#API Test

[totallyauselesstrashcan]

Write a short paragraph about trash can

A trash can is a simple yet essential item found in homes, offices, and public spaces. Its primary purpose is to collect and contain waste materials, keeping the surroundings clean and organized. Trash cans come in various sizes, shapes, and materials, such as plastic, metal, or even eco-friendly options made from recycled materials. They often have a lid to prevent odors and to keep pests away. Regularly emptying and cleaning the trash can is important to maintain hygiene and prevent the spread of germs. Overall, a trash can is a practical and necessary tool in managing waste disposal efficiently.

[indocoanalyticalsolutions]

Indoco Analytical Solutions provides drug, cosmetic and chemical testing solution to its client

[achamocha]

Anyway

```

from PIL import Image from numpy import asarray from time import sleep import requests

all_colors = [] for i in range(1,9):     image = Image.open(rf'./sky/{i}.jpg')     data = asarray(image)

    for i in data:         all_colors.append('#%02x%02x%02x' % tuple(i[95])) # 95 is the middle of the image

color_names = [] unique_color_dict = dict() for color in all_colors:     if color in unique_color_dict:         color_names.append(unique_color_dict[color])     else:         response = foo # query the api         name = response.json()["name"]["value"]         color_names.append(name)         unique_color_dict[color] = name         sleep(1) # let the poor api rest

intro = "[Image description: A long image of the phrase 'Do you love the color of the sky?' in black font over a gradient background of " medio = ", ".join(color_names) final = ", followed by the phrase in black font reading 'Which one?'. End ID]" total = intro+medio+final print(total)```

[gayspock]

i made blondies and theyre awful. suicide really is the answer to everything,

[lizclipse]

the thing i find with rust is that things never compile first time (it’s expected so the errors are quite helpful most of the time) but the flow is usually compile to see if you got things right and change if not. however, running first time is downright common, and i love it

[why-tap]

WHY tap

[claire-starsword]

oh god i just connected so many dots from looking at a cute beedrill fanart

Beedrill is called Spear in japanese, written in katakana as スピアー (supia-). Reading that backwards you have アピス (apisu).

Apis. As in, scientific name of bee. This might be the whole idea behind the Pokemon.

But more important to my current Shining Force obsessed self,

it finally explains this boy's name!! I have been joking about this boy having a random bee name for over a decade!!! Turns out I just had to connect some wildly distant dots in my life!!

[geraskier]

fortune, like, favors the brave, dude!

(shirt design by @zefyr)

[mio-nika]

How do you get access to exact poll results through the API?

Simple answer: Going to the page of the poll and pushing f12 and scrolling the network tab to find the exact request.

The long answer...

Fast recap how websites works:

We have backend (sweet sweet guts of the website) and frontend (beautiful facade). Usually frontend does nothing in the brains department. It's just looks what part of the website user checks, what buttons they're pushing and asks backend for the information. So, basically when you on the page with the poll, frontend needs to ask "what is current results, please send me information I need to create things" and you can spy on it, by looking through the network tab of the developer console (f12). Usually you can see all the requests from frontend to backend there.

The results actually are not protected by any way, and you don't need to be logged in in any way.

[iobartach]

🙃

[cromacampus2021]

APIs are an ignored aspect of online applications. One such popular API testing technology is the Karate framework. In this post, we will explore the basic concepts and the benefits of this framework. To know more about it join Karate API Online Training.

[blacklocksecuritynz]

Web Application Penetration Testing

Blacklock offers web application penetration testing to help businesses ensure the security of their applications. This service includes identifying and exploiting vulnerabilities in web applications, APIs, and mobile applications. Our API penetration testing helps to improve application security by simulating real-world attacks and identifying weaknesses before they can be exploited by malicious actors. Contact Blacklock Security to enhance your web application security and get it work smoothly.

[emmaameliamiaava]

Why Every Developer Needs an API Monitoring Dashboard for Real-Time Insights

In today’s fast-evolving digital landscape, APIs (Application Programming Interfaces) are the backbone of modern software development. They enable seamless communication between applications, services, and devices, allowing businesses to deliver enhanced functionality and integrations. However, as the reliance on APIs increases, ensuring their smooth operation becomes crucial for developers. This is where an API monitoring dashboard becomes indispensable.

In this blog, we’ll explore why every developer needs an API monitoring dashboard for real-time insights and how it can improve the API development process.

