Not referring to the build-in tool "Create Documentation...", are there any best practices and/or examples of how to document an AnyLogic model?
Even though I am slightly unsatisfied with the documentation of java programs, that is my current "inspiration". I try to create flowcharts which describe the model's input, processing, output, and, most important but hard to break down in terms of complexity, the agent interactions.
Curious for any experiences!
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In the context of bounded model checking, one describes the system as a State Transition System and the properties that need to be checked.
When one needs to provide multiple system descriptions and properties to the Model Checker Tool, it can become tedious to write the property by hand. In my case, I use some temporal logic.
How does one automate the process of translating/parsing the system description and deriving verifiable properties from it (ideally, a set of Initial states, Transitions, Set of States).
For example, consider the Microwave Example given here Given such a system description, how can I arrive at the specifications in an efficient manner?
There is no such open source tool that I know of, that can do this. Any approaches in terms of ideas, theories are welcome.
You can't automatically derive LTL formulae from automata as you suggest, because automata are more expressive than LTL formulae.
That leaves you with mainly two options: 1. find a verification tool which accepts specifications that are directly expressed as automata (I'm not sure which ones do, but I suspect it is worth checking SPIN and NuSMV for this feature.), or 2. use a meta-specification language that makes the writing of specifications easier; for example, https://www.isp.uni-luebeck.de/salt (doi: 10.1007/11901433_41) or IEE1850/PSL. While PSL is more a language definition for tool-implementors, SALT already offers a web front-end that translates your input straight into LTL.
(By the way, I find your approach methodologically challenging though: you're not supposed to derive formulae from your model, but from your initial system description as it is this very model which you're going to verify. But I am not a 100% sure, if I understood this point in your question correctly.)
I think properties of a system, e.g. Microwave system, come from technical and common sense expectation and requirements, not the model. E.g. microwave is supposed to cook the food. But it is not supposed to cook with door open. Nevertheless a repository of typical LTL pattern can be useful to define properties. It also lists properties along with more familiar regex and automata properties.
If you certain you still want to translate automata to LTL automatically check
https://mathoverflow.net/questions/96963/translate-a-buchi-automaton-to-ltl
Kansas Specification Property Repository
http://patterns.projects.cs.ksu.edu/documentation/patterns.shtml
I am looking for some tool capable of creating complex process of data manipulation which can be more or less easily modified by people who do not write code.
For example, my task is:
fetch data from sourceA
2.1 if data is full - filter it by condition 45
2.2 if data is not full - fetch additional data from source B
if result passes validation - return 1, otherwise 0
This should be described in some readable manner, best option is if one can modify this process in some UI tool.
What are the requirements?
Each process consists of two parts: steps, and a way to arrange them in a sequence.
(1)
The process in each step should be able to
1. emit commands for fetching some data from data-sources and inserting this into process context
2. filter, enrich, transform datasets obtained
Thus each step of this process should be described with some more or less simple DSL.
(2)
The selection of the step to go, i.e. the consequence of steps should be described by some visual tool, or again, as in (1), with some simple dsl.
Can you advise something for this typical, from my point of view, task?
Meanwhile, here are my own ideas.
First think comes to mind is BPMN combined with Drools.
For steps I may use DRL rules: they can make only basic data manipulation themselves, but I can call Java functions from them if I need something complicated.
For steps consequence I may use standart BPMN diagramm.
Mat be, there is something better?
The combination of BPMN with DMN would allow you indeed to describe with these visual standards, the execution of the process and decision logic to be applied, in order to achieve what in the "For example" paragraph.
In order to make it fully accessible by the business people, the BPMN task for fetching the data or performing any interaction with external system, should be prepared in advance and made available during the composition of the BPMN/DMN diagrams.
Alternatively to BPMN+DMN combination, you can look into Fuse or Fuse Online, it cannot describe all the semantics of the BPMN+DMN combination, but with Fuse Online for instance you can fully visually implement the steps you described in the "For example" paragraph.
The problem: Given a set of hand categorized strings (or a set of ordered vectors of strings) generate a categorize function to categorize more input. In my case, that data (or most of it) is not natural language.
