#software testing life

[egophiliac]

hey so I know you said art for personal use is okay but i was curious as to if you would be okay with me printing out your art 😭 i need things on my walls in my dorm and ive been obsessed with your unique magic series since you posted them lol. totally ok if not!

oh yeah, that's absolutely fine, thank you! ❤️❤️❤️ and if you're after the UM posters specifically, I put up higher-res versions of them here so they should print pretty nicely! :>

[ghduck]

Heeh

Lil test

[seens]

bit stoned applying to jobs n answering their dumbass questions like man I don't even remember what this job was but I play ARGs and have 14 years experience in python

[syncrovoid-presents]

I will continue being gone for a few days, sadly my original al plan of releasing the newest chapter of The Consequence Of Imagination's Fear has also been delayed. My apologies

Can't go into detail because its hush hush not-legally-mentionable stuff but today is my fifth 12 hour no-break work day. I'm also packing to move too in a fortnight (which is a Big Yahoo!! Yippee!! I'll finally have access to a kitchen!! And no more mold others keep growing!!! So exciting!!!)

[melsatar]

Discover how Artificial Intelligence is transforming the entire software development lifecycle (SDLC) — from requirements gathering to deployment and maintenance. In this article, we explore how AI tools boost productivity, enhance quality, and reshape the way teams build modern software. Learn how developers and organizations can harness AI as a powerful collaborator in creating faster, smarter, and more reliable applications.

[vtusoft]

Accelerating Software Development with Generative AI in SDLC

Generative AI is rapidly transforming the Software Development Life Cycle with Generative AI by enabling developers to automate critical processes. This innovation brings about a shift in how teams approach coding, testing, and deployment. By leveraging AI in SDLC, businesses can experience faster development cycles and a reduction in manual errors.

One of the most significant impacts of this transformation is the introduction of Gen AI in Software Development, which assists developers in creating software more efficiently. By automating repetitive tasks like code generation and bug fixes, AI-powered tools allow developers to focus on more complex, high-level problems.

Furthermore, integrating Generative AI in software testing enables faster and more accurate testing cycles. AI can identify bugs early in the development process, which reduces the time spent on manual testing and accelerates product releases.

As AI continues to evolve, its role in the SDLC will expand, helping organizations build software faster and more reliably, ensuring they stay competitive in a fast-paced market.

[dragonfromthewoodlands]

by the way if your software test says "input required information. expected result: information appears" then I'm sorry but I am viciously eviscerating you in my mind

[evabrielle]

Innovative Data Life Cycle Management Solutions in the US | 5Data

In the USA, 5Data Inc simplifies data processes and enhances security with comprehensive data life cycle management services. Visit our website for info

[keploy]

Assertions in Selenium Python: A Complete Guide

Introduction: Why Assertions Matter in Selenium Python

Assertions play a critical role in test automation by ensuring that the application under test behaves as expected during the execution of Selenium tests. They help identify discrepancies between the actual and expected outcomes, providing confidence in the reliability of your application.

What Are Assertions in Selenium Python?

Assertions in Selenium Python are statements that validate the expected output of a test case against its actual result. These validations are essential for verifying whether the application under test meets predefined criteria, making them a cornerstone of automated testing.

Types of Assertions in Selenium Python

Selenium Python supports various types of assertions, each serving a unique purpose in test validation:

Hard Assertions: Stop the execution immediately when an assertion fails. These are ideal for critical validations where subsequent steps depend on the result of the assertion.

Soft Assertions: Allow the test execution to continue even if an assertion fails. These are useful for scenarios where multiple conditions need to be validated independently.

For example, you can use hard assertions to verify a page title and soft assertions to check multiple UI elements on the page.

Commonly Used Assertion Methods in Python's unittest Framework

Python’s unittest framework provides several assertion methods to test various conditions effectively:

assertEqual(): Validate that two values are equal.

assertTrue() and assertFalse(): Check the truthiness of a condition.

assertIn(): Verify that an item exists within a list or string.

Examples:

assertEqual(driver.title, "Home Page"): Confirms that the page title matches "Home Page".

assertTrue(button.is_displayed()): Ensures a button is visible on the page.

assertIn("Welcome", driver.page_source): Checks if the word "Welcome" exists in the page source.

