Already have an account? Log in here. Pidoco is software that lets you quickly create click-through wireframes and fully interactive UX prototypes. Small and large companies in over 50 countries trust us. Please enter your contact information and select the option you are interested in.
One of our representatives will contact you to discuss any questions you may have and confirm your order. Create fully interactive prototypes to simulate what your application will really feel like. Use clicks, touch gestures, device motion, keyboard entries and even location data to trigger highly configurable reactions in your prototypes.
Share prototypes, collect comments and edit screens with others in real-time. Our collaboration features are easy to use and include versioning, comment history and issue tracking to give you all the tools you need. Test drive your prototypes on mobile devices like iOS and Android in real-time.
Pidoco allows you to run your prototypes on mobile devices directly without downloading or installing any app. Simply open the sharing link in your mobile web browser.
You can also add the project to your home screen to view it in full-screen mode. Generate handy specification documents at the click of a button to hand to your development team as a blue print or to clients for sign-off. Create custom building blocks that will make your prototyping super fast and improve design consistency in your projects. Create layers and masters as reusable components, upload your own image files or add screenshots as page backgrounds.
Pidoco is easy to learn and easy to use so you will be productive from minute one. This makes rolling Pidoco out within your organization easy as well. Interphase Transom Mount Transducer Speed and temp only. Interphase Transom Mount Transducer Khz with depth. Asset 1. Color Twinscope Color Twinscope operates like underwater sonic radar, scanning a moving beam over a degree arc either vertically or horizontally ahead of the boat.
T TM Transducer Interphase transom mount transducer with depth, temperature and speed. In summary, based on what I see, I can't find a reason to disagree with the outcome in favour of Oracle. That's not to say that I support it, since the publicly available case details that I can find are fairly sparse. I think the concern among most software developers was that the outcome of the case might set a precedent that would allow copyright or patents to cover parts of interfaces that would cause the above test I proposed to fail.
The outcome of the case rested on the district court's finding that 'the "structure, sequence and organization" of an API was copyrightable. Here are a few quotes from the linked article that are key:.
If true, the use of identical declarations would not be copyrightable. However, except for three of the API packages, Google did not dispute the fact that it could have written its own API packages to access the Java language. It would seem that the district court made the right decision in concluding that the intrinsically unique properties of an interface are not copyrightable, but Google also admitted to copying declarations 'verbatim'.
If 'verbatim' can be take to include literal copy and pasting, that includes non-functional aspects like white space, and spelling mistakes in comments, then I think it would be very reasonable to consider this as copyright infringement. The non-copyrightability of an interface does not need to prevent an individual artistic expression of an interface from being copyrighted.
My knowledge of this case only comes from what I can read online, but it would appear to me that Google created verbatim copies of Java source code which happened to include interfaces. Google themselves appears to have been been of the opinion that their use of Java required licensing, because prior to Google pursued licensing deals with Sun to license the use of Java.
After Sun was acquired by Oracle, the licensing negotiations fell through. The fact that Google was pursuing licensing deals that didn't come to fruition, but continued using 'verbatim' copies of code doesn't seem to help their case. I suspect that Google's lawyers may have known they had a weak case, and so they attempted to use a defense related to the very legitimate claim that interfaces should not be copyrighted, and hoped that the source code representation of an interface, and the more philosophical concept would become conflated, allowing them to win the case.
The reason I think this representation applies so well is because it clearly emphasizes the importance of the boundary of the module, and how it interfaces with the rest of its environment. Furthermore, the interface of the cube above imposes very strong constraints about how the external world can interact with what is inside. You can't go around the interface, so if you want to interact with it, you must do so through the means it exposes to you.
Finally, there is nothing in the cube, but we actually don't care, because it's not what's on the inside that counts sorry cube , it's the interface that is exposed to the world. Another example I really like is that of a cell and its membrane: Features on the cell surface like transport and receptor proteins only permit certain things in the extra-cellular matrix to influence what happens inside the cytoplasm according to very specific rules:.
In the context of this article, I will refer to 'modules' and 'abstractions' as if they were the same concept. The dictionary definition of these words is certainly not the same, and even between programming languages these concepts have different meanings. The key properties I'm interested in are that both of them can be though of as a 'system' as we've been using the term in this article: Abstractions and modules can be thought of as something consisting of an interface, and an implementation.
You could think of an individual function in C as a module, a 'module' in Python, a class or package in Java. Anything that has some kind of externally presented interface, and some 'hidden' implementation.
Note that the 'hiddenness' of the implementation can be imposed by the rules of the language, or even just by convention of the programmer. As far as I can tell, the idea of an abstraction leak can be traced back to an essay by Joel Spolsky. There are a few good examples of specific abstraction leaks in the essay, but I'd like to add one of my own: The concept of a 'map' is very common in programming, and represents a data structure consisting of key and value pairs.
An important constraint that the map guarantees is that all map keys must be unique: Trying to write a new value to a given key either results in an error, or overwriting the previous value for that key.