The question: are there any tools out there that will do that? I'm thinking of some kind of reasonably polished, download, install and go kind of things, as opposed to to some library or a brittle academic program.
(Please don't get stuck on details as the real details would restrict answers to less generally useful responses AND are under NDA.)
As an example of what I'm looking at; the input I'm wanting to filter is computer generated status strings pulled from logs. Error messages (as an example) being filtered based on who needs to be informed or what action needs to be taken.
Doing Things Manually
If the error messages are being generated automatically and the list of exceptions behind the messages is not terribly large, you might just want to have a table that directly maps each error message type to the people who need to be notified.
This should make it easy to keep track of exactly who/which-groups will be getting what types of messages and to update the routing of messages should you decide that some of the messages are being misdirected.
Typically, a small fraction of the types of errors make up a large fraction of error reports. For example, Microsoft noticed that 80% of crashes were caused by 20% of the bugs in their software. So, to get something useful, you wouldn't even need to start with a complete table covering every type of error message. Instead, you could start with just a list that maps the most common errors to the right person and routes everything else to a person for manual routing. Each time an error is routed manually, you could then add an entry to the routing table so that errors of that type are handled automatically in the future.
Document Classification
Unless the error messages are being editorialized by people who submit them and you want to use this information when routing them, I wouldn't recommend treating this as a document classification task. However, if this is what you want to do, here's a list of reasonably good packages for document document classification organized by programming language:
Python - To do this using the Python based Natural Language Toolkit (NLTK), see the Document Classification section in the freely available NLTK book.
Ruby - If Ruby is more of your thing, you can use the Classifier gem. Here's sample code that detects whether Family Guy quotes are funny or not-funny.
C# - C# programmers can use nBayes. The project's home page has sample code for a simple spam/not-spam classifier.
Java - Java folks have Classifier4J, Weka, Lucene Mahout, and as adi92 mentioned Mallet.
Learning Rules with Weka - If rules are what you want, Weka might be of particular interest, since it includes a rule set based learner. You'll find a tutorial on using Weka for text categorization here.
Mallet has a bunch of classifiers which you can train and deploy entirely from the commandline
Weka is nice too because it has a huge number of classifiers and preprocessors for you to play with
Have you tried spam or email filters? By using text files that have been marked with appropriate categories, you should be able to categorize further text input. That's what those programs do, anyway, but instead of labeling your outputs a 'spam' and 'not spam', you could do other categories.
You could also try something involving AdaBoost for a more hands-on approach to rolling your own. This library from Google looks promising, but probably doesn't meet your ready-to-deploy requirements.
This question relates to project design. The project takes an electrical system and defines it function programatically. Now that I'm knee-deep in defining the system, I'm incorporating a significant amount of interaction which causes the system to configure itself appropriately. Example: the system opens and closes electrical contactors when certain events occur. Because this system is on an airplane, it relies on air/ground logic and thus incorporates two different behaviors depending on where it is.
I give all of this explanation to demonstrate the level of complexity that this application contains. As I have continued in my design, I have employed the use of if/else constructs as a means of extrapolating the proper configurations in this electrical system. However, the deeper I get into the coding, the more if/else constructs are required. I feel that I have reached a point where I am inefficiently programing this system.
For those who tackled projects like this before, I ask: Am I treading a well-known path (when it comes to defining EVERY possible scenario that could occur) and I should continue to persevere... or can I employ some other strategies to accomplish the task of defining a real-world system's behavior.
At this point, I have little to no experience using delegates, but I wonder if I could utilize some observers or other "cocoa-ey" goodness for checking scenarios in lieu of endless if/else blocks.
Since you are trying to model a real world system, I would suggest creating a concrete object oriented design that well defines the is-a and a has-a relationships and apply good old fashioned object oriented design and apply it into breaking the real world system into a functional decomposition.
I would suggest you look into defining protocols that handle the generic case, and using them on specific cases.
