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What Is Functional Testing?

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Functional Testing is a crucial software testing process used within software development to ensure that the software conforms to all the requirements. The primary purpose of functional testing is to verify that the software has all the necessary functionality specified within its functional requirements. This testing process helps evaluate and compare each software function with the business requirements, ensuring that the software outputs align with the end-user or business's expectations. Functional Testing involves providing the software with related input and evaluating the output to see how it conforms, relates or varies compared to its base requirements. The testing process not only checks the software for its functionality but also for its usability. This means the navigational functions are tested to ensure they work as required. Various functional testing techniques are used in software testing. These techniques include smoke testing, white box testing, black box testing, unit testing and user acceptance testing. Smoke testing is a technique that is used to test whether the software is stable enough to undergo more rigorous testing. White box testing is a technique that involves testing the internal workings of the software to ensure that it is functioning as intended. Black box testing is a technique that involves testing the software's functionality from an external perspective without knowledge of its internal workings. Unit testing is a technique that involves testing individual units or components of the software to ensure that they are functioning as intended. Lastly, user acceptance testing involves testing the software with the end user to ensure it meets their expectations and requirements. All these techniques play a vital role in ensuring the software functions correctly and meets the requirements. In conclusion, functional testing is an essential process in software development that helps ensure the software functions correctly and meets the necessary requirements. The testing process enables developers to evaluate software functions and compare them with the business's needs, ensuring that the software outputs align with the end-user or business's expectations. The testing process also checks the software's usability, such as navigational functions, to ensure they work as required. With various functional testing techniques available, developers can choose the best technique for their software testing needs to ensure the software functions correctly.

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Related Terms by Software Development

Sentiment Analysis

Sentiment analysis is a lot like having the ability to discern minds, except it's done with computers. Opinion mining is a data mining subfield that utilizes unstructured text analysis to gauge consumer sentiment toward a brand, individual, or concept. Sentiment analysis is a technique for gleaning emotional data from online sources using NLP, computational linguistics, and text analysis. Social media sites and other online forums where users post their thoughts and observations on various subjects are familiar places to find this data. Sentiment analysis uses complex algorithms and machine learning methods to identify a person's opinion's positive, negative, or neutral nature. As a bonus, it can determine whether the text is joyful, sad, angry, or anxious, as well as other emotions. The results of this analysis can be used to calculate the extent to which the public approves or disapproves of various brands, individuals, and concepts. Knowing the thoughts and preferences of customers can be invaluable to companies and organizations. A business may employ mood analysis to monitor customer feedback via social media and use the results to improve its offerings. The material's polarity in its context can also be revealed through sentiment analysis. It can tell you how people feel about a subject or entity and what it is about that subject or entity that people like or dislike. Sentiment analysis can show, for instance, that consumers have a generally positive attitude toward a given brand but a negative attitude toward its customer service. To sum up, sentiment analysis is a subfield of data mining that assesses consumer reaction to a brand, individual, or concept by examining written language. It's like having the ability to read thoughts, only this time, and it's accomplished through complex mathematical formulas stored in a computer. Sentiment analysis, or opinion mining, is a method for gleaning and analyzing biased data from online sources, such as social media and blogs. Data analysis can reveal the contextual polarity of information and provide quantitative estimates of the public's feelings or responses to specific goods, people, or ideas.

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Self-Provisioning

If you're like most people, you're always looking for ways to get out of work. So when we heard about self-provisioning—the ability to set up services and applications by yourself without the help of a dedicated IT specialist or service provider—we were all over it. It's like having your server, except that instead of having to buy your server, pay for its maintenance, and hire an IT person to manage it when things go wrong, you sign up with a cloud provider who has already done everything for you. Moreover, they'll even let you use their servers for free! So if you have ever wanted to launch your website but didn't want to take on the burden of managing it yourself, or if you've been dreaming of starting an online business but didn't want to spend all that money on servers and software licenses well, now's your chance! Self-provisioning is excellent, but the self-de-provisioning part is even more significant. Provisioning is like getting a massage—you know what you want and are in charge of getting it. Deprovisioning is like getting a haircut—it's a little more complicated than telling someone what to do. It requires much attention to detail and technical skill to ensure you're not cutting off any substantial parts of yourself in your zeal to be smooth and sleek. We don't want you to be soft and elegant! We want you to be well-groomed! So here are some tips for taking care of yourself by taking care of your resources. Always deprovision after using a resource so that others can use it when they need it later. Only do something once you've found another that does what that other one did for you (and then de-provision the old one).