The result is never to have duplicate keys. An extremely common programer requirement is to want to iterate over all the keys of the map. Since ordering of keys is not necessarily a guarantee maps provide, you might wonder what order the keys will be in when you iterate over them? Well, the ordering is not defined, because the map interface does not provide any guarantee of ordering.
Therefore, any ordering is considered acceptable, but in practice the keys are likely to be sorted in some way. Why would they be sorted? Well, sorting happens to be an efficient way of organizing the data.
It can make things like checking for pre-existing keys easier. Iterating over sorted data can produce very different results than iterating over random data. For example, if you're trying to find the minimum number in a list:. The 'else if' branch will never execute if your data is sorted in ascending order, and even if you do randomised input testing, your program will never uncover the problem with this line.
This is a huge problem, because if you swap the map implementation out for another one that doesn't return sorted keys, then your code is suddenly going to start running the buggy code path. At this point, you've complete forgotten about this code, and it is hidden inside a huge monolithic project.
I'm going to propose my own definition of an abstraction leak for use later in this article:. An abstraction leak exists when it is possible for an implementation to affect the environment in a way that was not agreed upon in the interface. Using this definition, it would seem that nearly every abstraction is leaky, because specifying every environmental effect in the interface is only practical in the most rigorous mathematical systems.
The idea that most abstractions are leaky is not unfounded since that is essentially what Joel Spolsky implies with his 'The Law of Leaky Abstractions':. Well if every abstraction is leaky why bother talking about it?
Problems only occur when a part of the environment begins to rely on one of these unspecified environmental effects that originated from the system in question.
These are the problematic abstraction leaks that everybody talks about. This has far-reaching consequences, not only for casual bugs but also in the security domain. There is one well-known phrase related to the security of physical systems, where unintended effects from the system leak into the environment in a way that compromises its security: A Side-channel attack. Combining this with the claim that all abstractions are leaky would give the following conclusion:. Every physical implementation of a cryptosystem is vulnerable to a side-channel attack.
Given what we've discussed above, it is not unreasonable to extend this idea to include not only physical implementations, but also emulated ones as well. As we saw above, interfaces in C specify things like the return type, and the number of parameters that can be passed into a function.
But what do interfaces in Python specify? Note that I'm using the term 'interface' in a way that is consistent with this article, which is likely more general than any literature you've read before on 'interfaces' in Python.
In Python you don't have to specify the types on the interface to a function. This has the benefit of making the function easier to define and invoke because there is less information to specify, and the disadvantage of less constraints that can be checked ahead of time to detect possible programming errors. I think there is something to be said about comparing and quantifying the different characteristics of an interface in terms of how many ways you can send information through them.
This could be done for a specific interface, but also from the perspective of all interfaces that can be specified in a given programming language. It may also be useful for comparing the safety of specific interfaces within the same language.
Now if you take a look at the types of interfaces we can describe in Haskell Thanks to James Hudon for reviewing this, since I barely know any Haskell :.
For a specific interface in a given language, you can quantify a couple different things:. From the perspective of programming languages you can also make observations about. If you extend the same type of analysis to other interfaces, like for example graphical user interface where you can change directories:. And if you review the same task of changing directories performed on the command line using 'cd':.
For the information sent through GUIs and the command line, there is actually another piece of data that I didn't include in the above tables: The amount of noise in the signal. If you consider how hard it is to exactly repeat a sequence of keyboard strokes key by key versus a sequence of mouse movements pixel by pixel , you'll note that there is always way more error in the data you get from a mouse movement or click versus a keyboard stroke.
GUIs compensate for this by making the semantics they accept more non-specific. Can you imagine if the clickable area on "OK" and "Cancel" buttons was only 1 pixel wide? In addition, this analysis can get even more complex when you consider how the error rates change for differently abled individuals. Now that I've reviewed one possible way to quantifying and comparing interfaces, I'll make a few extrapolations from these examples and my own personal experience:.
I'm going to make a lot of observations based on the analysis in the previous section, so I'll define a couple terms for the sake of clarity:. A Leaky interface exists when the interface is prone to being ignored during any communication between the system and the environment.
An interface is Specific if is has a relatively small number of possible inputs and outputs. For more details on Leaky interfaces, consult the section on abstraction leaks. A good example of what I mean by a Specific interface would be piecewise defined functions, defined only for a very small number of inputs. If you can meaningfully quantify how 'leaky', or 'specific' interfaces are, I think it is worth defining a spectrum where interfaces that are very specific and non-leaky are on one end, and non-specific and leaky interfaces are on the other:.
There are probably reasonable arguments to move any of the items in the spectrum above either more to the right, or the left, but you get the idea. Note that you could probably split this up into two spectrums: One for how much the interfaces allow for 'leaky' abstractions, and one for how specific the interfaces are, although in general these two concepts seem to be correlated.
0コメント