For example, you can have many types of events adhering to an ElectricalEvent protocol and depending on the type you can better decide how an ElectricalContactor discriminates on a GeneralElectricEvent versus a SpecializedElectricEvent using the isKindOfClass selector.
If you can define all the states in advance, you're best of implementing this as a finite state machine. This allows you to define the state-dependent logic clearly in one central place.
There are some implementations that you could look into:
SCM allows you to generate state machine code for Objective-C
OFC implements them as DFSM
Of course you can also roll your own customized implementation if that suits you better.
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Inspired after watching Michael Feather's SCNA talk "Self-Education and the Craftsman", I am interested to hear about practical examples in software development where discrete mathematics have proved helpful.
Discrete math has touched every aspect of software development, as software development is based on computer science at its core.
http://en.wikipedia.org/wiki/Discrete_math
Read that link. You will see that there are numerous practical applications, although this wikipedia entry speaks mainly in theoretical terms.
Techniques I learned in my discrete math course from university helped me quite a bit with the Professor Layton games.
That counts as helpful... right?
There are a lot of real-life examples where map coloring algorithms are helpful, besides just for coloring maps. The question on my final exam had to do with traffic light programming on a six-way intersection.
As San Jacinto indicates, the fundamentals of programming are very much bound up in discrete mathematics. Moreover, 'discrete mathematics' is a very broad term. These things perhaps make it harder to pick out particular examples. I can come up with a handful, but there are many, many others.
Compiler implementation is a good source of examples: obviously there's automata / formal language theory in there; register allocation can be expressed in terms of graph colouring; the classic data flow analyses used in optimizing compilers can be expressed in terms of functions on lattice-like algebraic structures.
A simple example the use of directed graphs is in a build system that takes the dependencies involved in individual tasks by performing a topological sort. I suspect that if you tried to solve this problem without having the concept of a directed graph then you'd probably end up trying to track the dependencies all the way through the build with fiddly book-keeping code (and then finding that your handling of cyclic dependencies was less than elegant).
Clearly most programmers don't write their own optimizing compilers or build systems, so I'll pick an example from my own experience. There is a company that provides road data for satnav systems. They wanted automatic integrity checks on their data, one of which was that the network should all be connected up, i.e. it should be possible to get to anywhere from any starting point. Checking the data by trying to find routes between all pairs of positions would be impractical. However, it is possible to derive a directed graph from the road network data (in such a way as it encodes stuff like turning restrictions, etc) such that the problem is reduced to finding the strongly connected components of the graph - a standard graph-theoretic concept which is solved by an efficient algorithm.
I've been taking a course on software testing, and 3 of the lectures were dedicated to reviewing discrete mathematics, in relation to testing. Thinking about test plans in those terms seems to really help make testing more effective.
Understanding of set theory in particular is especially important for database development.
I'm sure there are numerous other applications, but those are two that come to mind here.
Just example of one of many many...
In build systems it's popular to use topological sorting of jobs to do.
By build system I mean any system where we have to manage jobs with dependency relation.
It can be compiling program, generating document, building building, organizing conference - so there is application in task management tools, collaboration tools etc.
I believe testing itself properly procedes from modus tollens, a concept of propositional logic (and hence discrete math), modus tollens being:
P=>Q. !Q, therefore !P.
If you plug in "If the feature is working properly, the test will pass" for P=>Q, and then take !Q as given ("the test did not pass"), then, if all these statements are factually correct, you have a valid, sound basis for returning the feature for a fix. By contrast, many, maybe most testers operate by the principle:
"If the program is working properly, the test will pass. The test passed, therefore the program is working properly."
This can be written as: P=>Q. Q, therefore P.
But this is the fallacy of "affirming the consequent" and does not show what the tester believes it shows. That is, they mistakenly believe that the feature has been "validated" and can be shipped. When Q is given, P may in fact either be true or it may be untrue for P=>Q, and this can be shown with a truth table.
Modus tollens is core to Karl Popper's notion of science as falsification, and testing should proceed in much the same way. We're attempting to falsify the claim that the feature always works under every explicit and implicit circumstance, rather than attempting to verify that it works in the narrow sense that it can work in some proscribed way.