Writing Assertions in Selenium Python Tests

Writing assertions in Selenium Python tests involves combining Selenium commands with Python's assertion methods. Here are two examples:

Validating the Title of a Webpage:

from selenium import webdriver

import unittest

 

class TestTitle(unittest.TestCase):

    def test_title(self):

        driver = webdriver.Chrome()

        driver.get("https://example.com")

        self.assertEqual(driver.title, "Example Domain")

        driver.quit()

Verifying the Presence of an Element:

from selenium.webdriver.common.by import By

 

def test_element_presence(self):

    element = driver.find_element(By.ID, "submit-button")

    self.assertTrue(element.is_displayed())

Handling Assertion Errors

Assertion errors are raised when an assertion fails, and understanding how to handle them is crucial for debugging. Here’s how to interpret assertion errors:

Error Messages: Read the error message carefully to identify the failing assertion and its expected vs. actual values.

Debugging Tips: Use logs, screenshots, or breakpoints to investigate why the assertion failed.

Best Practices for Using Assertions in Selenium Python

To write effective and maintainable Selenium Python tests, follow these best practices:

Keep Assertions Simple: Focus on validating one condition per assertion.

Use Meaningful Messages: Include custom messages for better error reporting, e.g., self.assertEqual(actual, expected, "Page title mismatch").

Avoid Overusing Assertions: Limit the number of assertions in a single test case to maintain clarity and focus.

Integrating Assertions with Test Frameworks

Integrating assertions with popular Python test frameworks like unittest and pytest enhances test readability and efficiency:

Using Assertions in pytest:

def test_example():

    assert "Welcome" in driver.page_source, "Welcome message not found"

Organizing Tests in unittest: Group related assertions into test cases and use setup/teardown methods for efficient resource management.

Common Pitfalls and How to Avoid Them

Misusing assertions can lead to unreliable tests and wasted debugging efforts. Avoid these common pitfalls:

Overcomplicating Assertions: Avoid combining multiple conditions in a single assertion.

Ignoring Assertion Errors: Always investigate and resolve failed assertions instead of ignoring them.

Relying Too Heavily on Soft Assertions: Use soft assertions judiciously to avoid masking critical issues.

Conclusion: Mastering Assertions for Reliable Selenium Tests

Assertions are a vital component of Selenium Python testing, ensuring that your tests are robust, accurate, and efficient. By leveraging assertions effectively, you can identify issues early, improve test reliability, and build confidence in your application’s quality.

[alittleemo]

going to actually lose my shit how is no one in the entire school of architecture competent and useful during a group project is it truly that hard to ask you to do ANYTHING

[in-sightpublishing]

Matthew Scillitani on Machine Learning and Family

Author(s): Scott Douglas Jacobsen Publication (Outlet/Website): The Good Men Project Publication Date (yyyy/mm/dd): 2024/07/23 Matthew Scillitani, member of the Glia Society and Giga Society, is a software engineer living in Cary, North Carolina. He is of Italian and British lineage, and is fluent in English and Dutch (reading and writing). He holds a B.S. in Computer Science and a B.A. in…

[brillioitservices]

The Generative AI Revolution: Transforming Industries with Brillio

The realm of artificial intelligence is experiencing a paradigm shift with the emergence of generative AI. Unlike traditional AI models focused on analyzing existing data, generative AI takes a leap forward by creating entirely new content. The generative ai technology unlocks a future brimming with possibilities across diverse industries. Let's read about the transformative power of generative AI in various sectors: 

1. Healthcare Industry: 

AI for Network Optimization: Generative AI can optimize healthcare networks by predicting patient flow, resource allocation, etc. This translates to streamlined operations, improved efficiency, and potentially reduced wait times. 

Generative AI for Life Sciences & Pharma: Imagine accelerating drug discovery by generating new molecule structures with desired properties. Generative AI can analyze vast datasets to identify potential drug candidates, saving valuable time and resources in the pharmaceutical research and development process. 

Patient Experience Redefined: Generative AI can personalize patient communication and education. Imagine chatbots that provide tailored guidance based on a patient's medical history or generate realistic simulations for medical training. 

Future of AI in Healthcare: Generative AI has the potential to revolutionize disease diagnosis and treatment plans by creating synthetic patient data for anonymized medical research and personalized drug development based on individual genetic profiles. 

