be able to review a project’s data models along with any other project documentation.All data models should follow a clear naming schema that tells readers the intended purpose for the data and how it is going to be applied within a project.

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  • Basic is Best

    - by Eric A. Stephens
    Fellow foodies will recognize the recent movement towards "farm-to-table" restaurants. These venues attempt to simplify their menus and source ingredients as close to the source as possible. I had the opportunity to dine at such a restaurant the other evening. I was gushing about the appetizer to my server when she described the preparation for the item and then punctuated her comments with "basic is best". I reminded my fellow enterprise architect diners there was an architecture lesson in that statement. They rolled their eyes and chuckled. But they also knew I was right. I'm reminded of Frederick Brooks' book The Mythical Man Month and his latest The Design of Design. The former must read book talks about complexity. But he refrains from damning all complexity. The world we live in and enterprises we strive to transform with enterprise architecture are complicated organisms, much like the human body. But sometimes a simple solution is the best approach. Fewer applications (think: portfolio rationalization). Fewer components. Fewer lines of code. Whatever level of abstraction you are working at, less is more. I'm reminded of the enterprise architecture principle "Control Technical Diversity". At one firm I created pithy catch phrases for each principles. I named this one "Less is More". But perhaps another variation is what my server said the other night, "Basic is Best".

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  • Tail-recursive implementation of take-while

    - by Giorgio
    I am trying to write a tail-recursive implementation of the function take-while in Scheme (but this exercise can be done in another language as well). My first attempt was (define (take-while p xs) (if (or (null? xs) (not (p (car xs)))) '() (cons (car xs) (take-while p (cdr xs))))) which works correctly but is not tail-recursive. My next attempt was (define (take-while-tr p xs) (let loop ((acc '()) (ys xs)) (if (or (null? ys) (not (p (car ys)))) (reverse acc) (loop (cons (car ys) acc) (cdr ys))))) which is tail recursive but needs a call to reverse as a last step in order to return the result list in the proper order. I cannot come up with a solution that is tail-recursive, does not use reverse, only uses lists as data structure (using a functional data structure like a Haskell's sequence which allows to append elements is not an option), has complexity linear in the size of the prefix, or at least does not have quadratic complexity (thanks to delnan for pointing this out). Is there an alternative solution satisfying all the properties above? My intuition tells me that it is impossible to accumulate the prefix of a list in a tail-recursive fashion while maintaining the original order between the elements (i.e. without the need of using reverse to adjust the result) but I am not able to prove this. Note The solution using reverse satisfies conditions 1, 3, 4.

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  • Purpose oriented user accounts on a single desktop?

    - by dd_dent
    Starting point: I currently do development for Dynamics Ax, Android and an occasional dabble with Wordpress and Python. Soon, I'll start a project involving setting up WP on Google Apps Engine. Everything is, and should continue to, run from the same PC (running Linux Mint). Issue: I'm afraid of botching/bogging down my setup due to tinkering/installing multiple runtimes/IDE's/SDK's/Services, so I was thinking of using multiple users, each purposed to handle the task at hand (web, Android etc) and making each user as inert as possible to one another. What I need to know is the following: Is this a good/feasible practice? The second closest thing to this using remote desktops connections, either to computers or to VM's, which I'd rather avoid. What about switching users? Can it be made seamless? Anything else I should know? Update and clarification regarding VM's and whatnot: The reason I wish to avoid resorting to VM's is that I dislike the performance impact and sluggishness associated with it. I also suspect it might add a layer of complexity I wish to avoid. This answer by Wyatt is interesting but I think it's only partly suited for requirements (web development for example). Also, in reference to the point made about system wide installs, there is a level compromise I should accept as experessed by this for example. This option suggested by 9000 is also enticing (more than VM's actually) and by no means do I intend to "Juggle" JVMs and whatnot, partly due to the reason mentioned before. Regarding complexity, I agree and would consider what was said, only from my experience I tend to pollute my work environment with SDKs and runtimes I tried and discarded, which would occasionally leave leftovers which cause issues throught the session. What I really want is a set of well defined, non virtualized sessions from which I can choose at my leisure and be mostly (to a reasonable extent) safe from affecting each session from the other. And what I'm really asking is if and how can this be done using user accounts.

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  • SourceMonitor Beta Test Version 3.3.2.261 now available

    - by TATWORTH
    Source Monitor is a useful independent utility for producing code metrics. Beta Test Version 3.3.2.261 has been released.Download and test Source Monitor beta (Version 3.3.2.261 - 2.30 MBytes)  via HTTP"The Beta page is at http://www.campwoodsw.com/smbeta.htmlHere is the official description of it>The freeware program Source Monitor lets you see inside your software source code to find out how much code you have and to identify the relative complexity of your modules. For example, you can use Source Monitor to identify the code that is most likely to contain defects and thus warrants formal review. Source Monitor, written in C++, runs through your code at high speed. Source Monitor provides the following: Collects metrics in a fast, single pass through source files.Measures metrics for source code written in C++, C, C#, VB.NET, Java, Delphi, Visual Basic (VB6) or HTML.Includes method and function level metrics for C++, C, C#, VB.NET, Java, and Delphi. Offers Modified Complexity metric option. Saves metrics in checkpoints for comparison during software development projects.Displays and prints metrics in tables and charts, including Kiviat diagrams.Operates within a standard Windows GUI or inside your scripts using XML command files.Exports metrics to XML or CSV (comma-separated-value) files for further processing with other tools.

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  • Integrating Code Metrics in TFS 2010 Build