The Importance of Monitoring APIs in Real-Time

When developing an API, it’s not just about building and deploying it; maintaining its performance and reliability is equally critical. APIs can experience performance bottlenecks, latency issues, downtime, or security vulnerabilities, all of which could impact user experience. Monitoring your API in real time allows you to proactively identify and address these problems before they escalate.

An API monitoring dashboard provides a centralized platform where developers can track the health and performance of their APIs. This tool not only helps in detecting issues but also offers real-time insights into how the API is being used, making it an essential part of any developer’s toolkit.

Key Benefits of an API Monitoring Dashboard

1. Real-Time Alerts and Notifications

APIs are subject to downtime or failures that could impact business operations. With an API monitoring dashboard, you can set up real-time alerts that notify you of any issues such as server outages, slow response times, or unexpected errors. This allows developers to respond immediately, minimizing downtime and reducing the negative impact on users.

Example: If an API that processes payments starts showing increased response times, a real-time alert would notify developers so they can address the issue before transactions fail.

2. Tracking API Performance and Latency

One of the major challenges in developing an API is ensuring that it performs efficiently under different conditions. An API monitoring dashboard offers detailed insights into your API’s performance, including latency and response times. By tracking these metrics, developers can optimize their APIs for better performance and ensure they meet the expected service level agreements (SLAs).

Example: An e-commerce API experiencing slow response times could lead to abandoned shopping carts. Monitoring this in real time allows developers to optimize the API’s performance to enhance user experience.

3. Improving API Reliability and Availability

An API monitoring dashboard enables developers to measure the uptime and availability of their APIs. Downtime can have significant consequences, especially for APIs used in mission-critical applications. Continuous monitoring ensures that developers are always aware of their API’s status, allowing them to maintain high availability and meet customer expectations.

Example: A healthcare API used for processing patient data must ensure 100% availability. An API monitoring dashboard helps ensure that any downtime is swiftly addressed to maintain reliability.

4. Identifying Usage Patterns and Trends

In addition to performance metrics, an API monitoring dashboard can provide valuable insights into how your API is being used. By tracking usage patterns, you can determine peak usage times, common endpoints accessed, and the geographical locations of users. This data allows developers to make informed decisions about scaling resources, adding features, or addressing potential issues with specific endpoints.

Example: If an API serving a global audience sees a spike in traffic from a particular region, developers can optimize server resources to ensure smooth performance during those peak times.

5. Proactive Troubleshooting and Debugging

When something goes wrong with your API, it’s often difficult to pinpoint the issue without comprehensive data. An API monitoring dashboard collects and logs detailed data about each request, helping developers troubleshoot issues more effectively. Whether it’s a slow endpoint, failed request, or a security threat, developers can use real-time data to quickly diagnose and resolve problems.

Example: If a particular endpoint is failing due to high traffic, developers can use the API monitoring dashboard’s logs to analyze the problem and adjust the API configuration to prevent future issues.

How API Monitoring Improves the Development Process

Having real-time insights into your API’s performance not only helps you maintain uptime and reliability, but it also enhances the entire API development lifecycle. Here’s how:

Faster Debugging: Real-time monitoring simplifies identifying issues during API development, reducing debugging time.

Improved Testing: With continuous monitoring, you can catch performance issues early, allowing you to refine and test APIs before going live.

Enhanced User Experience: By ensuring your API runs smoothly, you enhance the overall experience for the end users who rely on the API for app functionality.

Conclusion: Why Every Developer Needs an API Monitoring Dashboard

As businesses and applications increasingly rely on APIs, ensuring their smooth and uninterrupted operation is crucial. An API monitoring dashboard provides developers with the tools and real-time insights they need to ensure optimal API performance, reliability, and scalability.

Whether you are developing an API from scratch or maintaining a complex API ecosystem, monitoring is essential. By utilizing an API monitoring dashboard, developers can respond proactively to issues, optimize API performance, and deliver a seamless experience to users. For anyone serious about developing and maintaining APIs, this tool is non-negotiable. Do you have an API monitoring solution in place? If not, now is the time to implement one and take control of your APIs!

