Let’s be honest, code reviews can be tedious. You open a pull request, scroll through hundreds of lines, spot a couple of obvious issues, and approve. Then, two weeks later, a bug shows up in production… and it’s traced back to a line you skimmed over.

Sound familiar?

That’s exactly why I decided to try Qodo AI, an AI-powered code review assistant that promises to help you review smarter, not harder. And after two months of using, I can confidently say: Qodo AI is a game-changer.

This blog walks you through:

•  What Qodo AI is
•  How to use it effectively

What is Qodo AI?

Qodo AI is an intelligent assistant that plugs into your Git workflows and automatically reviews pull requests. But it’s not just a smarter linter. It goes beyond style rules and dives deep into:

• Bug-prone patterns
•  Missed test coverage
•  Security vulnerabilities
•  Code cleanup and refactoring suggestions
•  Best practice recommendations

It’s like having a seasoned engineer on your team who never gets tired, misses nothing, always explains why something needs fixing.

How to Use Qodo AI (Step-by-Step)

1. Connect Your Repo

Qodo integrates seamlessly with GitHub, GitLab, and Bitbucket. I connected it to my GitHub repo in under 5 minutes.

You can configure it to scan all branches or just specific PRs.

2. Open a PR and Watch It Work

Once a pull request is opened, Qodo jumps into action. It adds inline comments and a summary report—highlighting issues, test gaps, and suggestions.

3. Act on Insights, Collaboratively

What sets Qodo apart is how clear and contextual its suggestions are. You don’t get vague warnings—you get:

• A plain-language explanation
• The rationale behind the issue
• Suggested fixes or safer alternatives

Our developers started using Qodo’s comments as mini code lessons. Juniors appreciated the clarity; seniors appreciated the time saved.

Final Thoughts: Qodo AI is the Real Deal

We often talk about tools that “shift left” or “boost developer productivity.” Qodo AI delivers on both fronts. It’s a powerful, practical, and collaborative solution for any dev or QA team that wants to ship better code, faster.

Would I recommend it?

Absolutely—especially if:

• You want faster, smarter code reviews
• You care about test coverage and edge cases
• You’re working in a security-conscious industry
• You mentor junior developers or maintain high-stakes codebases

Qodo AI didn’t replace our code review process. It enhanced it.

Ready to give it a shot?
Visit https://qodo.ai to explore more. Let me know in comments if you try it—or already have.

Meet Sean Brundle, Lead Technical Consultant, Sitecore 

Sean’s dedication to excellence and passion for continuous growth have defined his 10-year journey at Perficient, culminating in his recent promotion to Lead Technical Consultant. As a remarkable people leader, his commitment to professional development and mentoring his team across diverse technologies exemplifies Perficient’s promise to challenge, champion, and celebrate every colleague.  

Sean supports a broad range of clients within Perficient’s Customer Experience Platform (CXP) Managed Services department—primarily in the Sitecore business unit (BU)—by monitoring infrastructure applications, addressing performance issues, tracking latency, and maintaining robust security. Through these efforts, he delivers top-tier applications, expert recommendations, and security solutions that ensure clients maintain fast, secure websites with maximum reliability and minimal downtime. 

Continue reading to discover how Sean’s proactive client approach and obsession over outcomes have driven growth for Perficient’s DevOps practice.  

READ MORE: Perficient’s Customer Experience Expertise 

Sean’s Early Career Journey

Sean began his career in 2015 as an IT Specialist Intern at a marketing technology company and quickly advanced the following year to Project Specialist, building content in Sitecore and conducting in-depth content analysis.  

In 2017, he transitioned to the technical side of the business. As a Technical Quality Assurance (QA) Specialist, he sharpened his QA expertise by driving automation projects and pioneering numerous new processes within the department. Sean’s enthusiasm for software development and IT converged in 2018, when the company launched its DevOps department and entrusted him with the role of Junior DevOps Engineer. In this position, Sean played a key role in architecting many of the core standards and operational processes that continue to underpin Perficient’s DevOps practice today.  

Establishing a CXP Managed Services Department 

Sean’s passion for DevOps reached a defining moment in 2019 when he was promoted to DevOps Engineer. By anticipating client needs and fostering collaboration within his team, Sean led the creation and expansion of the Managed Services department, which later evolved into Perficient’s CXP Managed Services department. This milestone marked a significant turning point in Sean’s career and stands as one of his proudest achievements.  

“While supporting our clients, we noticed recurring challenges with some hosting providers. These experiences highlighted an opportunity for us to create our own Managed Services department. I helped lead that initiative and created the different tools and processes we use today. We started off with two or three clients and now have over 10 different clients expanding to various platforms. It was a cool initiative that I was able to have a major hand in leading.” 

