software testing

Guidelines On How To Test Blockchain Implementation

The Blockchain Implementation world should be familiar to everyone who works with digital currencies. After conducting thorough research, we have concluded that blockchain technologies may be leveraged to transform your company. Follow these guidelines on how to test blockchain implementation. The Following are the steps involved in Testing Blockchain Implementation: 1. Starting with a Use Case To begin, this is the most crucial step in the blockchain implementation process. To identify a use case, you must first research, explain, and organize your blockchain requirements. Next, you must choose relevant blockchain use cases to examine, research, and explore extensively before incorporating them into your blockchain implementation. 2. The Importance of Creating a Proof of Concept (POC) The next stage is to construct a valid Proof of Concept (POC) for your use case. POC is a strategic approach for determining whether or not a blockchain implementation is feasible for your company. It's critical to emphasize that "direction" is essential to developing your Proof of Concept. You should be able to see what your actions might lead to. In a nutshell, you should be tactical and deliberate in your actions and conclusions. The steps for creating a Proof of Concept are as follows: Create and adhere to a set of standards that describe the scope of your company’s endeavor. Make a prototype that includes sketching, design, coding, architecture, and other elements. Put your prototype to the test. Analyze your MVP with the bare minimum of desired top characteristics. The term "minimum viable product" refers to a product that meets the basic minimum requirements. 3. Carefully choosing a blockchain When choosing a blockchain platform for your company, it's critical to be deliberate and cautious. This is a strategic step in which you must think about your budget and do extensive research. There are several well-known platforms to choose from when choosing a Blockchain for your organization. Some of the most well-known blockchain platforms are: Ethereum: It is primarily used to create creative contracts. Many companies are already using it to estimate their business's prospective growth rate. Quorum: Many businesses have used Quorum as part of their blockchain implementation. Quorum essentially eliminates data tampering in commercial transactions. Hyperledger Fabric is a blockchain technology that allows corporations and organizations to construct private, public, chain-based applications. Stellar: It is a blockchain platform used to create blockchain applications for businesses and organizations. Corda: A blockchain platform expert would produce unique designs while also exploiting the platform to eliminate all costly contingencies in your business contracts. It enables you to conduct direct transactions using smart contracts that ensure the highest security and anonymity. Open the chain: The main goal of this blockchain platform is to take advantage of blockchain technology to improve every element of your company's human resource management. Multichain: The Multichain platform may be used in various industries, including banking and finance, healthcare, human resources, e-commerce, education, retail, and so on. It is mainly utilized for professional human resource optimization in your company. 4. Developing and Validating a Blockchain Solutions It's a good idea to keep up with the latest blockchain technology so you can pick the ideal one for your needs. This procedure entails assessing several variables, including: Blockchain technology's infrastructure. It's also important to know whether it works with both private and public blockchains. Is it compatible with multichain and other various platforms because of the breadth of the technology? The functionality of smart contracts is one of the essential aspects of blockchain technology. Smart contracts allow firms to conduct critical transactions without third parties, and this has the advantage of making the transaction process more automated, transparent, and fair. Testing your system requires testing apps on several platforms. 5. In production, run and manage the network To accomplish this, you must first construct your block. Keep in mind that this block must have all of the chain's attributes. Essentially, this step lays the groundwork for receiving data. At this point, the encrypted token, more commonly known as cryptocurrency, enters the picture. The encrypted token would activate the power required to ensure that the nodes' communication would stay persistent. 6. Active Blockchain All that remains is to activate an application on the blockchain network after the network has been set up and managed. This is an application server that is ready to use. Each of your applications would be hosted on the main blockchain in this case. It is advised that you initialize hybrid solutions on the cloud server if we are on-chain and off-chain entities. 7. Choosing the Most Appropriate Consensus Protocol The goal of Proof of Work is to protect against cyber-attacks like DDoS or Distributed Denial-of-Service attacks. The agreement in this consensus system is based on the amount of cryptocurrency held by miners or the 'weight' of their coins. In most Proof of Stake implementations, the miner must validate block transactions based on the number of coins they own. This consensus protocol ensures that transactions are approved. A miner's ability to create more blocks is proportional to the amount of cryptocurrency in their possession. 8. Creating an Ecosystem When a large number of stakeholders become involved in the blockchain, an ecosystem becomes necessary. An ecosystem will function as a subset of the greater blockchain community. It will serve to boost understanding of the sector and encourage business trust. Stakeholders must agree on the following issues in order to create an ecosystem: The terms of the contract How to ensure that costs and benefits are distributed fairly. The governance procedures that have been put in place. Having a Planned Design 9. Having a Planned Design Every blockchain specialist will agree that the blockchain's structure necessitates careful planning. The design must be purposeful to ensure that any faults with the organization are quickly resolved. ...