    - by Jakob Ehn
    The build process template and custom activity described in this post is available here: http://cid-ee034c9f620cd58d.office.live.com/self.aspx/BlogSamples/CodeMetricsSample.zip Running code metrics has been available since VS 2008, but only from inside the IDE. Yesterday Microsoft finally releases a Visual Studio Code Metrics Power Tool 10.0, a command line tool that lets you run code metrics on your applications.  This means that it is now possible to perform code metrics analysis on the build server as part of your nightly/QA builds (for example). In this post I will show how you can run the metrics command line tool, and also a custom activity that reads the output and appends the results to the build log, and also fails he build if the metric values exceeds certain (configurable) treshold values. The code metrics tool analyzes all the methods in the assemblies, measuring cyclomatic complexity, class coupling, depth of inheritance and lines of code. Then it calculates a Maintainability Index from these values that is a measure f how maintanable this method is, between 0 (worst) and 100 (best). For information on hwo this value is calculated, see http://blogs.msdn.com/b/codeanalysis/archive/2007/11/20/maintainability-index-range-and-meaning.aspx. After this it aggregates the information and present it at the class, namespace and module level as well. Running Metrics.exe in a build definition Running the actual tool is easy, just use a InvokeProcess activity last in the Compile the Project sequence, reference the metrics.exe file and pass the correct arguments and you will end up with a result XML file in the drop directory. Here is how it is done in the attached build process template: In the above sequence I first assign the path to the code metrics result file ([BinariesDirectory]\result.xml) to a variable called MetricsResultFile, which is then sent to the InvokeProcess activity in the Arguments property. Here are the arguments for the InvokeProcess activity: Note that we tell metrics.exe to analyze all assemblies located in the Binaries folder. You might want to do some more intelligent filtering here, you probably don’t want to analyze all 3rd party assemblies for example. Note also the path to the metrics.exe, this is the default location when you install the Code Metrics power tool. You must of course install the power tool on all build servers. Using the standard output logging (in the Handle Standard Output/Handle Error Output sections), we get the following output when running the build: Integrating Code Metrics into the build Having the results available next to the build result is nice, but we want to have results integrated in the build result itself, and also to affect the outcome of the build. The point of having QA builds that measure, for example, code metrics is to make it very clear how the code being built measures up to the standards of the project/company. Just having a XML file available in the drop location will not cause the developers to improve their code, but a (partially) failing build will! To do this, we need to write a custom activity that parses the metrics result file, logs it to the build log and fails the build if the values frfom the metrics is below/above some predefined treshold values. The custom activity performs the following steps Parses the XML. I’m using Linq 2 XSD for this, since the XML schema for the result file is available, it is vey easy to generate code that lets you query the structure using standard Linq operators. Runs through the metric result hierarchy and logs the metrics for each level and also verifies maintainability index and the cyclomatic complexity with the treshold values. The treshold values are defined in the build process template are are sent in as arguments to the custom activity If the treshold values are exceeded, the activity either fails or partially fails the current build. For more information about the structure of the code metrics result file, read Cameron Skinner's post about it. It is very simpe and easy to understand. I won’t go through the code of the custom activity here, since there is nothing special about it and it is available for download so you can look at it and play with it yourself. The treshold values for Maintainability Index and Cyclomatic Complexity is defined in the build process template, and can be modified per build definition: I have taken the default value for these settings from my colleague Terje Sandström post on Code Metrics - suggestions for approriate limits. You’ll notice that this is quite an improvement compared to using code metrics inside the IDE, where Red/Yellow/Green limits are fixed (and the default values are somewaht strange, see Terjes post for a discussion on this) This is the first version of the code metrics integration with TFS 2010 Build, I will proabably enhance the functionality and the logging (the “tree view” structure in the log becomes quite hard to read) soon. I will also consider adding it to the Community TFS Build Extensions site when it becomes a bit more mature. Another obvious improvement is to extend the data warehouse of TFS and push the metric results back to the warehouse and make it visible in the reports.

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  • 7 Reasons for Abandonment in eCommerce and the need for Contextual Support by JP Saunders

    - by Tuula Fai
    Shopper confidence, or more accurately the lack thereof, is the bane of the online retailer. There are a number of questions that influence whether a shopper completes a transaction, and all of those attributes revolve around knowledge. What products are available? What products are on offer? What would be the cost of the transaction? What are my options for delivery? In general, most online businesses do a good job of answering basic questions around the products as the shopper engages in the online journey, navigating the product catalog and working through the checkout process. The needs that are harder to address for the shopper are those that are less concerned with product specifics and more concerned with deciding whether the transaction met their needs and delivered value. A recent study by the Baymard Institute [1] finds that more than 60% of ecommerce site visitors will abandon their shopping cart. The study also identifies seven reasons for abandonment out of the commerce process [2]. Most of those reasons come down to poor usability within the commerce experience. Distractions. External distractions within the shopper’s external environment (TV, Children, Pets, etc.) or distractions on the eCommerce page can drive shopper abandonment. Ideally, the selection and check-out process should be straightforward. One common distraction is to drive the shopper away from the task at hand through pop-ups or re-directs. The shopper engaging with support information in the checkout process should not be directed away from the page to consume support. Though confidence may improve, the distraction also means abandonment may increase. Poor Usability. When the experience gets more complicated, buyer’s remorse can set in. While knowledge drives confidence, a lack of understanding erodes it. Therefore it is important that the commerce process is streamlined. In some cases, the number of clicks to complete a purchase is lengthy and unavoidable. In these situations, it is vital to ensure that the complexity of your experience can be explained with contextual support to avoid abandonment. If you can illustrate the solution to a complex action while the user is engaged in that action and address customer frustrations with your checkout process before they arise, you can decrease abandonment. Fraud. The perception of potential fraud can be enough to deter a buyer. Does your site look credible? Can shoppers trust your brand? Providing answers on the security of your experience and the levels of protection applied to profile information may play as big a role in ensuring the sale, as does the support you provide on the product offerings and purchasing process. Does it fit? If it is a clothing item or oversized furniture item, another common form of abandonment is for the shopper to question whether the item can be worn by the intended user. Providing information on the sizing applied to clothing, physical dimensions, and limitations on delivery/returns of oversized items will also assist the sale. A photo alone of the item will help, as it answers some of those questions, but won’t assuage all customer concerns about sizing and fit. Sometimes the customer doesn’t want to buy. Prospective buyers might be browsing through your catalog to kill time, or just might not have the money to purchase the item! You are unlikely to provide any information in contextual support to increase the likelihood to buy if the shopper already has no intentions of doing so. The customer will still likely abandon. Ensuring that any questions are proactively answered as they browse through your site can only increase their likelihood to return and buy at a future date. Can’t Buy. Errors or complexity at checkout can be another major cause of abandonment. Good contextual support is unlikely to help with severe errors caused by technical issues on your site, but it will have a big impact on customers struggling with complexity in the checkout process and needing a question answered prior to completing the sale. Embedded support within the checkout process to patiently explain how to complete a task will help increase conversion rates. Additional Costs. Tax, shipping and other costs or duties can dramatically increase the cost of the purchase and when unexpected, can increase abandonment, particularly if they can’t be adequately explained. Again, a lack of knowledge erodes confidence in the purchase, and cost concerns in particular, erode the perception of your brand’s trustworthiness. Again, providing information on what costs are additive and why they are being levied can decrease the likelihood that the customer will abandon out of the experience. Knowledge drives confidence and confidence drives conversion. If you’d like to understand best practices in providing contextual customer support in eCommerce to provide your shoppers with confidence, download the Oracle Cloud Service and Oracle Commerce - Contextual Support in Commerce White Paper. This white paper discusses the process of adding customer support, including a suggested process for finding where knowledge has the most influence on your shoppers and practical step-by-step illustrations on how contextual self-service can be added to your online commerce experience. Resources: [1] http://baymard.com/checkout-usability [2] http://baymard.com/blog/cart-abandonment

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  • Implementing a Custom Coherence PartitionAssignmentStrategy

