What is the Best Test Automation Approach for WatiN - nunit

I Studied both data-driven and keyword driven approaches. After reading, It seems data driven is better than keyword. For documentation purpose keyword sounds great. But it has many levels. I need guidance from people who actually have implemented Automation frameworks. Personally, I want to store all data in database or excel and break up the system into modular parts (functions that are common to major company products).
Currently using, WatiN, Nunit, CC.net
Any advise pls


I would hightly recommend that you look into the stack that Michael Hunter aka the braidy tester built for testing expression at Microsoft he has a lot of articles about it http://www.thebraidytester.com/stack.html
Esentially he splits out into a logical model, a physical model and a data model and all three are loosley copupled. All my stacks are written this way now. So the test cases end up looking like this:
Logical.Google.Search.Websearch("watin");
Verification.VerifySearchResult("watin");
All the test data is then stored in a sql express database that indexed by the text string, in this case watin.
You will need to build a full domain model and data access layer, I personally auto generate that using SubSonic.

Related

What is currently the best way to add a custom dictionary to a neural machine translator that uses the transformer architecture?

It's common to add a custom dictionary to a machine translator to ensure that terminology from a specific domain is correctly translated. For example, the term server should be translated differently when the document is about data centers, vs when the document is about restaurants.
With a transformer model, this is not very obvious to do, since words are not aligned 1:1. I've seen a couple of papers on this topic, but I'm not sure which would be the best one to use. What are the best practices for this problem?

I am afraid you cannot easily do that. You cannot easily add new words to the vocabulary because you don't know what embedding it would get during training. You can try to remove some words, or alternatively you can manually change the bias in the final softmax layer to prevent some words from appearing in the translation. Anything else would be pretty difficult to do.
What you want to do is called domain adaptation. To get an idea of how domain adaptation is usually done, you can have a look at a survey paper.
The most commonly used approaches are probably model finetuning or ensembling with a language model. If you want to have parallel data in your domain, you can try to fine-tune your model on that parallel data (with simple SGD, small learning rate).
If you only have monolingual data in the target language, you train a language model on that data. During the decoding, you can mix the probabilities from the domain-specific language and the translation model. Unfortunately, I don't know of any tool that could do this out of the box.

Deploy Knowledge Studio dictionary pre-annotator to Natural Language Understanding

I'm getting started with Knowledge Studio and Natural Language Understanding.
I'm able to deploy a machine-learning model toNatural Language Understanding and use the API to query it.
I would know if there's a way to deploy only the pre-annotator.
I read from Knowledge Studio's documentation that
You can deploy or export a machine-learning annotator. A dictionary pre-annotator can only be used to pre-annotate documents within Watson Knowledge Studio.
Does exist a workaround to create a model that simply does the job of the pre-annotator, i.e. use dictionaries to find entities instead of the machine-learning model?

Does exist a workaround to create a model that simply does the job of the pre-annotator, i.e. use dictionaries to find entities instead of the machine-learning model?
You may need to explain this better in what you need.
WKS allows you to pre-annotate documents with dictionaries you upload. Once you have created a ML model, you can alternatively use that to annotate your training documents, and then manually correct. As you continue the amount of manual work will reduce after each model iteration.
The assumption is that you are creating a model with a reasonable amount of examples. In your model results, you will want the mention/relations to be outside or close to outside the gray area of the report.
The other interpretation of your request I took was you want to create a dictionary based model only. This is possible using the "Rule-Based Model" functionality. You would have to create the parsing rules but you just map what you want to find to the dictionary/rule.
Using this in production though is still limited. You should get a warning when you deploy these kinds of models.
It's slightly better than just a keyword search as you can map items to parts of speech.
The last point. The purpose of WKS is to create a machine learning model which will do the work in discovering new terms you haven't seen before. With the rule based engine it can only find what you explicitly tell it to find.
If all you want is just dictionary entries, then you can create a very simple string comparison solution, but you lose the linguistic features.

How can I build a generic dataset-handling Perl library?