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Difference between Functional and Non-Functional testing?

Functional and Non-Functional Testing are two fundamental aspects of software quality assurance, each serving a distinct purpose in ensuring the reliability and performance of software applications. Functional Testing: Functional testing primarily focuses on evaluating the functions and features of a software application to ensure that it performs as intended. Testers examine the application against predefined requirements, and the key objective is to verify that it delivers the expected outcomes under various conditions. Test cases are designed to check specific functionalities, such as user interactions, data processing, and output generation. The goal is to identify any functional defects or discrepancies between the actual and expected results. Functional testing is essential for confirming that the software meets its intended purpose and user requirements. Non-Functional Testing: Non-functional testing, on the other hand, assesses aspects of an application that go beyond its basic functionality. It is concerned with performance, reliability, usability, and other quality attributes. Non-functional testing includes various types, such as: Performance Testing: This evaluates how well the application performs under different conditions, including load testing, stress testing, and scalability testing. Usability Testing: It examines the user-friendliness of the software, focusing on the user experience, accessibility, and overall usability. Security Testing: Security testing identifies vulnerabilities and ensures that data and resources are adequately protected. Reliability Testing: This verifies that the application consistently operates without failures or crashes. Scalability Testing: It assesses how well the software can adapt and handle increased workloads. In summary, while functional testing ensures that the software functions correctly, non-functional testing validates its performance, security, and usability under diverse conditions. Both types of testing are crucial for delivering a well-rounded, high-quality software product that meets user expectations and performs reliably in real-world scenarios. A well-designed Quality Assurance strategy should cover all software aspects. It would not only check the basic functionality of the application but also the way it performs its required functions. By understanding the difference between the two objectives, we establish that quality assurance agendas need to be distinctively dedicated to testing both how the application behaves (Functional Testing), and at what level of performance (Non-Functional Testing). In this article, we will learn the definition of both main testing approaches look at their key differences, and how we at Q-Pros apply each testing type to our services. What is Functional Testing? Functional testing is mainly concerned with testing a software’s behavior under test. This means we build a set of test cases, and we examine if the software is responding with the expected output. These tests are done in real environments to guarantee that the actual result is synchronized with client demands. This type of testing mostly consists of Black-box testing and is unconcerned with the application’s source code. Types of Functional Testing: Smoke testing – This type of testing checks functionally on a crucial level (Making sure the applications are working fine), it paves the way for other testing types to take part. Sanity testing – It is regarded as an extremely specific way of testing; it mainly focuses on testing a specific function within the application to check if it runs properly with no bugs or defects. Integration testing – This type of testing will determine if components within the system that are intended to integrate are functioning correctly in one singularity. Localization testing – A technique in which tests are performed to check if the application can cope and be suitable with local changes i.e., language, region, time zone. etc. Regression testing – A testing method that proceeds any changes that occur to the system's internal code to make sure no new bugs arise as a result of the modification. It also checks to see if the entire system is working fine with the novel changes. User Acceptance Testing (UAT) – This testing method is used to make sure that the application meets the user’s standards. Under a certain set of criteria, UAT sessions are carried out before the official launch/release date, to make sure that the applications hold their ground within a real environment and guarantee “user’s acceptance.” What is Non-Functional Testing? Non-functional testing is the testing of software application for its non-functional requirements i.e., the way the system operates, rather than the way the system behaves Non-functional testing is designed to assess a system’s readiness using non-functional factors never addressed by functional testing. Checking how many individuals can simultaneously log into software is an example of a non-functional test. Non-functional tests are just as important as functional testing and impact customer satisfaction. To consider any application a reliable one, we must check if the application is running smoothly with speed and efficiency, this will assure client and user satisfaction. To achieve that level of quality, a group of non-functional testing methods was established to cover all areas of performance indicators, these methods are mainly designed in a way that makes it difficult for manual testing and instead mostly rely on automated testing tools. Types of Non-functional Testing: Load testing – It is the process of assessing the behavior of a system in a simulated environment under predicted conditions (various numbers of users). Stress testing – A testing method that evaluates performance when resources are scarce, such as when a server is down or there is not enough capacity on a device’s hard drive. Scalability testing - This testing method determines whether a system can scale with additional usage and, if so, how much the performance is impacted. Security testing – A process of testing that determines how vulnerable a system is to attack and safeguard data. Volume testing – it evaluates performance with a large amount of data, not necessarily involving many users, but rather a single person executing a high-volume job, such as multiple-file uploading. (Functional Testing VS Non-Functional Testing) Functional Testing Non-Functional Testing Objective validating software actions. ensuring that the software performs as expected. Execution It is carried out before non-functional testing. This is done when the functional testing is completed. Usage Assists in the validation of the application's behavior. Assists in validating the application's performance. Testing tools It can simply rely on manual efforts. It is extremely difficult to execute manually. (Automated testing tools are used) Requirement It is done based on business requirements. It is done based on customer expectations and performance requirements as defined by IT. Q-Pros Q-Pros is a leading IT company specializing in providing testing services and Quality Assurance. Learn more about testing types and request one our unique testing services via our online request form ...