    - by jpurdy
    A recent A-Team engagement required the development of a custom PartitionAssignmentStrategy (PAS). By way of background, a PAS is an implementation of a Java interface that controls how a Coherence partitioned cache service assigns partitions (primary and backup copies) across the available set of storage-enabled members. While seemingly straightforward, this is actually a very difficult problem to solve. Traditionally, Coherence used a distributed algorithm spread across the cache servers (and as of Coherence 3.7, this is still the default implementation). With the introduction of the PAS interface, the model of operation was changed so that the logic would run solely in the cache service senior member. Obviously, this makes the development of a custom PAS vastly less complex, and in practice does not introduce a significant single point of failure/bottleneck. Note that Coherence ships with a default PAS implementation but it is not used by default. Further, custom PAS implementations are uncommon (this engagement was the first custom implementation that we know of). The particular implementation mentioned above also faced challenges related to managing multiple backup copies but that won't be discussed here. There were a few challenges that arose during design and implementation: Naive algorithms had an unreasonable upper bound of computational cost. There was significant complexity associated with configurations where the member count varied significantly between physical machines. Most of the complexity of a PAS is related to rebalancing, not initial assignment (which is usually fairly simple). A custom PAS may need to solve several problems simultaneously, such as: Ensuring that each member has a similar number of primary and backup partitions (e.g. each member has the same number of primary and backup partitions) Ensuring that each member carries similar responsibility (e.g. the most heavily loaded member has no more than one partition more than the least loaded). Ensuring that each partition is on the same member as a corresponding local resource (e.g. for applications that use partitioning across message queues, to ensure that each partition is collocated with its corresponding message queue). Ensuring that a given member holds no more than a given number of partitions (e.g. no member has more than 10 partitions) Ensuring that backups are placed far enough away from the primaries (e.g. on a different physical machine or a different blade enclosure) Achieving the above goals while ensuring that partition movement is minimized. These objectives can be even more complicated when the topology of the cluster is irregular. For example, if multiple cluster members may exist on each physical machine, then clearly the possibility exists that at certain points (e.g. following a member failure), the number of members on each machine may vary, in certain cases significantly so. Consider the case where there are three physical machines, with 3, 3 and 9 members each (respectively). This introduces complexity since the backups for the 9 members on the the largest machine must be spread across the other 6 members (to ensure placement on different physical machines), preventing an even distribution. For any given problem like this, there are usually reasonable compromises available, but the key point is that objectives may conflict under extreme (but not at all unlikely) circumstances. The most obvious general purpose partition assignment algorithm (possibly the only general purpose one) is to define a scoring function for a given mapping of partitions to members, and then apply that function to each possible permutation, selecting the most optimal permutation. This would result in N! (factorial) evaluations of the scoring function. This is clearly impractical for all but the smallest values of N (e.g. a partition count in the single digits). It's difficult to prove that more efficient general purpose algorithms don't exist, but the key take away from this is that algorithms will tend to either have exorbitant worst case performance or may fail to find optimal solutions (or both) -- it is very important to be able to show that worst case performance is acceptable. This quickly leads to the conclusion that the problem must be further constrained, perhaps by limiting functionality or by using domain-specific optimizations. Unfortunately, it can be very difficult to design these more focused algorithms. In the specific case mentioned, we constrained the solution space to very small clusters (in terms of machine count) with small partition counts and supported exactly two backup copies, and accepted the fact that partition movement could potentially be significant (preferring to solve that issue through brute force). We then used the out-of-the-box PAS implementation as a fallback, delegating to it for configurations that were not supported by our algorithm. Our experience was that the PAS interface is quite usable, but there are intrinsic challenges to designing PAS implementations that should be very carefully evaluated before committing to that approach.

