by Teknita Team | Jan 6, 2023 | Uncategorized
Test Driven Development (TDD) refers to a style of programming in which three activities are tightly interwoven: coding, testing (in the form of writing unit tests) and design (in the form of refactoring). Test cases are developed to specify and validate what the code will do. In simple terms, test cases for each functionality are created and tested first and if the test fails then the new code is written in order to pass the test and making code simple and bug-free.
Test-Driven Development starts with designing and developing tests for every small functionality of an application. TDD framework instructs developers to write new code only if an automated test has failed. This avoids duplication of code. The TDD full form is Test-driven development.
The simple concept of TDD is to write and correct the failed tests before writing new code (before development). This helps to avoid duplication of code as we write a small amount of code at a time in order to pass tests. (Tests are nothing but requirement conditions that we need to test to fulfill them).
Test-Driven development is a process of developing and running automated test before actual development of the application. Hence, TDD sometimes also called as Test First Development.
Following steps define how to perform TDD test,
- Add a test.
- Run all tests and see if any new test fails.
- Write some code.
- Run tests and Refactor code.
- Repeat.
The greatest benefit of Test Driven Development is the detection of errors at the early stage of software development. The developer can fix the invalid code immediately by himself. Reducing the time between the introduction of a bug and its detection means fewer people involved in fixing bugs and cheaper and faster process. To sum up, TDD reduces the cost of creating new software which appears much faster, and the code quality is higher than with classic programming methods.
On the minus side of TDD, there is the difficulty in determining the length of cycles and the number of necessary tests. It’s also hard to keep a balance between writing the code and creating further detailed tests. A large number of small and simple tests is not bad in general, but if performed improperly, it may cause slowing down the execution of the entire task.
You can read more about TDD here.
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by Teknita Team | Dec 28, 2022 | Uncategorized
XML Query, XQuery for short, is a new query language currently under development by the W3C. It is designed to query XML documents using a SQL-like syntax. XQuery’s capabilities go far beyond SQL however, because XML (and thus XQuery) isn’t bound to the rigid structure of tables and relations. XML can represent a large number of data models. Furthermore an XQuery query can return data from multiple documents in different locations. XSLT has similar capabilities, but many IT people will find XQuery much easier to understand, particularly database administrators familiar with SQL.
You can use XQuery to extract an XML document from a physical or virtual representation of XML data. An example of the latter is SQLXML (provided in Microsoft SQL Server 2000), which enables you to extract data from a SQL Server database formatted as XML using the HTTP protocol. Any system that exposes XML over HTTP is a potential source of data for XQuery. XQuery’s designers hope that XQuery can act as a unified query language for any data store, including XML files, XML databases, and non-XML data stores. With the proliferation of loosely coupled systems and data coming from half way across the globe, performance of multi-document queries is going to be an issue, particularly if you only need a small amount of data from a large document. Future versions of XQuery may alleviate this problem by distributing a query over the queried systems.
XQuery uses four main keywords to create query expressions: FOR, LET, WHERE, and RETURN. These keywords are commonly used in conjunction to query data and create a result. People familiar with XQuery who build an expression using these keywords refer to this as a FLWR-expression (or FLoWeR-expression). In technical terms, these expressions are element constructors – you use them to construct (sequences of) elements.
There are several applications providing the ability to query using XQuery. Microsoft has already hinted that the next release of SQL Server (codename Yukon) will provide support for XQuery as well, and both IBM and Oracle will likely offer some kind of XQuery support once XQuery attains W3C Recommendation status.
You can read more about XQuery here.
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by Teknita Team | Dec 27, 2022 | Uncategorized
Git is a version control tool that helps a developer to track what all changes that he/she has done in his code. Git’s user interface is fairly similar to these other VCSs, but Git stores and thinks about information in a very different way. Git thinks of its data more like a series of snapshots of a miniature filesystem. With Git, every time you commit, or save the state of your project, Git basically takes a picture of what all your files look like at that moment and stores a reference to that snapshot. GIT allows you to analyze all code changes with great accuracy. If necessary, you can also use a very important function that allows you to restore the selected version of the file. This is especially useful when developer made a mistake that caused the software to stop working properly.
Most operations in Git need only local files and resources to operate — generally no information is needed from another computer on your network. If you’re used to a CVCS where most operations have that network latency overhead, this aspect of Git will make you think that the gods of speed have blessed Git with unworldly powers. Because you have the entire history of the project right there on your local disk, most operations seem almost instantaneous.
Everything in Git is checksummed before it is stored and is then referred to by that checksum. This means it’s impossible to change the contents of any file or directory without Git knowing about it. This functionality is built into Git at the lowest levels and is integral to its philosophy. You can’t lose information in transit or get file corruption without Git being able to detect it.