I want to build a generic Perl module for handling and analysing biomedical character separated datasets and which can, most certain, be used on any kind of datasets that contain a mixture of categorical (A,B,C,..) and continuous (1.2,3,881..) and identifier (XXX1,XXX2...). The plan is to have people initialize the module and then use some arguments to point to the data file(s), the place were the analysis reports should be placed and the structure of the data.
By structure of data I mean which variable is in which place and its name/type. And this is where I need some enlightenment. I am baffled how to do this in a clean way. Obviously, having people create a simple schema file, be it XML or some other format would be the cleanest but maybe not all people enjoy doing something like this.
The solutions I can think of are:
Create a configuration file in XML or similar and with a prespecified format.
Pass the information during initialization of the module.
Use the first row of the data as headers and try to guess types (ouch)
Surely there must be a "canonical" way of doing this that is also usable and efficient.

This doesn't answer your question directly, but have you checked CPAN? It might have the module you need already. If not, it might have similar modules -- related either to biomedical data or simply to delimited data handling -- that you can mine for good ideas, both concerning formats for metadata and your module's API.

Any of the approaches you've listed could make sense. It all depends on how complex the data structures and their definitions are. What will make something like this useful to people is whether it saves them time and effort. So, your decision will have to be answered based on what approach will best satisfy the need to make:
use of the module easy
reuse of data definitions easy
the data definition language sufficiently expressive to describe all known use cases
the data definition language sufficiently simple that an infrequent user can spend minimal time with the docs before getting real work done.
For example, if I just need to enter the names of the columns and their types (and there are only 4 well defined types), doing this each time in a script isn't too bad. Unless I have 350 columns to deal with in every file.
However, if large, complicated structure definitions are common, then a more modular reuse oriented approach is better.
If your data description language is difficult to work with, you can mitigate the issue a bit by providing a configuration tool that allows one to create and edit data schemes.

rx might be worth looking at, as well as the Data::Rx module on the CPAN. It provides schema checking for JSON, but there is nothing inherent in the model that makes it JSON-only.

How do I adapt my recommendation engine to cold starts?

I am curious what are the methods / approaches to overcome the "cold start" problem where when a new user or an item enters the system, due to lack of info about this new entity, making recommendation is a problem.
I can think of doing some prediction based recommendation (like gender, nationality and so on).

You can cold start a recommendation system.
There are two type of recommendation systems; collaborative filtering and content-based. Content based systems use meta data about the things you are recommending. The question is then what meta data is important? The second approach is collaborative filtering which doesn't care about the meta data, it just uses what people did or said about an item to make a recommendation. With collaborative filtering you don't have to worry about what terms in the meta data are important. In fact you don't need any meta data to make the recommendation. The problem with collaborative filtering is that you need data. Before you have enough data you can use content-based recommendations. You can provide recommendations that are based on both methods, and at the beginning have 100% content-based, then as you get more data start to mix in collaborative filtering based.
That is the method I have used in the past.
Another common technique is to treat the content-based portion as a simple search problem. You just put in meta data as the text or body of your document then index your documents. You can do this with Lucene & Solr without writing any code.
If you want to know how basic collaborative filtering works, check out Chapter 2 of "Programming Collective Intelligence" by Toby Segaran

Maybe there are times you just shouldn't make a recommendation? "Insufficient data" should qualify as one of those times.
I just don't see how prediction recommendations based on "gender, nationality and so on" will amount to more than stereotyping.
IIRC, places such as Amazon built up their databases for a while before rolling out recommendations. It's not the kind of thing you want to get wrong; there are lots of stories out there about inappropriate recommendations based on insufficient data.

Working on this problem myself, but this paper from microsoft on Boltzmann machines looks worthwhile: http://research.microsoft.com/pubs/81783/gunawardana09__unified_approac_build_hybrid_recom_system.pdf

This has been asked several times before (naturally, I cannot find those questions now :/, but the general conclusion was it's better to avoid such recommendations. In various parts of the worls same names belong to different sexes, and so on ...