Driven by a genuine commitment to people-first leadership, Sean’s forward-thinking approach has been pivotal in delivering greater value—empowering both colleagues and clients to accelerate growth and achieve meaningful results.  

“Our team is highly proactive. We focus on making recommendations and helping clients make their applications much better and faster before difficulties arise. We’ve spent a lot of time analyzing different client systems, and I’ve implemented specific processes and tooling that accelerate our work and identify issues the client might not even be aware of.”  

Building Expertise, Strengthening Client Engagement, and Leading with Purpose

Sean joined Perficient as a Technical Consultant through an acquisition in 2020 and quickly advanced to Senior Technical Consultant by 2022. Now serving as a Lead Technical Consultant, he works closely with the Managed Services team to deliver proactive client support, leveraging his expertise to inform tooling and offerings that optimize application and infrastructure development.  

Sean stays ahead in the fast-changing digital world by actively working with different platforms and technologies, continuously learning emerging best practices, and reading up on the latest innovations. Motivated by a results-driven mindset and devotion to client success, he has built lasting relationships through consistent delivery excellence and continues to shatter boundaries with cutting-edge solutions.  

“Identifying performance gaps, presenting those insights to the client, implementing solutions, and then demonstrating the impressive speed improvements we’ve achieved—that’s incredibly rewarding. Seeing the client’s excitement energizes my team and me, and it motivates me to keep enhancing their offerings. This ongoing effort helps strengthen client relationships.”  

Sean’s people-first leadership shines through his collaborative work with colleagues and clients. Anchored by more than a decade of IT industry experience, he has become a trusted advisor and influential mentor. 

Empowering Teams and Clients Through Strategic Leadership  

Sean has continuously advanced his technical knowledge by working with diverse clients and technologies, while also expanding his credentials with certifications such as Microsoft Azure Administrator Associate, Microsoft Azure DevOps Engineer Expert, and AWS Certified Solutions Architect.  

READ MORE: Accelerating Professional Growth Through Certifications  

Alongside his professional development, Sean has deepened his client engagement and relationship-building skills. He leads quarterly, in-depth reviews of his team’s progress and client successes, delivering strategic presentations that highlight untapped tooling opportunities—driving measurable value and strengthening long-term client relationships. Additionally, Sean completed Perficient’s Consultant Curriculum program, where he acquired strategies for effectively identifying and addressing client needs. 

READ MORE: Learn About Perficient’s Award-Winning Growth for Everyone Programming 

Sean has developed outstanding leadership skills that inspire collaboration, enhance team dynamics, and deliver impactful results. He embodies true team spirit by empowering individuals to grow and excel, driving collective success with passion and purpose.

“At Perficient, my role has given me a lot of opportunities to lead and mentor other engineers. I’ve really taken on that role and enjoy uplifting team members. Sharing knowledge, helping to support them, and seeing them grow has been really exciting. Focusing on helping my team rather than just myself benefits the entire team and department.”  

In mentoring his colleagues, Sean champions open communication as the foundation for building trust, setting clear expectations, and fostering meaningful learning experiences. 

“I think that listening and having clear communication has been valuable in my leadership growth. When assigning a task to someone you’re mentoring, it’s important to follow up, but not be too strict. Providing clear steps, setting actionable goals within a certain timeframe, maintaining consistent communication, and helping them when they get stuck makes a difference. I’ve noticed many team members respond well to this approach.”  

Sean’s empathetic leadership naturally promotes transparency across teams and time zones, driving seamless global collaboration. He works regularly with Perficient’s Nagpur office to monitor different applications and infrastructures, gaining cutting-edge insights from diverse multicultural perspectives.  

“There are times when I’m working with a team of colleagues who all come from different cultural backgrounds. This influences how they communicate or approach certain tasks. Being able to adapt to these differences and learn from them has significantly helped my growth as a leader.”

LEARN MORE: Perficient’s Global Footprint Enables Genuine Connection  

Unlocking Potential Through Shared Knowledge and Cross-Functional Collaboration 

Within Perficient’s Sitecore BU, Sean’s team fosters continuous learning through a dedicated Confluence platform for role-specific skills and monthly meetings to discuss new technologies and processes. Sean takes great pride in Perficient’s broader culture of cross-functional collaboration. 

“We have a lot of different BUs at Perficient, and they’re able to work together to support each other. Perficient colleagues are great about coming together as a team, supporting everyone, and enabling knowledge sharing that benefits both our team and our clients. I think it’s a big benefit with Perficient.” 

Charting New Waters Through Relentless Innovation 

Sean shatters boundaries and drives excellence through continuous innovation, fueling both individual and team success. 