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Secure Hash Algorithm (SHA)

Secure Hash Algorithm is a set of algorithms developed by the National Institutes of Standards and Technology and other government and private parties. Cryptographic hashes (or checksums) have been used for electronic signatures and file integrity for decades. However, these functions have evolved to address some of the cybersecurity challenges of the 21st century. The NIST has developed a set of secure hashing algorithms that act as a global framework for encryption and data management systems. The initial instance of the Secure hash Algorithm (SHA) was in 1993. It was a 16-bit hashing algorithm and is known as SHA-0. The successor to SHA-0, SHA-1, was released in 1995 and featured 32-bit hashing. Eventually, the next version of SHA was developed in 2002, and it is known as SHA-2. SHA-2 differs from its predecessors because it can generate hashes of different sizes. The whole family of secure hash algorithms goes by the name SHA. SHA-3, or Keccak or KECCAK, is a family of cryptographic hash functions designed by Guido Bertoni, Joan Daemen, Michaël Peeters, and Gilles Van Assche. SHA-3 competition to develop a new secure hash algorithm was held by the United States National Security Agency (NSA) in 2007. To be a super safe and fast hashing algorithm, SHA3 was developed from this contest. The evolution of cybersecurity has led to the development of several "secure hash algorithms." Security is a crucial concern for businesses and individuals in today's digital world. As a result, many types of encryption have been developed to protect data in various scenarios. One of these is hash algorithms. All secure hash algorithms are part of new encryption standards to keep sensitive data safe and prevent different types of attacks. These algorithms use advanced mathematical formulas so that anyone who tries to decode them will get an error message that they aren't expected in regular operation.

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Trending Definitions

Overfitting

Machine learning and data science often need help with the issue of overfitting. Trying to force a square peg into a round hole doesn't work. There's no point in trying; it simply won't work. Overfitting is a problem in machine learning and data science that happens when a model is trained using insufficient data. The model attempts to force a round peg (the overall dataset) into a square hole (the general problem). The model needs to improve at handling novel inputs. Take a model taught to identify pictures of dogs as pictures of dogs. After being fed a large collection of such images, the program learns to accurately identify canine subjects in photographs. In contrast, the model's performance declines because it has become too dataset-dependent when new, unseen dog photographs are introduced into the mix. Overfitting is problematic since it often results in subpar performance in generalization tasks. The model's predictive abilities on novel, unseen data may be limited. Since machine learning and data science aim to create reliable predictions based on novel data, this can present a challenge. Overfitting can be avoided in several ways, such as by utilizing a bigger training dataset, evaluating the model via cross-validation, and constraining the model with regularisation methods. Overfitting can be avoided by using a bigger training dataset, as this gives the model more data to learn from and makes it less data-dependent. The goal of cross-validation is to train and test a model using multiple independent subsets of the original training dataset. This can be useful for avoiding overfitting by testing the model on unseen data. To prevent the model from growing overly complex, regularisation can be used to impose further constraints on it. This can prevent overfitting by requiring the model to be simpler and broader, enhancing the model's capacity to accurately predict new data. That sums up, in a nutshell, the problem of overfitting in machine learning and data analytics. This issue arises when a model is overtrained on a small dataset and needs help generalizing to new data. Overfitting can be avoided in a number of methods, such as by increasing the size of the training dataset, employing cross-validation, or employing regularisation.

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Server Redundancy

So, imagine you're throwing a big party at your house. You've invited all your friends, prepared delicious food and drinks, and set up a killer sound system. But just as the party is getting started, the power goes out. Bummer, right? That can happen when you're running a business or website. If something goes wrong with the server that's running your website, it can cause massive disruption for your customers. But, just like you have a backup generator for your party, you can also have a backup server for your website. This is called "server redundancy." Server redundancy is all about having a backup plan in case something goes wrong with your primary server. This can be achieved in a few different ways, such as having a standby server that automatically takes over if the primary server goes down or having multiple servers running in different locations so that if one goes down, the others can pick up the slack. One of the most common ways to achieve server redundancy is through "clustering." This is where you have multiple servers working together as a team. Each server has a unique role; if one server goes down, the others can take over its responsibilities. This is like having different friends who can take over the DJ duties if one of them gets too drunk to keep playing music. Another way to achieve server redundancy is through "load balancing." This is where you have multiple servers that work together to distribute the workload. This is like having numerous bartenders at your party so that no one gets overwhelmed and everyone can enjoy their drinks. So, server redundancy is like having a backup generator for your website. It helps ensure that your website is still up and running even if something goes wrong with your primary server. So, you can keep the party going, and your customers will never know anything went wrong in the first place.

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Status Bar

It's the status bar, and it's here to stay. Sure, it's not as flashy as the new feature you've been working on, but it's one of those things that makes your application work. You can't have an app without a status bar, but once you have one, you can do many cool things with it. The first thing to know about the status bar is that a graphical control element displays certain status information depending on the application or device. It is usually expressed as a horizontal bar at the bottom of the computer application window or along the top of the screen for tablets and smartphones. The status bar can also be divided into information sections and sometimes has extra functionalities such as access to tools and shortcuts. In the case of text-based or console applications, they may replace the status bar with a status line. If you're using a computer, you need a status bar. It is an integral part of every computer program that allows you to view important information while using your computer. Whether you're working on a spreadsheet or writing a report, you need to be able to see how much time has elapsed since you started working on your project so that it doesn't take too long to complete; this information is shown in the form of tooltips on your computer screen by default when it appears next to an icon in your taskbar (which usually rests at the bottom right-hand corner). What if they still need to sign in? In that case, what do we show instead? Well… nothing! If someone hasn't signed in yet and tries to access some part of your app that would require logging in (like a particular feature or section), we won't let them see anything until they've signed up for an account first. That sounds like much work for just one little component!

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