Recommendations based on "similar users liked..." clearly must wait. You can give out coupons or other incentives to survey respondents if you are absolutely committed to doing predictions based on user similarity.
There are two other ways to cold-start a recommendation engine.
Build a model yourself.
Get your suppliers to fill in key information to a skeleton model. (Also may require $ incentives.)
Lots of potential pitfalls in all of these, which are too common sense to mention.
As you might expect, there is no free lunch here. But think about it this way: recommendation engines are not a business plan. They merely enhance the business plan.

There are three things needed to address the Cold-Start Problem:
The data must have been profiled such that you have many different features (with product data the term used for 'feature' is often 'classification facets'). If you don't properly profile data as it comes in the door, your recommendation engine will stay 'cold' as it has nothing with which to classify recommendations.
MOST IMPORTANT: You need a user-feedback loop with which users can review the recommendations the personalization engine's suggestions. For example, Yes/No button for 'Was This Suggestion Helpful?' should queue a review of participants in one training dataset (i.e. the 'Recommend' training dataset) to another training dataset (i.e. DO NOT Recommend training dataset).
The model used for (Recommend/DO NOT Recommend) suggestions should never be considered to be a one-size-fits-all recommendation. In addition to classifying the product or service to suggest to a customer, how the firm classifies each specific customer matters too. If functioning properly, one should expect that customers with different features will get different suggestions for (Recommend/DO NOT Recommend) in a given situation. That would the 'personalization' part of personalization engines.

Do you create your own code generators?

The Pragmatic Programmer advocates the use of code generators.
Do you create code generators on your projects? If yes, what do you use them for?

In "Pragmatic Programmer" Hunt and Thomas distinguish between Passive and Active code generators.
Passive generators are run-once, after which you edit the result.
Active generators are run as often as desired, and you should never edit the result because it will be replaced.
IMO, the latter are much more valuable because they approach the DRY (don't-repeat-yourself) principle.
If the input information to your program can be split into two parts, the part that changes seldom (A) (like metadata or a DSL), and the part that is different each time the program is run (B)(the live input), you can write a generator program that takes only A as input, and writes out an ad-hoc program that only takes B as input.
(Another name for this is partial evaluation.)
The generator program is simpler because it only has to wade through input A, not A and B. Also, it does not have to be fast because it is not run often, and it doesn't have to care about memory leaks.
The ad-hoc program is faster because it's not having to wade through input that is almost always the same (A). It is simpler because it only has to make decisions about input B, not A and B.
It's a good idea for the generated ad-hoc program to be quite readable, so you can more easily find any errors in it. Once you get the errors removed from the generator, they are gone forever.
In one project I worked on, a team designed a complex database application with a design spec two inches thick and a lengthy implementation schedule, fraught with concerns about performance. By writing a code generator, two people did the job in three months, and the source code listings (in C) were about a half-inch thick, and the generated code was so fast as to not be an issue. The ad-hoc program was regenerated weekly, at trivial cost.
So active code generation, when you can use it, is a win-win. And, I think it's no accident that this is exactly what compilers do.

Code generators if used widely without correct argumentation make code less understandable and decrease maintainability (the same with dynamic SQL by the way). Personally I'm using it with some of ORM tools, because their usage here mostly obvious and sometimes for things like searcher-parser algorithms and grammatic analyzers which are not designed to be maintained "by hands" lately. Cheers.

In hardware design, it's fairly common practice to do this at several levels of the 'stack'. For instance, I wrote a code generator to emit Verilog for various widths, topologies, and structures of DMA engines and crossbar switches, because the constructs needed to express this parameterization weren't yet mature in the synthesis and simulation tool flows.
It's also routine to emit logical models all the way down to layout data for very regular things that can be expressed and generated algorithmically, like SRAM, cache, and register file structures.
I also spent a fair bit of time writing, essentially, a code generator that would take an XML description of all the registers on a System-on-Chip, and emit HTML (yes, yes, I know about XSLT, I just found emitting it programatically to be more time-effective), Verilog, SystemVerilog, C, Assembly etc. "views" of that data for different teams (front-end and back-end ASIC design, firmware, documentation, etc.) to use (and keep them consistent by virtue of this single XML "codebase"). Does that count?
People also like to write code generators for e.g. taking terse descriptions of very common things, like finite state machines, and mechanically outputting more verbose imperative language code to implement them efficiently (e.g. transition tables and traversal code).