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Quality Assurance vs Quality Control

This article provides clarity on the often confusion in the IT industry regarding the difference between quality assurance (QA) and quality control (QC). Many QA teams, in fact, practice quality control. But let’s briefly discuss why Quality is a must property of software applications nowadays. It has always been known that the success of any software product, application, and/or service relies greatly upon users’ satisfaction. Users are satisfied just when they have their requirements/needs met and associated with all relevant quality factors (Correctness, Usability, Reliability, Efficiency, Integrity, etc.) to enhance their journey using the software application. In any typical IT project, there are four significant constraints that need to be worked out during project execution. These four requirements are scope, time, cost, and quality. In terms of importance, experience has proven that quality ranks second after scope. If a software application is delivered within the given cutoff time, scope, and cost but lacks the quality factors would lead to unhappy users and consequently business loss. Organizations usually get rectified by adding more time and budget to cover the needed correction and rework. Quality terms can be segmented into two categories - assurance/control - based on the scope and type of activities involved. The confusion between both categories can be clarified by the following definitions. Quality assurance is a strategy of prevention. This means that QA covers all activities that are related to planning, defining, and designing work policies, processes, and procedures for the team to follow during the software development lifecycle in order to prevent errors, issues, or defects. This set of processes helps minimize or even avoid result variation (due to technologies, tools, manpower, etc.) and ensures that an acceptable level of quality is always achieved. Therefore, if there is no need for a process, there is no role for quality assurance. Examples of QA deliverables: Development methodologies Estimation processes Requirements definition processes Testing processes and standards What is Quality Control? Quality control is a strategy of detection. This means that QC activities focus on identifying defects in the actual software produced. These activities begin at the start of the software development process with reviews of requirements and continue until all application testing is complete. Therefore, it is possible to have quality control without quality assurance. For example, a test team may be in place to conduct application testing at the end of development, regardless of whether that application is produced using a software development methodology. Examples of QC deliverables: Requirement Review Reports Testing Execution Reports Test Cases Suite Defects List Summary: Difference between Quality Assurance (QA) and Quality Control (QC) Quality Assurance Quality Control QA activities aim to prevent defects QC activities aim to detect defects It is a proactive measure It is a reactive measure QA identifies weaknesses in a process to improve it QC identifies defects in an application or service to have them fixed QA is concerned with all applications that will ever be developed by a process. QC relates to a specific application or service. Types of Software Testing Based on what was explained earlier in this article, Software Testing is one of the main activities under the umbrella of Quality Control. Software Testing is segmented into two categories; functional and non-functional. Functional Testing – validate the application’s compliance with its functional requirements and design specifications. The various techniques under this testing type focus on validating that the application’s functionalities are met and work as expected without issues. Some of the main functional testing techniques are: Unit Testing Component Testing (Whitebox) Integration Testing (Greybox) System Testing (Blackbox) Mobile Testing Omnichannel Testing Error-handling Testing Regression Testing Business As Usual Testing (BAU) User Acceptance Testing (UAT) Non-functional Testing – validate the application’s compliance with technology, safety, operation, digital standards,s and best practices. functional requirements and design specifications. The various techniques under this testing type focus on validating that the application’s non-functional requirements are met and work as expected without issues. Some of the main non-functional testing techniques are: Usability Compatibility Localization Personalization Performance Load / Stress Security Scalability ...