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  • Sorting Algorithms

    - by MarkPearl
    General Every time I go back to university I find myself wading through sorting algorithms and their implementation in C++. Up to now I haven’t really appreciated their true value. However as I discovered this last week with Dictionaries in C# – having a knowledge of some basic programming principles can greatly improve the performance of a system and make one think twice about how to tackle a problem. I’m going to cover briefly in this post the following: Selection Sort Insertion Sort Shellsort Quicksort Mergesort Heapsort (not complete) Selection Sort Array based selection sort is a simple approach to sorting an unsorted array. Simply put, it repeats two basic steps to achieve a sorted collection. It starts with a collection of data and repeatedly parses it, each time sorting out one element and reducing the size of the next iteration of parsed data by one. So the first iteration would go something like this… Go through the entire array of data and find the lowest value Place the value at the front of the array The second iteration would go something like this… Go through the array from position two (position one has already been sorted with the smallest value) and find the next lowest value in the array. Place the value at the second position in the array This process would be completed until the entire array had been sorted. A positive about selection sort is that it does not make many item movements. In fact, in a worst case scenario every items is only moved once. Selection sort is however a comparison intensive sort. If you had 10 items in a collection, just to parse the collection you would have 10+9+8+7+6+5+4+3+2=54 comparisons to sort regardless of how sorted the collection was to start with. If you think about it, if you applied selection sort to a collection already sorted, you would still perform relatively the same number of iterations as if it was not sorted at all. Many of the following algorithms try and reduce the number of comparisons if the list is already sorted – leaving one with a best case and worst case scenario for comparisons. Likewise different approaches have different levels of item movement. Depending on what is more expensive, one may give priority to one approach compared to another based on what is more expensive, a comparison or a item move. Insertion Sort Insertion sort tries to reduce the number of key comparisons it performs compared to selection sort by not “doing anything” if things are sorted. Assume you had an collection of numbers in the following order… 10 18 25 30 23 17 45 35 There are 8 elements in the list. If we were to start at the front of the list – 10 18 25 & 30 are already sorted. Element 5 (23) however is smaller than element 4 (30) and so needs to be repositioned. We do this by copying the value at element 5 to a temporary holder, and then begin shifting the elements before it up one. So… Element 5 would be copied to a temporary holder 10 18 25 30 23 17 45 35 – T 23 Element 4 would shift to Element 5 10 18 25 30 30 17 45 35 – T 23 Element 3 would shift to Element 4 10 18 25 25 30 17 45 35 – T 23 Element 2 (18) is smaller than the temporary holder so we put the temporary holder value into Element 3. 10 18 23 25 30 17 45 35 – T 23   We now have a sorted list up to element 6. And so we would repeat the same process by moving element 6 to a temporary value and then shifting everything up by one from element 2 to element 5. As you can see, one major setback for this technique is the shifting values up one – this is because up to now we have been considering the collection to be an array. If however the collection was a linked list, we would not need to shift values up, but merely remove the link from the unsorted value and “reinsert” it in a sorted position. Which would reduce the number of transactions performed on the collection. So.. Insertion sort seems to perform better than selection sort – however an implementation is slightly more complicated. This is typical with most sorting algorithms – generally, greater performance leads to greater complexity. Also, insertion sort performs better if a collection of data is already sorted. If for instance you were handed a sorted collection of size n, then only n number of comparisons would need to be performed to verify that it is sorted. It’s important to note that insertion sort (array based) performs a number item moves – every time an item is “out of place” several items before it get shifted up. Shellsort – Diminishing Increment Sort So up to now we have covered Selection Sort & Insertion Sort. Selection Sort makes many comparisons and insertion sort (with an array) has the potential of making many item movements. Shellsort is an approach that takes the normal insertion sort and tries to reduce the number of item movements. In Shellsort, elements in a collection are viewed as sub-collections of a particular size. Each sub-collection is sorted so that the elements that are far apart move closer to their final position. Suppose we had a collection of 15 elements… 10 20 15 45 36 48 7 60 18 50 2 19 43 30 55 First we may view the collection as 7 sub-collections and sort each sublist, lets say at intervals of 7 10 60 55 – 20 18 – 15 50 – 45 2 – 36 19 – 48 43 – 7 30 10 55 60 – 18 20 – 15 50 – 2 45 – 19 36 – 43 48 – 7 30 (Sorted) We then sort each sublist at a smaller inter – lets say 4 10 55 60 18 – 20 15 50 2 – 45 19 36 43 – 48 7 30 10 18 55 60 – 2 15 20 50 – 19 36 43 45 – 7 30 48 (Sorted) We then sort elements at a distance of 1 (i.e. we apply a normal insertion sort) 10 18 55 60 2 15 20 50 19 36 43 45 7 30 48 2 7 10 15 18 19 20 30 36 43 45 48 50 55 (Sorted) The important thing with shellsort is deciding on the increment sequence of each sub-collection. From what I can tell, there isn’t any definitive method and depending on the order of your elements, different increment sequences may perform better than others. There are however certain increment sequences that you may want to avoid. An even based increment sequence (e.g. 2 4 8 16 32 …) should typically be avoided because it does not allow for even elements to be compared with odd elements until the final sort phase – which in a way would negate many of the benefits of using sub-collections. The performance on the number of comparisons and item movements of Shellsort is hard to determine, however it is considered to be considerably better than the normal insertion sort. Quicksort Quicksort uses a divide and conquer approach to sort a collection of items. The collection is divided into two sub-collections – and the two sub-collections are sorted and combined into one list in such a way that the combined list is sorted. The algorithm is in general pseudo code below… Divide the collection into two sub-collections Quicksort the lower sub-collection Quicksort the upper sub-collection Combine the lower & upper sub-collection together As hinted at above, quicksort uses recursion in its implementation. The real trick with quicksort is to get the lower and upper sub-collections to be of equal size. The size of a sub-collection is determined by what value the pivot is. Once a pivot is determined, one would partition to sub-collections and then repeat the process on each sub collection until you reach the base case. With quicksort, the work is done when dividing the sub-collections into lower & upper collections. The actual combining of the lower & upper sub-collections at the end is relatively simple since every element in the lower sub-collection is smaller than the smallest element in the upper sub-collection. Mergesort With quicksort, the average-case complexity was O(nlog2n) however the worst case complexity was still O(N*N). Mergesort improves on quicksort by always having a complexity of O(nlog2n) regardless of the best or worst case. So how does it do this? Mergesort makes use of the divide and conquer approach to partition a collection into two sub-collections. It then sorts each sub-collection and combines the sorted sub-collections into one sorted collection. The general algorithm for mergesort is as follows… Divide the collection into two sub-collections Mergesort the first sub-collection Mergesort the second sub-collection Merge the first sub-collection and the second sub-collection As you can see.. it still pretty much looks like quicksort – so lets see where it differs… Firstly, mergesort differs from quicksort in how it partitions the sub-collections. Instead of having a pivot – merge sort partitions each sub-collection based on size so that the first and second sub-collection of relatively the same size. This dividing keeps getting repeated until the sub-collections are the size of a single element. If a sub-collection is one element in size – it is now sorted! So the trick is how do we put all these sub-collections together so that they maintain their sorted order. Sorted sub-collections are merged into a sorted collection by comparing the elements of the sub-collection and then adjusting the sorted collection. Lets have a look at a few examples… Assume 2 sub-collections with 1 element each 10 & 20 Compare the first element of the first sub-collection with the first element of the second sub-collection. Take the smallest of the two and place it as the first element in the sorted collection. In this scenario 10 is smaller than 20 so 10 is taken from sub-collection 1 leaving that sub-collection empty, which means by default the next smallest element is in sub-collection 2 (20). So the sorted collection would be 10 20 Lets assume 2 sub-collections with 2 elements each 10 20 & 15 19 So… again we would Compare 10 with 15 – 10 is the winner so we add it to our sorted collection (10) leaving us with 20 & 15 19 Compare 20 with 15 – 15 is the winner so we add it to our sorted collection (10 15) leaving us with 20 & 19 Compare 20 with 19 – 19 is the winner so we add it to our sorted collection (10 15 19) leaving us with 20 & _ 20 is by default the winner so our sorted collection is 10 15 19 20. Make sense? Heapsort (still needs to be completed) So by now I am tired of sorting algorithms and trying to remember why they were so important. I think every year I go through this stuff I wonder to myself why are we made to learn about selection sort and insertion sort if they are so bad – why didn’t we just skip to Mergesort & Quicksort. I guess the only explanation I have for this is that sometimes you learn things so that you can implement them in future – and other times you learn things so that you know it isn’t the best way of implementing things and that you don’t need to implement it in future. Anyhow… luckily this is going to be the last one of my sorts for today. The first step in heapsort is to convert a collection of data into a heap. After the data is converted into a heap, sorting begins… So what is the definition of a heap? If we have to convert a collection of data into a heap, how do we know when it is a heap and when it is not? The definition of a heap is as follows: A heap is a list in which each element contains a key, such that the key in the element at position k in the list is at least as large as the key in the element at position 2k +1 (if it exists) and 2k + 2 (if it exists). Does that make sense? At first glance I’m thinking what the heck??? But then after re-reading my notes I see that we are doing something different – up to now we have really looked at data as an array or sequential collection of data that we need to sort – a heap represents data in a slightly different way – although the data is stored in a sequential collection, for a sequential collection of data to be in a valid heap – it is “semi sorted”. Let me try and explain a bit further with an example… Example 1 of Potential Heap Data Assume we had a collection of numbers as follows 1[1] 2[2] 3[3] 4[4] 5[5] 6[6] For this to be a valid heap element with value of 1 at position [1] needs to be greater or equal to the element at position [3] (2k +1) and position [4] (2k +2). So in the above example, the collection of numbers is not in a valid heap. Example 2 of Potential Heap Data Lets look at another collection of numbers as follows 6[1] 5[2] 4[3] 3[4] 2[5] 1[6] Is this a valid heap? Well… element with the value 6 at position 1 must be greater or equal to the element at position [3] and position [4]. Is 6 > 4 and 6 > 3? Yes it is. Lets look at element 5 as position 2. It must be greater than the values at [4] & [5]. Is 5 > 3 and 5 > 2? Yes it is. If you continued to examine this second collection of data you would find that it is in a valid heap based on the definition of a heap.

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  • Transformation of Product Management in Telecommunications for Rapid Launch of Next Generation Products