We use code generators for generating data entity classes, database objects (like triggers, stored procs), service proxies etc. Anywhere you see lot of repititive code following a pattern and lot of manual work involved, code generators can help. But, you should not use it too much to the extend that maintainability is a pain. Some issues also arise if you want to regenerate them.
Tools like Visual Studio, Codesmith have their own templates for most of the common tasks and make this process easier. But, it is easy to roll out on your own.

It is often useful to create a code generator that generates code from a specification - usually one that has regular tabular rules. It reduces the chance of introducing an error via a typo or omission.

Yes ,
I developed my own code generator for AAA protocol Diameter (RFC 3588).
It could generate structures and Api's for diameter messages reading from an XML file that described diameter application's grammar.
That greatly reduced the time to develop complete diameter interface (such as SH/CX/RO etc.).

in my opinion a good programming language would not need code generators because introspection and runtime code generation would be part of language e.g. in python metaclasses and new module etc.

code generators usually generate more unmanageable code in long term usage.
however, if it is absolutely imperative to use a code generator (eclipse VE for swing development is what I use at times) then make sure you know what code is being generated. Believe me, you wouldn't want code in your application that you are not familiar with.

Writing own generator for project is not efficient. Instead, use a generator such as T4, CodeSmith and Zontroy.
T4 is more complex and you need to know a .Net programming language. You have to write your template line by line and you have to complete data relational operations on your own. You can use it over Visual Studio.
CodeSmith is an functional tool and there are plenty of templates ready to use. It is based on T4 and writing your own temlate takes too much time as it is in T4. There is a trial and a commercial version.
Zontroy is a new tool with a user friendly user interface. It has its own template language and is easy to learn. There is an online template market and it is developing. Even you can deliver templates and sell them online over market.
It has a free and a commercial version. Even the free version is enough to complete a medium-scale project.

there might be a lot of code generators out there , however I always create my own to make the code more understandable and suit the frameworks and guidelines we are using

We use a generator for all new code to help ensure that coding standards are followed.
We recently replaced our in-house C++ generator with CodeSmith. We still have to create the templates for the tool, but it seems ideal to not have to maintain the tool ourselves.

My most recent need for a generator was a project that read data from hardware and ultimately posted it to a 'dashboard' UI. In-between were models, properties, presenters, events, interfaces, flags, etc. for several data points. I worked up the framework for a couple data points until I was satisfied that I could live with the design. Then, with the help of some carefully placed comments, I put the "generation" in a visual studio macro, tweaked and cleaned the macro, added the datapoints to a function in the macro to call the generation - and saved several tedious hours (days?) in the end.
Don't underestimate the power of macros :)
I am also now trying to get my head around CodeRush customization capabilities to help me with some more local generation requirements. There is powerful stuff in there if you need on-the-fly decision making when generating a code block.

I have my own code generator that I run against SQL tables. It generates the SQL procedures to access the data, the data access layer and the business logic. It has done wonders in standardising my code and naming conventions. Because it expects certain fields in the database tables (such as an id column and updated datetime column) it has also helped standardise my data design.

How many are you looking for? I've created two major ones and numerous minor ones. The first of the major ones allowed me to generate programs 1500 line programs (give or take) that had a strong family resemblance but were attuned to the different tables in a database - and to do that fast, and reliably.
The downside of a code generator is that if there's a bug in the code generated (because the template contains a bug), then there's a lot of fixing to do.
However, for languages or systems where there is a lot of near-repetitious coding to be done, a good (enough) code generator is a boon (and more of a boon than a 'doggle').