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In software testing, features and functionalities supported by a software system are not the only concern. The performance of a software application, like its response time, reliability, resource, usage, and scalability, is essential for customer satisfaction. The goal of Software Performance Testing is not to find bugs but to eliminate performance bottlenecks. This article provides insights into one of the tricky non-functional testing types that usually gets overlooked during the software development lifecycle. And how we at Q-Pros plan, design, and execute various performance evaluation techniques the right way without enduring a high cost. What is Performance Testing? Performance Testing is done to provide stakeholders with information about their application regarding speed, stability, and scalability. More importantly, Performance Testing in software testing is to uncover what needs to be improved before the application goes to market. Without Performance Testing, the software is likely to suffer from issues such as running slow while several users use it simultaneously, inconsistencies across different operating systems, and poor usability. Software Performance testing will determine whether the software meets speed, scalability, and stability requirements under expected workloads. Applications sent to the market with poor performance metrics due to nonexistent or low-quality performance testing are likely to gain a bad reputation and fail to meet expected sales and adoption goals. Also, mission-critical applications should be performance tested to ensure they run for longer periods without deviations (i.e., soak testing). According to Dunn & Bradstreet, 59% of Fortune 500 companies experience an estimated 1.6 hours of downtime every week due to an unhandled high load. Considering the average Fortune 500 company with a minimum of 10,000 employees is paying $56 per hour, the labor part of downtime costs for such an organization would be $896,000 weekly, translating into more than $46 million per year. Only a 5-minute downtime of Google.com (19-Aug-13) is estimated to have cost the search giant as much as $545,000. Why do we need Performance Testing? Performance optimization of various products, platforms, devices, and technologies. Shifting from conventional models to continuous delivery. Limited timelines and budgets while providing high-quality service. Maximizing performance of the existing infrastructure. Early detection of system performance. Common Performance Problems Most performance problems revolve around speed, response time, load time, and poor scalability. Speed is one of the most important attributes of an application. A slow-running application (i.e., high time-to-interact) will lose potential users. Performance testing guarantees that an app runs fast enough to keep a user’s attention and interest. Here is a list of the most common performance problems: Long loading time - Load time is normally the initial time it takes an application to start. This should generally be kept to a minimum. While some applications are impossible to load in under a minute, Load time should be kept under a couple of seconds if possible. Poor response time – Response time is the time it takes from when a user inputs data into the application until the application outputs a response to that input. Generally, this should be very quick. Again, if users wait too long, they lose interest. Poor scalability – A software product suffers from poor scalability when it cannot handle the expected number of users or if the application does not accommodate a wide enough range of users. Bottlenecking - Bottlenecks are obstructions in a system that degrade overall system performance. Bottlenecking is when either coding errors or hardware issues cause a decrease in throughput under certain loads. Bottlenecking is often caused by one faulty section of code. The key to fixing a bottlenecking issue is to find the section of code that is causing the slowdown and try to tune it. Bottlenecking is generally fixed by either fixing poor running processes or adding additional Hardware. Some common performance bottlenecks are: CPU utilization Memory utilization Network utilization Operating System limitations Disk usage Types of Performance Testing We Cover Load testing – checks the applications' ability to perform under anticipated user loads. The objective is to identify performance bottlenecks before the application goes live. Stress testing – involves testing an application under extreme workloads to see how it handles high traffic or data processing. The objective is to identify the breaking point of an application. Soak testing – is done to validate the software's ability to handle the expected load over a long period. Spike testing - tests the software's reaction to sudden large spikes in the load generated by users. Volume testing – Under volume testing, a large number of Data gets populated in a database, and then the overall behavior is monitored. The objective is to check the performance under varying database volumes. Scalability testing - The objective of scalability testing is to determine the software application's effectiveness in "scaling up" to support an increase in user load. It helps plan capacity addition to your software system. This is critical with cloud hosting. In Conclusion Performance testing is essential for companies who wish to provide an excellent long-living digital service. Certain types of clients would include governmental and semi-governmental agencies as well as privet institutions that rely heavily on online mediums for outreach. Q-Pros works and continues to make progress in elevating performance for our clients. Request your very own custom-made testing service via our online request form ...