[engenhariadesoftware]

Testes de Software: Caixa Branca e Caixa Preta Garantia de Qualidade?

Introdução aos Testes de Software O desenvolvimento de software envolve diversas etapas para garantir que o produto final seja funcional, eficiente e atenda às necessidades dos usuários. Uma das fases mais cruciais deste processo é o teste de software, uma prática fundamental para a garantia da qualidade e confiabilidade do sistema. Testar um software não apenas identifica falhas e bugs, mas…

[jcmarchi]

The Future of AI in Quality Assurance

New Post has been published on https://thedigitalinsider.com/the-future-of-ai-in-quality-assurance/

The Future of AI in Quality Assurance

Traditional quality assurance (QA) processes have long depended on manual testing and predefined test cases. While effective in the past, these methods are often slow, susceptible to human error, and lead to development delays and inflated costs. Unsurprisingly, Gartner reports that 88% of service leaders feel that today’s QA approaches don’t meet the mark. As AI takes center stage, AI quality assurance can empower teams to deliver higher-quality software faster. This article explains how AI in  quality assurance streamlines software testing while improving product performance.

What is AI-powered Quality Assurance?

AI quality assurance (QA) uses artificial intelligence to streamline and automate different parts of the software testing process. AI-powered QA introduces several technical innovations that transform the testing process.

Machine learning models analyze historical data to detect high-risk areas, prioritize test cases, and optimize test coverage. AI also automates test data generation, creating a wide range of test data that reduces the need for manual input.

With adaptive testing, AI adjusts test cases in real-time as user requirements change. Additionally, AI empowers testers to build and run tests easily without writing a single line of code.

Benefits of AI in Quality Assurance

Here are a few benefits of AI-powered quality assurance:

Greater Efficiency: AI takes over the repetitive tasks that often slow the QA process. As a result, QA teams can focus on delivering high-performing software instead of generating test cases and tracking errors manually. AI automates test data generation, too, creating a wide range of test data that reduces the need for manual input. As a result, QA teams execute a higher volume of test cases and cover a broader range of scenarios. With these advantages, AI-powered QA can help organizations reduce QA costs by more than 50%.

Enhanced Accuracy: AI-powered automation boosts QA accuracy by eliminating human errors common in manual testing. Automated QA surpasses manual testing by offering up to 90% accuracy. As a result, it becomes better at recognizing patterns, bugs, and performance issues that manual testers might miss.

Intelligent Testing: Machine learning analyzes past data to identify high-risk areas and helps prioritize which test cases need attention first. Through AI-powered adaptive testing, testers can update test cases in real time as needs and requirements evolve.

Emerging Trends in AI Software Quality Control

AI is reshaping how QA teams operate, from speeding up test creation to enhancing test data management. Here are a few emerging trends in AI software quality control:

AI-powered Test Automation

Creating test cases is now faster and more accurate with AI. Tools like Taskade’s AI Test Case Generator analyze software requirements and automatically automate test cases to cover a wide range of scenarios. This simplifies the testing process and ensures you don’t miss any critical areas. The result? Better coverage and higher accuracy, all in less time.

Automatic Recovery from Test Failures

One of the most valuable AI features is automatic recovery from test failures or ‘self-healing.’ TestRigor excels here, as it can adjust tests automatically when the application changes. This means fewer interruptions and less time spent fixing test scripts. The tests only fail when AI detects errors relevant to application requirements.

Improved Predictive Analytics for Quality

Tools like those used by Atlassian dive into historical data to predict potential failures and spot root causes before they become more significant issues. This allows teams to focus on high-risk areas and prioritize testing where it matters most. McKinsey points out that these analytics can significantly improve software reliability and cut down on warranty costs.

Enhanced Test Data Management

With AI-driven tools, managing test data becomes much simpler. Solutions offering synthetic data generation and data masking ensure that the test data is realistic and accurate while protecting sensitive information. Synthetic data helps QA teams conduct meaningful tests while complying with data privacy regulations.

AI-Powered Monitoring