In embedded systems, sometimes you need a big block of binary data in the flash. For example, I have one that takes a text file containing bitmap font glyphs and turns it into a .cc/.h file pair declaring interesting constants (such as first character, last character, character width and height) and then the actual data as a large static const uint8_t[].
Trying to do such a thing in C++ itself, so the font data would auto-generate on compilation without a first pass, would be a pain and most likely illegible. Writing a .o file by hand is out of the question. So is breaking out graph paper, hand encoding to binary, and typing all that in.
IMHO, this kind of thing is what code generators are for. Never forget that the computer works for you, not the other way around.
BTW, if you use a generator, always always always include some lines such as this at both the start and end of each generated file:
// This code was automatically generated from Font_foo.txt. DO NOT EDIT THIS FILE.
// If there's a bug, fix the font text file or the generator program, not this file.

Yes I've had to maintain a few. CORBA or some other object communication style of interface is probably the general thing that I think of first. You have object definitions that are provided to you by the interface you are going to talk over but you still have to build those objects up in code. Building and running a code generator is a fairly routine way of doing that. This can become a fairly lengthy compile just to support some legacy communication channel, and since there is a large tendency to put wrappers around CORBA to make it simpler, well things just get worse.
In general if you have a large amount of structures, or just rapidly changing structures that you need to use, but you can't handle the performance hit of building objects through metadata, then your into writing a code generator.

I can't think of any projects where we needed to create our own code generators from scratch but there are several where we used preexisting generators. (I have used both Antlr and the Eclipse Modeling Framework for building parsers and models in java for enterprise software.) The beauty of using a code generator that someone else has written is that the authors tend to be experts in that area and have solved problems that I didn't even know existed yet. This saves me time and frustration.
So even though I might be able to write code that solves the problem at hand, I can generate the code a lot faster and there is a good chance that it will be less buggy than anything I write.

If you're not going to write the code, are you going to be comfortable with someone else's generated code?
Is it cheaper in both time and $$$ in the long run to write your own code or code generator?
I wrote a code generator that would build 100's of classes (java) that would output XML data from database in a DTD or schema compliant manner. The code generation was generally a one time thing and the code would then be smartened up with various business rules etc. The output was for a rather pedantic bank.

Code generators are work-around for programming language limitations. I personally prefer reflection instead of code generators but I agree that code generators are more flexible and resulting code obviously faster during runtime. I hope, future versions of C# will include some kind of DSL environment.

The only code generators that I use are webservice parsers. I personally stay away from code generators because of the maintenance problems for new employees or a separate team after hand off.

I write my own code generators, mainly in T-SQL, which are called during the build process.
Based on meta-model data, they generate triggers, logging, C# const declarations, INSERT/UPDATE statements, data model information to check whether the app is running on the expected database schema.
I still need to write a forms generator for increased productivity, more specs and less coding ;)

I've created a few code generators. I had a passive code generator for SQL Stored procedures which used templates. This generated generated 90% of our stored procedures.
Since we made the switch to Entity Framework I've created an active codegenerator using T4 (Text Template Transformation Toolkit) inside visual studio. I've used it to create basic repository partial classes for our entities. Works very nicely and saves a bunch of coding. I also use T4 for decorating the entity classes with certain Attributes.

I use code generation features provided by EMF - Eclipse Modeling Framework.

Code generators are really useful in many cases, especially when mapping from one format to another. I've done code generators for IDL to C++, database tables to OO types, and marshalling code just to name a few.
I think the point the authors are trying to make is that if you're a developer you should be able to make the computer work for you. Generating code is just one obvious task to automate.
I once worked with a guy who insisted that he would do our IDL to C++ mapping manually. In the beginning of the project he was able to keep up, because the rest of us were trying to figure out what to do, but eventually he became a bottleneck. I did a code generator in Perl and then we could pretty much do his "work" in a few minutes.

See our "universal" code generator based on program transformations.
I'm the architect and a key implementer.
It is worth noting that a significant fraction of this generator, is generated using this generator.

We uses Telosys code generator in our projects : http://www.telosys.org/
We have created it to reduce the development duration in recurrent tasks like CRUD screens, documentation, etc...
For us the most important thing is to be able to customize the generator's templates, in order to create new generation targets if necessary and to customize existing templates. That's why we have also created a template editor (for Velocity .vm files).
It works fine for Java/Spring/AngularJS code generator and can be adapt for other targets (PHP, C#, Python, etc )