    - by raul.goycoolea
    @font-face { font-family: "Arial"; }@font-face { font-family: "Courier New"; }@font-face { font-family: "Wingdings"; }@font-face { font-family: "Cambria"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; font-size: 12pt; font-family: "Times New Roman"; }a:link, span.MsoHyperlink { color: blue; text-decoration: underline; }a:visited, span.MsoHyperlinkFollowed { color: purple; text-decoration: underline; }p.MsoListParagraph, li.MsoListParagraph, div.MsoListParagraph { margin: 0cm 0cm 0.0001pt 36pt; font-size: 12pt; font-family: "Times New Roman"; }p.MsoListParagraphCxSpFirst, li.MsoListParagraphCxSpFirst, div.MsoListParagraphCxSpFirst { margin: 0cm 0cm 0.0001pt 36pt; font-size: 12pt; font-family: "Times New Roman"; }p.MsoListParagraphCxSpMiddle, li.MsoListParagraphCxSpMiddle, div.MsoListParagraphCxSpMiddle { margin: 0cm 0cm 0.0001pt 36pt; font-size: 12pt; font-family: "Times New Roman"; }p.MsoListParagraphCxSpLast, li.MsoListParagraphCxSpLast, div.MsoListParagraphCxSpLast { margin: 0cm 0cm 0.0001pt 36pt; font-size: 12pt; font-family: "Times New Roman"; }div.Section1 { page: Section1; }ol { margin-bottom: 0cm; }ul { margin-bottom: 0cm; } The Telecom industry continues to evolve through disruptive products, uncertain markets, shorter product lifecycles and convergence of technologies. Today’s market has moved from network centric to consumer centric and focuses primarily on the customer experience. It has resulted in several product management challenges such as an increased complexity and volume of offerings, creating product variants, accelerating time-to-market, ability to provide multiple product views for varied stakeholders, leveraging OSS intelligence to BSS layer, product co-creation and increasing audit and security concerns for service providers. The document discusses how enterprise product management enabled by PLM-based product catalogue solutions helps to launch next generation products rapidly in the context of the Telecommunication Industry.   1.0.       Introduction   Figure 1: Business Scenario   Modern business demands the launch of complex products in a very short timeframe and effecting changes in the price plan faster without IT intervention. One of the key transformation initiatives companies are focusing on is in the area of product management transformation and operational efficiency improvement. As part of these initiatives, companies are investing in best- in-class COTs-based Product Management solutions developed on industry-wide standards.   The new COTs packages are planned to integrate with existing or new B/OSS systems to provide a strategic end-to-end agile solution for reduced time-to-market and order journey time. In addition, system rationalization is being undertaken to phase out legacy systems and migrate to strategic systems.   2.0.       An Overview of Product Management in Telecom   Product data in telecom is multi- dimensional and difficult to manage. It increased significantly due to the complexity of the product, product offerings on the converged network, increased volume of offerings, bundled offering structures and ever increasing regulatory requirements.   In addition, the shrinking product lifecycle in telecom makes it difficult to manage the dynamic product data. Mergers and acquisitions coupled with organic growth pose major challenges in product portfolio management. It is a roadblock in the journey towards becoming an agile organization.       Figure 2: Complexity in Product Management   Network Technology’ is the new dimension in telecom product management where the same products are realized through different networks i.e., Soiled network to Converged network. Consequently, the product solution is different.     Figure 3: Current Scenario - Pain Points in Product Management   The major business implications arising out of the current scenario are slow time-to-market and an inefficient process that affects innovation.   3.0. Transformation of Next Generation Product Management   Companies must focus on their Product Management Transformation Journey in the areas of:   ·       Management of single truth of product information across the organization/geographies which is currently managed in heterogeneous systems   ·       Management of the Intellectual Property (IP) on the product concept and partnership in the design of discrete components to integrate into the system   ·       Leveraging structured and unstructured product data within the extended enterprise to extract consumer insights and drive innovation   ·       Management of effective operational separation to comply with regulatory bodies   ·       Reuse of existing designs and add relevant features such as value-added services to enable effective product bundling     Figure 4: Next generation needs   PLM-based Enterprise Product Catalogue solutions efficiently address the above requirements and act as an enabler towards product management transformation and rapid product launch.   4.0. PLM-based Enterprise Product Management     Figure 5: PLM-based Enterprise Product Mastering   Enterprise Product Management (EPM) enables the business to manage complex product attributes of data in complex environments. Product Mastering helps create a 'single view' of the product by creating a business-driven, IT-supported environment where a global 'single truth record' is created, managed and reused.   4.1 The Business Case for Telco PLM-based solutions for Enterprise Product Management   ·       Telco PLM-based Product Mastering solutions provide a centralized authoring environment for product definition and control of all product data and rules   ·       PLM packages are designed to support multiple perspectives of product data (ordering perspective, billing perspective, provisioning perspective)   ·       Maintains relationships/links between different elements of the entire product definition   ·       Telco PLM packages are specialized in next generation lifecycle management requirements of products such as revision and state management, test and release management, role management and impact analysis)   ·       Takes into consideration all aspects of OSS product requirements compared to CRM product catalogue solutions where the product data managed is mostly order oriented and transactional     ·       New breed of Telco PLM packages are designed with 'open' standards such as SID and eTOM. They are interoperable, support integration frameworks such as subscription and notification.   ·       Telco PLM packages have developed good collaboration frameworks to integrate suppliers and partners into the product development value chain   4.2 Various Architectures/Approaches for Product Mastering using Telco PLM systems   4. 2.a Single Central Product Management (Mastering) Approach   Figure 6: Single Central Product Management (Master) Approach       This approach is implemented across verticals such as aerospace and automotive. It focuses on a physically centralized product master to which other sources are dependent on. The product definition data (Product bundles, service bundles, price plans, offers and discounts, product configuration rules and market campaigns) is created and maintained physically in a centralized environment. In addition, the product definition/authoring environment is centralized. The existing legacy product definition data available in CRM product catalogue, billing catalogue and the legacy product catalogue is migrated to the centralized PLM-based Enterprise Product Management solution.   Architectural changes must be made in the existing business landscape of applications to create and revise data because the applications have to refer to the central repository for approvals and validation of product configurations. It is achieved by modifying how the applications write data or how the applications can be adapted to use the rules to be managed and published.   Complete product configuration validation will be done in enterprise / central product catalogue and final configuration will be sent to the B/OSS system through the SOA compliant product distribution architecture. The approach/architecture enables greater control in terms of product data management and product data governance.   4.2.b Federated Product Management (Mastering) Architecture     Figure 7: Federated Product Management (Mastering) Architecture   In the federated product mastering approach, the basic unique product definition data (product id, description product hierarchy, basic price plans and simple product design rules) will be centrally created and will be maintained. And, the advanced product definition (Product bundling, promotions, offers & discount plans) will be created in respective down stream OSS systems. The advanced product definition (Product bundling, promotions, offers and discount plans) will be created in respective downstream OSS systems.   For example, basic product definitions such as attributes, product hierarchy and basic price plans will be created and maintained in Enterprise/Central product reference catalogue and distributed to downstream OSS systems. Respective downstream OSS systems build product bundles, promotions, advanced price plans over the basic product definition and master the advanced product definition. Central reference database accesses the respective other source product master data and assembles a point-in-time consolidated view of the product. The approach is typically adapted in some merger and acquisition scenarios where there is a low probability of a central physical authority managing the data. In addition, the migration effort in this case is minimal and there are no big architectural changes to the organization application landscape. However, this approach will not result in better product data management and data governance.   5.0 Customer Scenario – Before EPC deployment   A leading global telecommunications service provider wanted to launch a quad play and triple play service offering in the shortest possible lead time. The service provider was offering Broadband and VoIP services to customers. The company wanted to reuse a majority of the Broadband services and price plans and bundle them with new wireless and IPTV services for quad play and triple play. The challenges in launching the new service offerings were:       Figure 8: Triple Play Plan   ·       Broadband product data was stored in multiple product catalogues (CRM catalogue, Billing catalogue, spread sheets)   ·       Product managers spent a lot of time performing tasks involving duplication or re-keying of data. Manual effort caused errors, cost and time over-runs.   ·       No effective product and price data governance mechanism. Price change issues arising from the lack of data consistency across systems resulted in leakage of customer value and revenue.   ·       Product data had re-usability issues and was not in a structured format. It resulted in uncontrolled product portfolio creation and product management issues.   ·       Lack of enterprise product model resulted into product distribution challenges and thus delays in product launch.   ·       Designers are constrained by existing legacy product management solutions to model product/service requirements and product configuration rules such as upgrading, downgrading and cross selling.    5.1 Customer Scenario - After EPC deployment     Figure 9: SOA-based end-to-end EPC Solution   The company deployed PLM-based Enterprise Product Catalogue solutions to launch quad play service after evaluating various product catalogues. The broadband product offering, service and price data were migrated to the new system, and the product and price plan hierarchy for new offerings were created using the entities defined in the Enterprise Product Model. Supplier product catalogue data such as routers and set up boxes were loaded onto the new solution through SOA-based web service. Price plans and configuration rules were built in the new system. The validated final product configurations were extracted from the product catalogue in a SID format and were distributed to the downstream B/OSS systems through exposed SOA-based web services. The transformations required for the B/OSS system were handled using the transformation layer as part of the solution.   6.0 How PLM enabled Product Management Transformation         Figure 10: Product Management Transformation     PLM-based Product Catalogue Solution helped the customer reduce the product launch cycle time by 30% and enable transformation of Product Management for next generation services.   7.0 Conclusion   On the one hand, the telecom industry is undergoing changes due to disruptions, uncertain product markets and increased complexity of products. On the other hand, the ARPU is decreasing year-on-year. Communications Service Providers are embarking on convergence, bundled service offerings, flexibility to cross-sell and up-sell, introduce new value-added services, leverage Web 2.0 concepts and network capabilities. Consequently, large scale IT transformation initiatives to improve their ARPU supporting network and business transformations are a business imperative. Product Management has become a focus area. Companies are investing in best-in- class COTS solutions to reduce time-to-market, ensure rapid service delivery and improve operational efficiency. An efficient PLM-based enterprise product mastering solution plays a key role in achieving zero touch automation and rapid product launch.   References:   1.     Preston G.Smith, Donald G.Reineristsem, Van Nostrand Reinhold “Developing Products in Half the time”.   2.     John G. Innes, "Achieving Successful Product Change", Pitman Publishing.   3.     D T Pham and R M Setchi (16th Jan, 2001) "Authoring environment for documentation development" University of Wales Cardiff, U.K., Proceedings on Institution of Mechanical Engineers, Vol. 215, Part B.   4.     Oracle Product Hub for Communications:   http://www.oracle.com/us/products/applications/master-data-management/product-hub-082059.html  