When you do actions in Git, nearly all of them only add data to the Git database. It is hard to get the system to do anything that is not undoable or to make it erase data in any way. As with any VCS, you can lose or mess up changes you haven’t committed yet, but after you commit a snapshot into Git, it is very difficult to lose, especially if you regularly push your database to another repository. Thanks to the fact that previous versions of the code are saved, programmers do not have to worry about “breaking something” – they can experiment with the code and test different solutions.
The GIT software also has some very useful advantage – allow you to work in teams, what is very often in the IT industry. Thanks to GIT, every team member has access to exactly the same, up-to-date version, and the risk of errors is decreased to an absolute minimum.
You can read more about GIT here.
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by Teknita Team | Dec 23, 2022 | Artificial Intelligence - Machine Learning, Uncategorized
Artificial neural networks and related deep learning are conquering other areas of the industry.
It underpins most deep learning models. As a result, deep learning may sometimes be referred to as deep neural learning or deep neural networking. The use of networks built of artificial neurons allows to create software that imitates the work of the human brain, which translates into an increase in the efficiency of business processes and companies.
The Neural Network is constructed from 3 type of layers:
- Input layer — initial data for the neural network.
- Hidden layers — intermediate layer between input and output layer and place where all the computation is done.
- Output layer — produce the result for given inputs.
The input layer is used to retrieve data and pass it on to the first hidden layer.
In hidden layers, calculations are performed, as well as the learning process itself.
The output layer calculates the output values obtained from the entire network, and then sends the obtained results to the outside.
Each node has a weight and a threshold – when the threshold value exceeds the allowable value, it activates and sends data to the next layer. Neural networks need training data from which they learn to function properly. As they receive more data, they can improve their performance.
Neural networks come in several different forms, including recurrent neural networks, convolutional neural networks, artificial neural networks and feedforward neural networks, and each has benefits for specific use cases. However, they all function in somewhat similar ways — by feeding data in and letting the model figure out for itself whether it has made the right interpretation or decision about a given data element.
Neural networks involve a trial-and-error process, so they need massive amounts of data on which to train. It’s no coincidence neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data. Because the model’s first few iterations involve somewhat educated guesses on the contents of an image or parts of speech, the data used during the training stage must be labeled so the model can see if its guess was accurate. This means, though many enterprises that use big data have large amounts of data, unstructured data is less helpful. Unstructured data can only be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can’t train on unstructured data.
Deep learning will be developed, and deep neural networks will find application in completely new areas. It is already predicted that they can be used in driving autonomous cars or in the entertainment sector to analyze the behavior of users of a streaming service, or add sound to silent movies.
You can read more about Artificial Neural Network here.
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by Teknita Team | Dec 22, 2022 | Artificial Intelligence - Machine Learning
Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier. At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction.
Computer programs that use deep learning go through much the same process as the toddler learning to identify things around him. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.
Unlike the toddler, who will take weeks or even months to understand the concept of eg. bed, a computer program that uses deep learning algorithms can be shown a training set and sort through millions of images, accurately identifying which images have beds in them within a few minutes.
To achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing. Because deep learning programming can create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the internet of things (IoT) continues to become more pervasive because most of the data humans and machines create is unstructured and is not labeled.
Deep learning examples
Because deep learning models process information in ways similar to the human brain, they can be applied to many tasks people do. Deep learning is currently used in most common image recognition tools, natural language processing (NLP) and speech recognition software. These tools are starting to appear in applications as diverse as self-driving cars and language translation services.
Use cases today for deep learning include all types of big data analytics applications, especially those focused on NLP, language translation, medical diagnosis, stock market trading signals, network security and image recognition.
Specific fields in which deep learning is currently being used include the following:
- Customer experience (CX). Deep learning models are already being used for chatbots. And, as it continues to mature, deep learning is expected to be implemented in various businesses to improve CX and increase customer satisfaction.
- Text generation. Machines are being taught the grammar and style of a piece of text and are then using this model to automatically create a completely new text matching the proper spelling, grammar and style of the original text.
- Aerospace and military. Deep learning is being used to detect objects from satellites that identify areas of interest, as well as safe or unsafe zones for troops.
Industrial automation. Deep learning is improving worker safety in environments like factories and warehouses by providing services that automatically detect when a worker or object is getting too close to a machine.
- Adding color. Color can be added to black-and-white photos and videos using deep learning models. In the past, this was an extremely time-consuming, manual process.
- Medical research. Cancer researchers have started implementing deep learning into their practice as a way to automatically detect cancer cells.
- Computer vision. Deep learning has greatly enhanced computer vision, providing computers with extreme accuracy for object detection and image classification, restoration and segmentation.
You can read more about Deep Learning here.
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