“I try to push my team and myself to constantly seek better ways to serve our clients and improve ourselves—exploring better approaches, speaking up, and testing ideas we haven’t tried before. I strive to do this as often as I can.”  

While Sean champions continuous growth, he also emphasizes the value of experiential learning and resilience in the face of setbacks.

“Do not be afraid to fail. If you take on a new role or responsibility—even if you make mistakes— you will grow and learn. I’m lucky to have a lot of responsibility, and there are times when I have to learn from my mistakes, but it makes me much stronger and a better consultant engineer.” 

Just as Sean explores new technologies with curiosity and determination, he embraces the wonders of nature—reaching new heights through rock climbing and kayaking while cherishing time outdoors with his son. 

“Outside of work, I try to spend as much time with my son as I can. He’s almost 2 years old and loves to play outside, so we try to spend as much time outdoors as possible. I go rock climbing occasionally, and I also love being on the water. If I get the chance, I try to take out our kayaks. More than anything, I focus on spending quality time with my son and enjoying the outdoors as much as we can together.” 

MORE ON GROWTH FOR EVERYONE  

Perficient continually looks for ways to champion and challenge our workforce, encourage personal and professional growth, and celebrate the unique culture created by the ambitious, brilliant, people-oriented team we have cultivated.  These are their stories. 

Learn more about what it’s like to work at Perficient on our Careers page. Connect with us on  LinkedIn here. 

After highlighting the key benefits of the AI Assistant for enterprise analytics in my previous blog post, I am sharing here a demo of what it looks like to use the AI Assistant. The video below demonstrates how a persona interested in understanding enterprise projects may quickly  find answers to their typical everyday questions. The information requested includes profitability, project analysis, cost management, and timecard reporting.
A Perficient Demo of AI Assistant for Project Analytics

What to Watch Out For

With the right upfront configuration in place, the AI assistant, native to Oracle Analytics, can transform how various levels of the workforce find the insights they need to be successful in their tasks. Here are a few things that make a difference when configuring the AI Assistant.

• Multiple Subject Areas: When enterprise data consists of several subject areas, for example Projects, Receivables, Payables, Procurement, etc., performing Q&A with the AI Assistant across multiple subject areas simultaneously is not currently possible. What the AI Assistant does in this situation is prompt for the subject area to use for the response. That is not an issue when the information requested is from a single subject area. However, there are situations when we want to simultaneously gain insights across two or more subject areas. This can be handled by preparing a combined subject area that contains the key relevant information from other underlying subject areas. As a result, the AI Assistant interfaces with a single subject area that consists of all the transaction facts and conformed dimensions across the various transactional data sets. With a little semantic model adjustments this is an achievable solution.
• Be selective on what is included in AI prompts: Enterprise semantic models typically have a lot of information that may not be relevant for an AI chat interface. Therefore, excluding any fields from being included in an AI prompt improves performance, accuracy, and sometimes even reduces the processing cost incurred by AI when leveraging external LLMs. Dimension codes, identifiers, keys, and audit columns are some examples of things to exclude. The Oracle Analytics AI Assistant comes with a fine-grained configuration that enables selecting the fields to include in AI prompts.
• Metadata Enrichment with Synonyms: Use synonyms on ambiguous fields, for example to clarify what a date field represents (Is it the transaction creation date or the date it was invoiced on?). Another example of when synonyms are useful is when there is a need to enable proper interpretation of internal organization-specific terms. The AI Assistant enables setting up synonyms on individual columns to improve it’s level of understanding.
• Indexing Data: For an enhanced user experience, I recommend identifying which data elements are worth indexing. This means the AI LLM will be made aware of the information stored in these fields that you chose while setting up the AI Assistant. This is an upfront one-time activity. The more information you equip the AI Assistant with, the smarter it gets when interpreting and responding to questions.

For guidance on how to get started with enabling GenAI for your enterprise data analytics, reach out to mazen.manasseh@perficient.com.

What is VBA?

Visual Basic for Applications (VBA) is a programming language developed by Microsoft. Microsoft Office applications like Excel, Word, and Access primarily use VBA to automate repetitive tasks. VBA is a programming language that automates tasks in Microsoft Office applications, especially Excel.

Types of VBA Macros

VBA macros are custom scripts created to automate tasks and improve efficiency within Microsoft Office applications. The types of VBA macros vary in functionality, ranging from simple recorded macros to complex event-driven scripts. Here’s a breakdown of the most commonly used types of VBA macros:

VBA (Visual Basic for Applications) categorizes macros based on their functionality and the events that trigger them. Here are the main types of macros:

A visually appealing infographic showcasing VBA (Visual Basic for Applications) and its different types of macros.