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  • Testing Entity Framework applications, pt. 3: NDbUnit

    - by Thomas Weller
    This is the third of a three part series that deals with the issue of faking test data in the context of a legacy app that was built with Microsoft's Entity Framework (EF) on top of an MS SQL Server database – a scenario that can be found very often. Please read the first part for a description of the sample application, a discussion of some general aspects of unit testing in a database context, and of some more specific aspects of the here discussed EF/MSSQL combination. Lately, I wondered how you would ‘mock’ the data layer of a legacy application, when this data layer is made up of an MS Entity Framework (EF) model in combination with a MS SQL Server database. Originally, this question came up in the context of how you could enable higher-level integration tests (automated UI tests, to be exact) for a legacy application that uses this EF/MSSQL combo as its data store mechanism – a not so uncommon scenario. The question sparked my interest, and I decided to dive into it somewhat deeper. What I've found out is, in short, that it's not very easy and straightforward to do it – but it can be done. The two strategies that are best suited to fit the bill involve using either the (commercial) Typemock Isolator tool or the (free) NDbUnit framework. The use of Typemock was discussed in the previous post, this post now will present the NDbUnit approach... NDbUnit is an Apache 2.0-licensed open-source project, and like so many other Nxxx tools and frameworks, it is basically a C#/.NET port of the corresponding Java version (DbUnit namely). In short, it helps you in flexibly managing the state of a database in that it lets you easily perform basic operations (like e.g. Insert, Delete, Refresh, DeleteAll)  against your database and, most notably, lets you feed it with data from external xml files. Let's have a look at how things can be done with the help of this framework. Preparing the test data Compared to Typemock, using NDbUnit implies a totally different approach to meet our testing needs.  So the here described testing scenario requires an instance of an SQL Server database in operation, and it also means that the Entity Framework model that sits on top of this database is completely unaffected. First things first: For its interactions with the database, NDbUnit relies on a .NET Dataset xsd file. See Step 1 of their Quick Start Guide for a description of how to create one. With this prerequisite in place then, the test fixture's setup code could look something like this: [TestFixture, TestsOn(typeof(PersonRepository))] [Metadata("NDbUnit Quickstart URL",           "http://code.google.com/p/ndbunit/wiki/QuickStartGuide")] [Description("Uses the NDbUnit library to provide test data to a local database.")] public class PersonRepositoryFixture {     #region Constants     private const string XmlSchema = @"..\..\TestData\School.xsd";     #endregion // Constants     #region Fields     private SchoolEntities _schoolContext;     private PersonRepository _personRepository;     private INDbUnitTest _database;     #endregion // Fields     #region Setup/TearDown     [FixtureSetUp]     public void FixtureSetUp()     {         var connectionString = ConfigurationManager.ConnectionStrings["School_Test"].ConnectionString;         _database = new SqlDbUnitTest(connectionString);         _database.ReadXmlSchema(XmlSchema);         var entityConnectionStringBuilder = new EntityConnectionStringBuilder         {             Metadata = "res://*/School.csdl|res://*/School.ssdl|res://*/School.msl",             Provider = "System.Data.SqlClient",             ProviderConnectionString = connectionString         };         _schoolContext = new SchoolEntities(entityConnectionStringBuilder.ConnectionString);         _personRepository = new PersonRepository(this._schoolContext);     }     [FixtureTearDown]     public void FixtureTearDown()     {         _database.PerformDbOperation(DbOperationFlag.DeleteAll);         _schoolContext.Dispose();     }     ...  As you can see, there is slightly more fixture setup code involved if your tests are using NDbUnit to provide the test data: Because we're dealing with a physical database instance here, we first need to pick up the test-specific connection string from the test assemblies' App.config, then initialize an NDbUnit helper object with this connection along with the provided xsd file, and also set up the SchoolEntities and the PersonRepository instances accordingly. The _database field (an instance of the INdUnitTest interface) will be our single access point to the underlying database: We use it to perform all the required operations against the data store. To have a flexible mechanism to easily insert data into the database, we can write a helper method like this: private void InsertTestData(params string[] dataFileNames) {     _database.PerformDbOperation(DbOperationFlag.DeleteAll);     if (dataFileNames == null)     {         return;     }     try     {         foreach (string fileName in dataFileNames)         {             if (!File.Exists(fileName))             {                 throw new FileNotFoundException(Path.GetFullPath(fileName));             }             _database.ReadXml(fileName);             _database.PerformDbOperation(DbOperationFlag.InsertIdentity);         }     }     catch     {         _database.PerformDbOperation(DbOperationFlag.DeleteAll);         throw;     } } This lets us easily insert test data from xml files, in any number and in a  controlled order (which is important because we eventually must fulfill referential constraints, or we must account for some other stuff that imposes a specific ordering on data insertion). Again, as with Typemock, I won't go into API details here. - Unfortunately, there isn't too much documentation for NDbUnit anyway, other than the already mentioned Quick Start Guide (and the source code itself, of course) - a not so uncommon problem with smaller Open Source Projects. Last not least, we need to provide the required test data in xml form. A snippet for data from the People table might look like this, for example: <?xml version="1.0" encoding="utf-8" ?> <School xmlns="http://tempuri.org/School.xsd">   <Person>     <PersonID>1</PersonID>     <LastName>Abercrombie</LastName>     <FirstName>Kim</FirstName>     <HireDate>1995-03-11T00:00:00</HireDate>   </Person>   <Person>     <PersonID>2</PersonID>     <LastName>Barzdukas</LastName>     <FirstName>Gytis</FirstName>     <EnrollmentDate>2005-09-01T00:00:00</EnrollmentDate>   </Person>   <Person>     ... You can also have data from various tables in one single xml file, if that's appropriate for you (but beware of the already mentioned ordering issues). It's true that your test assembly may end up with dozens of such xml files, each containing quite a big amount of text data. But because the files are of very low complexity, and with the help of a little bit of Copy/Paste and Excel magic, this appears to be well manageable. Executing some basic tests Here are some of the possible tests that can be written with the above preparations in place: private const string People = @"..\..