2. Retail Industry: 

Advanced Analytics with Generative AI: Retailers can leverage generative AI to analyze customer behavior and predict future trends. This allows for targeted marketing campaigns, optimized product placement based on customer preferences, and even the generation of personalized product recommendations. 

AI Retail Merchandising: Imagine creating a virtual storefront that dynamically adjusts based on customer demographics and real-time buying patterns. Generative AI can optimize product assortments, recommend complementary items, and predict optimal pricing strategies. 

Demystifying Customer Experience: Generative AI can analyze customer feedback and social media data to identify emerging trends and potential areas of improvement in the customer journey. This empowers retailers to take proactive steps to enhance customer satisfaction and loyalty. 

3. Finance Industry: 

Generative AI in Banking: Generative AI can streamline loan application processes by automatically generating personalized loan offers and risk assessments. This reduces processing time and improves customer service efficiency. 

4. Technology Industry: 

Generative AI for Software Testing: Imagine automating the creation of large-scale test datasets for various software functionalities. Generative AI can expedite the testing process, identify potential vulnerabilities more effectively, and contribute to faster software releases. 

Generative AI for Hi-Tech: This technology can accelerate innovation in various high-tech fields by creating novel designs for microchips, materials, or even generating code snippets to enhance existing software functionalities. 

Generative AI for Telecom: Generative AI can optimize network performance by predicting potential obstruction and generating data patterns to simulate network traffic scenarios. This allows telecom companies to proactively maintain and improve network efficiency. 

5. Generative AI Beyond Industries: 

GenAI Powered Search Engine: Imagine a search engine that understands context and intent, generating relevant and personalized results tailored to your specific needs. This eliminates the need to sift through mountains of irrelevant information, enhancing the overall search experience. 

Product Engineering with Generative AI: Design teams can leverage generative AI to create new product prototypes, explore innovative design possibilities, and accelerate the product development cycle. 

Machine Learning with Generative AI: Generative AI can be used to create synthetic training data for machine learning models, leading to improved accuracy and enhanced efficiency. 

Global Data Studio with Generative AI: Imagine generating realistic and anonymized datasets for data analysis purposes. This empowers researchers, businesses, and organizations to unlock insights from data while preserving privacy. 

6. Learning & Development with Generative AI: 

L&D Shares with Generative AI: This technology can create realistic simulations and personalized training modules tailored to individual learning styles and skill gaps. Generative AI can personalize the learning experience, fostering deeper engagement and knowledge retention. 

HFS Generative AI: Generative AI can be used to personalize learning experiences for employees in the human resources and financial services sector. This technology can create tailored training programs for onboarding, compliance training, and skill development. 

7. Generative AI for AIOps: 

AIOps (Artificial Intelligence for IT Operations) utilizes AI to automate and optimize IT infrastructure management. Generative AI can further enhance this process by predicting potential IT issues before they occur, generating synthetic data for simulating scenarios, and optimizing remediation strategies. 

Conclusion: 

The potential of generative AI is vast, with its applications continuously expanding across industries. As research and development progress, we can expect even more groundbreaking advancements that will reshape the way we live, work, and interact with technology. 

Reference- https://articlescad.com/the-generative-ai-revolution-transforming-industries-with-brillio-231268.html 

[amplework]

Software Testing and Quality Assurance Services | Amplework

Amplework provides top-notch software testing and quality assurance (QA) services. Our experienced team ensures comprehensive testing strategies to identify and resolve issues, ensuring a seamless user experience. Enhance the quality and reliability of your software with our trusted QA services.

[advanto-software]

10 Years of Excellence: Empowering Careers with Advanced Courses!

Explore the Future of Tech with Our Full stack Java, Software Testing, and Data Analytics Programs. Ready to Elevate Your Skills? Call Now to Get Your Seat.

[vtusoft]

How Generative AI in Software Testing is Revolutionizing Quality Assurance

The Need for AI-Driven Software Testing Software testing is a critical phase in the development lifecycle, ensuring that applications function as expected before deployment. However, traditional testing methods face several challenges, including manual errors, slow execution, and high costs. Software Development Life Cycle with Generative AI enhances testing by automating repetitive tasks,…