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Artificial Intelligence

Artificial Intelligence (AI) is no longer a futuristic concept confined to science fiction; it's a transformative force that's reshaping our world across various domains. In this article, we'll delve into the fascinating realm of AI, exploring its history, core principles, current applications, and the potential it holds for the future. The Genesis of Artificial Intelligence Artificial Intelligence, as a field of study, was officially coined in 1956, but its origins can be traced back to early philosophical debates about human cognition. Early pioneers like Alan Turing and John McCarthy laid the foundation for AI by proposing concepts such as the Turing machine and the Dartmouth Workshop. AI's development took significant strides in the 20th century, with the creation of expert systems and the development of rule-based AI. However, AI's progress was marked by periods of enthusiasm followed by "AI winters" where research slowed due to high expectations and unmet challenges. Key Concepts in AI Artificial Intelligence operates on several fundamental principles, including: Machine Learning (ML): ML is a subset of AI that focuses on creating algorithms and models that enable computers to learn from and make predictions or decisions based on data. Neural Networks: Inspired by the human brain, neural networks are a fundamental component of deep learning, allowing Artificial Intelligence systems to process data, identify patterns, and make complex decisions. Natural Language Processing (NLP): NLP enables AI to understand, interpret, and generate human language, giving rise to applications like chatbots and language translation services. Computer Vision: Artificial Intelligence systems use computer vision to "see" and interpret images or video, powering applications from facial recognition to autonomous vehicles. Robotics: AI-driven robots can perform tasks ranging from manufacturing to healthcare, leveraging sensors, actuators, and intelligent algorithms. AI Applications Today Artificial Intelligence has transcended its academic roots and is now deeply embedded in our daily lives: Virtual Assistants: Voice-activated virtual assistants like Siri, Alexa, and Google Assistant have become part of our daily routines, from setting reminders to answering questions. Recommendation Systems: Streaming services, online retailers, and social media platforms use AI to suggest content, products, and connections based on user preferences. Healthcare: AI assists in medical diagnostics, drug discovery, and predictive analytics, enabling more accurate and efficient healthcare delivery. Finance: AI algorithms drive trading decisions, credit scoring, fraud detection, and personalized financial advice. Autonomous Vehicles: Self-driving cars and drones use AI to navigate and make real-time decisions based on sensor data. Natural Language Processing: NLP is revolutionizing customer support with chatbots, enhancing language translation services, and improving sentiment analysis for businesses. AI Ethics and Challenges Artificial Intelligence rapid growth also raises ethical concerns and challenges: Bias and Fairness: AI algorithms can inherit biases from training data, potentially resulting in discriminatory or unfair outcomes. Privacy: The use of AI in surveillance and data analysis raises privacy concerns about how personal information is used. Job Displacement: Automation powered by AI may lead to job displacement in certain industries, requiring society to adapt to changing employment landscapes. Security: The same AI used to protect against threats can be used to orchestrate cyberattacks, creating an ongoing security dilemma. The Future of AI The future of AI is brimming with possibilities: AI in Healthcare: AI is expected to continue making significant contributions to medical diagnosis, drug development, and personalized treatment plans. AI in Education: Adaptive learning platforms powered by AI can provide personalized education experiences, enhancing student engagement and outcomes. AI in Environmental Conservation: AI can play a vital role in monitoring and addressing environmental challenges, such as climate change and wildlife conservation. AI and Creativity: AI-generated art, music, and literature are becoming more sophisticated, challenging our understanding of creativity. AI in Space Exploration: AI systems are used to analyze vast datasets from space missions, aiding in the search for extraterrestrial life and understanding the universe. AI in Robotics: Advanced robotics and AI systems are likely to transform industries like manufacturing, agriculture, and healthcare. In conclusion, AI's influence on our world is profound and multifaceted, touching every aspect of our lives. While Artificial Intelligence has come a long way since its inception, its journey is far from over. As AI continues to evolve, its potential to address complex problems and enhance human capabilities is boundless. The careful consideration of ethics and responsible development will be paramount as we navigate the AI-driven future, ensuring that the transformation it brings is not just profound but also positive. Request a Service ...

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