\TestData\School.People.xml"; ... [Test, MultipleAsserts, TestsOn("PersonRepository.GetNameList")] public void GetNameList_ListOrdering_ReturnsTheExpectedFullNames() {     InsertTestData(People);     List<string> names =         _personRepository.GetNameList(NameOrdering.List);     Assert.Count(34, names);     Assert.AreEqual("Abercrombie, Kim", names.First());     Assert.AreEqual("Zheng, Roger", names.Last()); } [Test, MultipleAsserts, TestsOn("PersonRepository.GetNameList")] [DependsOn("RemovePerson_CalledOnce_DecreasesCountByOne")] public void GetNameList_NormalOrdering_ReturnsTheExpectedFullNames() {     InsertTestData(People);     List<string> names =         _personRepository.GetNameList(NameOrdering.Normal);     Assert.Count(34, names);     Assert.AreEqual("Alexandra Walker", names.First());     Assert.AreEqual("Yan Li", names.Last()); } [Test, TestsOn("PersonRepository.AddPerson")] public void AddPerson_CalledOnce_IncreasesCountByOne() {     InsertTestData(People);     int count = _personRepository.Count;     _personRepository.AddPerson(new Person { FirstName = "Thomas", LastName = "Weller" });     Assert.AreEqual(count + 1, _personRepository.Count); } [Test, TestsOn("PersonRepository.RemovePerson")] public void RemovePerson_CalledOnce_DecreasesCountByOne() {     InsertTestData(People);     int count = _personRepository.Count;     _personRepository.RemovePerson(new Person { PersonID = 33 });     Assert.AreEqual(count - 1, _personRepository.Count); } Not much difference here compared to the corresponding Typemock versions, except that we had to do a bit more preparational work (and also it was harder to get the required knowledge). But this picture changes quite dramatically if we look at some more demanding test cases: Ok, and what if things are becoming somewhat more complex? Tests like the above ones represent the 'easy' scenarios. They may account for the biggest portion of real-world use cases of the application, and they are important to make sure that it is generally sound. But usually, all these nasty little bugs originate from the more complex parts of our code, or they occur when something goes wrong. So, for a testing strategy to be of real practical use, it is especially important to see how easy or difficult it is to mimick a scenario which represents a more complex or exceptional case. The following test, for example, deals with the case that there is some sort of invalid input from the caller: [Test, MultipleAsserts, TestsOn("PersonRepository.GetCourseMembers")] [Row(null, typeof(ArgumentNullException))] [Row("", typeof(ArgumentException))] [Row("NotExistingCourse", typeof(ArgumentException))] public void GetCourseMembers_WithGivenVariousInvalidValues_Throws(string courseTitle, Type expectedInnerExceptionType) {     var exception = Assert.Throws<RepositoryException>(() =>                                 _personRepository.GetCourseMembers(courseTitle));     Assert.IsInstanceOfType(expectedInnerExceptionType, exception.InnerException); } Apparently, this test doesn't need an 'Arrange' part at all (see here for the same test with the Typemock tool). It acts just like any other client code, and all the required business logic comes from the database itself. This doesn't always necessarily mean that there is less complexity, but only that the complexity happens in a different part of your test resources (in the xml files namely, where you sometimes have to spend a lot of effort for carefully preparing the required test data). Another example, which relies on an underlying 1-n relationship, might be this: [Test, MultipleAsserts, TestsOn("PersonRepository.GetCourseMembers")] public void GetCourseMembers_WhenGivenAnExistingCourse_ReturnsListOfStudents() {     InsertTestData(People, Course, Department, StudentGrade);     List<Person> persons = _personRepository.GetCourseMembers("Macroeconomics");     Assert.Count(4, persons);     Assert.ForAll(         persons,         @p => new[] { 10, 11, 12, 14 }.Contains(@p.PersonID),         "Person has none of the expected IDs."); } If you compare this test to its corresponding Typemock version, you immediately see that the test itself is much simpler, easier to read, and thus much more intention-revealing. The complexity here lies hidden behind the call to the InsertTestData() helper method and the content of the used xml files with the test data. And also note that you might have to provide additional data which are not even directly relevant to your test, but are required only to fulfill some integrity needs of the underlying database. Conclusion The first thing to notice when comparing the NDbUnit approach to its Typemock counterpart obviously deals with performance: Of course, NDbUnit is much slower than Typemock. Technically,  it doesn't even make sense to compare the two tools. But practically, it may well play a role and could or could not be an issue, depending on how much tests you have of this kind, how often you run them, and what role they play in your development cycle. Also, because the dataset from the required xsd file must fully match the database schema (even in parts that otherwise wouldn't be relevant to you), it can be quite cumbersome to be in a team where different people are working with the database in parallel. My personal experience is – as already said in the first part – that Typemock gives you a better development experience in a 'dynamic' scenario (when you're working in some kind of TDD-style, you're oftentimes executing the tests from your dev box, and your database schema changes frequently), whereas the NDbUnit approach is a good and solid solution in more 'static' development scenarios (when you need to execute the tests less frequently or only on a separate build server, and/or the underlying database schema can be kept relatively stable), for example some variations of higher-level integration or User-Acceptance tests. But in any case, opening Entity Framework based applications for testing requires a fair amount of resources, planning, and preparational work – it's definitely not the kind of stuff that you would call 'easy to test'. Hopefully, future versions of EF will take testing concerns into account. Otherwise, I don't see too much of a future for the framework in the long run, even though it's quite popular at the moment... The sample solution A sample solution (VS 2010) with the code from this article series is available via my Bitbucket account from here (Bitbucket is a hosting site for Mercurial repositories. The repositories may also be accessed with the Git and Subversion SCMs - consult the documentation for details. In addition, it is possible to download the solution simply as a zipped archive – via the 'get source' button on the very right.). The solution contains some more tests against the PersonRepository class, which are not shown here. Also, it contains database scripts to create and fill the School sample database. To compile and run, the solution expects the Gallio/MbUnit framework to be installed (which is free and can be downloaded from here), the NDbUnit framework (which is also free and can be downloaded from here), and the Typemock Isolator tool (a fully functional 30day-trial is available here). Moreover, you will need an instance of the Microsoft SQL Server DBMS, and you will have to adapt the connection strings in the test projects App.config files accordingly.