AI-powered monitoring offers real-time feedback during testing, which means issues can detected and fixed immediately. Monitoring tools track performance across different environments. This ensures that software works consistently no matter where it’s running. This also makes troubleshooting faster and keeps performance up to par under various conditions.

Enhanced Test Case Prioritization

Another area where AI makes a significant impact is prioritizing test cases. Tools like Deloitte Digital Tester use historical test results and product usage data to determine the most critical tests. Therefore, teams can focus on the most important tests first, reducing unnecessary tests and improving overall efficiency in the QA process.

How Popular Testing Tools Leverage AI in Quality Assurance

Testing tools are becoming smarter and more efficient by integrating AI. Here are some popular tools that are using AI to boost their capabilities.

Test Automation Tools

Selenium uses AI to enhance its web automation capabilities by efficiently identifying dynamic elements within web applications. Its AI-powered self-healing feature keeps test scripts up-to-date as application elements change, reducing the need for manual maintenance. This makes Selenium a versatile tool for automating functional tests across multiple platforms and browsers.

Appium uses AI to simplify mobile app testing across iOS and Android platforms. It automates the detection and interaction with mobile elements, such as gestures and inputs. Ultimately, AI helps Appium streamline the test creation process and give users a unified testing experience across both mobile operating systems.

Test Management Tools

TestRail integrates AI to streamline test management by generating test cases through NLP. It goes one step further and prioritizes each test case based on risk. Additionally, TestRail uses AI to assist in debugging, making test maintenance more efficient and reducing the likelihood of errors.

ALM Octane uses AI to enhance test management and analytics. Its AI-driven quality risk analysis recommends tests for high-risk areas, ensuring that critical issues are covered. The platform’s AI-powered root cause analysis helps pinpoint defects, while NLP allows both technical and non-technical users to easily create tests in natural language.

QA Tools

TestCraft provides a low-code, AI-powered platform for web application testing. It automatically generates test scenarios and uses AI to self-heal test scripts as UI changes. This minimizes the need for manual updates.

ACCELQ simplifies test automation with its AI-powered, codeless platform. It supports behavior-driven development (BDD), mirroring real business processes to create reusable test cases. Additionally, AI helps manage the automation of complex dynamic web pages and APIs, making ACCELQ highly efficient for testing modern web applications and services.

Parasoft uses AI to enhance its continuous quality platform by automating end-to-end testing processes. AI improves test coverage from code to UI, ensuring software reliability. The platform also provides AI-driven analytics to identify test gaps and optimize the overall testing strategy.

Challenges and Limitations of AI Quality Control

While AI brings several benefits to QA, there are a few challenges to keep in mind. Firstly, adding AI to the QA workflow requires a significant upfront investment. Businesses must allocate the necessary time and resources upfront to use AI effectively.

Beyond cost, inaccurate or biased data can compromise results, making AI less effective in detecting bugs or optimizing testing. This is closely tied to growing ethical concerns. If AI learns from biased data, the outcomes will reflect those biases and skew the results. QA teams must scrutinize data and maintain transparency throughout the testing workflow to ensure fairness.

Similarly, generative AI has not yet fully matured in QA, especially in mobile app testing. For example, tools like ChatGPT can’t yet test across diverse mobile devices. This limits their ability to load apps on specific hardware or create detailed test cases for specific functions like login screens. These limitations show that while AI is rapidly evolving, it hasn’t yet replaced the need for manual testing in certain areas.

How Will AI Impact Quality Assurance in the Future?

As more QA teams adopt AI for its unparalleled efficiency and precision, it will become an integral part of their workflows. The result will be greater innovation and new benchmarks for speed and quality in software development.

AI-powered QA is also becoming central to DevOps. Seamless integration will allow for continuous testing and faster release cycles. Processes will become more efficient, and collaboration between development and QA teams will improve.

Upskilling will become essential as AI transforms QA. Organizations must invest in training to ensure teams can fully leverage AI’s potential. Those who adapt will lead in an AI-driven future, while others risk falling behind.

Final Words

AI-driven QA is poised to automate repeatable tasks and enable smarter, more efficient testing. From automating test case generation to improving error detection and reducing time-to-market, AI-powered QA sets new standards for speed and quality.

Stay ahead in the future of AI-powered QA—follow Unite.AI for the latest updates!