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  • How to speed up calculation of length of longest common substring?

    - by eSKay
    I have two very large strings and I am trying to find out their Longest Common Substring. One way is using suffix trees (supposed to have a very good complexity, though a complex implementation), and the another is the dynamic programming method (both are mentioned on the Wikipedia page linked above). Using dynamic programming The problem is that the dynamic programming method has a huge running time (complexity is O(n*m), where n and m are lengths of the two strings). What I want to know (before jumping to implement suffix trees): Is it possible to speed up the algorithm if I only want to know the length of the common substring (and not the common substring itself)?

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  • Reading Local Group Policy / Active Directory Settings

    - by Shinobi
    I'm writing a C# program that will enforce password complexity in accordance with the Windows Group Policy setting "Password must meet complexity requirements". Specifically, if that policy is set to Enabled either on the local machine (if it's not part of a domain) or by the Domain Security Policy (for domain members), then my software needs to enforce a complex password for its own internal security. The issue is that I can't figure out how to read that GPO setting. Google searches have indicated that I can read GPO settings with one of these two APIs: the System.DirectoryServices library in .NET Framework, and Windows Management Instrumentation (WMI), but I haven't had any success so far. Any insights would be helpful.

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  • Advice needed: What tech expertise do I need to accomplish my goal?

    - by quaternion
    I have a business plan & money to hire a developer - the essentials involve simple online games for children (played via traditional computer over the internet) such that their scores are uploaded to a database that can be viewed via a nice portal. What I don't know is: what technologies I would want my developer to know (the only somewhat unusual requirement I have is that the games have relatively accurate timing of keypresses/mouse presses) how much I should expect to pay (in general, what is the appropriate range) how many hours a program of the complexity of a simple tower defense -type game should take to develop (to be clear, the idea is not to implement TD, but rather other games of similar complexity) whether I should go with capable acquaintances, some online kind of online web-dev bidding site, or just try to find a capable undergraduate at the local university who I can wow with his/her first big paycheck, how I should communicate the software's specifications to my hired developer... Is there a standard I should be using? whether I should hire a webdev consultant for a few hours to help me answer these questions, instead of asking stackoverflow Thanks for any advice!

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  • R ggplot barplot; Fill based on two separate variables

    - by user1476968
    A picture says more than a thousand words. As you can see, my fill is based on the variable variable. Within each bar there is however multiple data entities (black borders) since the discrete variable complexity make them unique. What I am trying to find is something that makes each section of the bar more distinguishable than the current look. Preferable would be if it was something like shading. Here's an example (not the same dataset, since the original was imported): dat <- read.table(text = "Complexity Method Sens Spec MMC 1 L Alpha 50 20 10 2 M Alpha 40 30 80 3 H Alpha 10 10 5 4 L Beta 70 50 60 5 M Beta 49 10 80 6 H Beta 90 17 48 7 L Gamma 19 5 93 8 M Gamma 18 39 4 9 H Gamma 10 84 74", sep = "", header=T) library(ggplot2) library(reshape) short.m <- melt(dat) ggplot(short.m, aes(x=Method, y= value/100 , fill=variable)) + geom_bar(stat="identity",position="dodge", colour="black") + coord_flip()

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  • Must a Language that Implements Monads be Statically Typed?

    - by Morgan Cheng
    I am learning functional programming style. From this link http://channel9.msdn.com/shows/Going+Deep/Brian-Beckman-Dont-fear-the-Monads/, Brian Beckman gave a brilliant introduction about Monad. He mentioned that Monad is about composition of functions so as to address complexity. A Monad includes a unit function that transfers type T to an amplified type M(T); and a Bind function that, given function from T to M(U), transforms type M(T) to another type M(U). (U can be T, but is not necessarily). In my understanding, the language implementing monad should be type-checked statically. Otherwise, type errors cannot be found during compilation and "Complexity" is not controlled. Is my understanding correct?

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  • MongoDB efficient dealing with embedded documents

    - by Sebastian Nowak
    I have serious trouble finding anything useful in Mongo documentation about dealing with embedded documents. Let's say I have a following schema: { _id: ObjectId, ... data: [ { _childId: ObjectId // let's use custom name so we can distinguish them ... } ] } What's the most efficient way to remove everything inside data for particular _id? What's the most efficient way to remove embedded document with particular _childId inside given _id? What's the performance here, can _childId be indexed in order to achieve logarithmic (or similar) complexity instead of linear lookup? If so, how? What's the most efficient way to insert a lot of (let's say a 1000) documents into data for given _id? And like above, can we get O(n log n) or similar complexity with proper indexing? What's the most efficient way to get the count of documents inside data for given _id?

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  • How to find validity of a string of parentheses, curly brackets and square brackets?

    - by Rajendra
    I recently came in contact with this interesting problem. You are given a string containing just the characters '(', ')', '{', '}', '[' and ']', for example, "[{()}]", you need to write a function which will check validity of such an input string, function may be like this: bool isValid(char* s); these brackets have to close in the correct order, for example "()" and "()[]{}" are all valid but "(]", "([)]" and "{{{{" are not! I came out with following O(n) time and O(n) space complexity solution, which works fine: Maintain a stack of characters. Whenever you find opening braces '(', '{' OR '[' push it on the stack. Whenever you find closing braces ')', '}' OR ']' , check if top of stack is corresponding opening bracket, if yes, then pop the stack, else break the loop and return false. Repeat steps 2 - 3 until end of the string. This works, but can we optimize it for space, may be constant extra space, I understand that time complexity cannot be less than O(n) as we have to look at every character. So my question is can we solve this problem in O(1) space?

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  • Tips for Managing Complex Queries

    There are ways to structure a query that will minimize the complexity while raising confidence in the data returned. This article shares a few techniques that will help simplify the writing of complex queries.

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  • How Google does disaster recovery

    Will you be ready when disaster strikes? It's an uncomfortable question for many IT administrators, because answering it with confidence usually requires boatloads of money, immense complexity, and...

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  • links for 2010-12-16

    - by Bob Rhubart
    Oracle Solaris 11 Express: Network Virtualization and Resource Control | Oracle Clinic XiangBingLiu's detailed overview of Oracle Solaris 11 Express features, including Crossbow. (tags: oracle solaris virtualization crossbow) A New Threat To Web Applications: Connection String Parameter Pollution (CSPP) (The Oracle Global Product Security Blog) "CSPP, if carried out successfully, can be used to steal user identities and hijack web credentials. CSPP is a high risk attack because of the relative ease with which it can be carried out (low access complexity) and the potential results it can have (high impact)." -- Shaomin Wang (tags: oracle otn security cspp)

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