1. Recorded Macros

• Description: A sequence of actions carried out within an Office application is recorded to create these macros. VBA translates these actions into code automatically.
• Use Case: Great for automating repetitive tasks without manually writing code.
• Example: Automatically applying consistent formatting to a set of worksheets in Excel.

Learn more about how to record macros in Excel.

2. Custom-Coded Macros

• Description: These are manually written scripts that perform specific tasks. They offer more flexibility and functionality than recorded macros.
• Use Case: Useful for complex tasks that require conditional logic, loops, or interaction between multiple Office applications.
• Example: Generating customized reports and automating email notifications from Outlook based on Excel data.

3. Event-Driven Macros

• Description: These macros run automatically in response to specific events, such as opening a document, saving a file, or clicking a button.
• Use Case: Used for automating tasks that should happen automatically when a certain event occurs.
• Example: Automatically updating a timestamp in a cell every time a worksheet is modified.

4. User-Defined Functions (UDFs)

• Description: These are custom functions created using VBA that can be used just like built-in functions in Excel formulas.
• Use Case: Ideal for creating reusable calculations or functions unavailable in Excel by default.
• Example: Creating a custom function to calculate a specific financial metric.

5. Macro Modules

• Description: A module is a container for VBA code, which can include multiple macros, functions, and subroutines. Related macros can be grouped together and organized using these.
• Use Case: Useful for keeping code organized, especially in large projects.
• Example: Group all macros related to data processing in one module and all macros associated with reporting in another.

Each type of macro serves a distinct function and suits specific tasks, depending on the requirements. Use these macros actively based on your needs to achieve the best results.

Conclusion

VBA allows you to automate operations and increase productivity in Microsoft Office programs. Understanding the various sorts of macros helps you select the best strategy for your requirements. Whether you are recording activities, building custom scripts, or creating event-driven automated processes, knowing the options can guide your decision. Moreover, this knowledge ensures you choose the most efficient approach for your tasks. Additionally, using the right type of macro can significantly improve your productivity and streamline your workflow. Begin learning VBA to achieve new levels of efficiency in your workflows.

Happy reading and automating!

When automating browser tests with Selenium and Pytest, it’s common to run into challenges. Selenium is a powerful tool, but it can be tricky to troubleshoot and debug. Whether you’re encountering timeouts, stale elements, or incorrect results, understanding how to identify and resolve common issues is essential.

In this blog, we’ll walk through some common pitfalls when using Selenium with Pytest and share practical solutions to help you debug your tests effectively.

• Element Not Found / NoSuchElementException:
  One of the most frequent errors when using Selenium is the NoSuchElementException, which occurs when Selenium cannot locate an element on the page. This usually happens if:
  • • The element is not present yet (e.g., it loads dynamically).
    • The selector is incorrect or out-of-date.
  • • The element is in a different frame or window.
• 
  Solution:
  To resolve this, you can use Explicit Waits to ensure the element is present before interacting with it. Selenium provides the WebDriverWait method, which waits for a specific condition to be met (e.g., an element to become visible or clickable).

• Example:

  from selenium.webdriver.common.by import By
  from selenium.webdriver.support.ui import WebDriverWait
  from selenium.webdriver.support import expected_conditions as EC
  
  # Wait for the element to be visible
  wait = WebDriverWait(driver, 10)  # Wait for up to 10 seconds
  element = wait.until(EC.visibility_of_element_located((By.ID, "myElement")))
  element.click()
  

  This will wait for the element to appear within 10 seconds before trying to interact with it.

• StaleElementReferenceException: The StaleElementReferenceException occurs when you try to interact with an element that is no longer part of the DOM. This can happen if the page is reloaded, or the element gets removed and recreated.
• 
  Solution: To solve this issue, simply re-locate the element before interacting with it again. Using an explicit wait before interacting with the element is also a good practice.

• Example:

  # First locate the element 
  element = driver.find_element(By.ID, "myElement") 
  
  # Perform an action 
  element.click() 
  
  # If the page is updated, re-locate the element 
  element = driver.find_element(By.ID, "myElement") 
  element.click()

   

• Timeouts (Element Not Interactable): Timeout errors often occur when Selenium takes longer than expected to find or interact with an element. For example, trying to click an element before it’s fully loaded, or interacting with an element that’s hidden
• 
  Solution: Using explicit waits as shown in the first example will help here. But you should also ensure that the element is interactable (visible and enabled) before performing any action on it
• .
• Example:

  wait = WebDriverWait(driver, 10)
  element = wait.until(EC.element_to_be_clickable((By.ID, "submitButton")))
  element.click()
  

  In this case, element_to_be_clickable ensures that the button is not only present but also interactable (i.e., visible and enabled).

• Wrong Browser Version or Compatibility Issues: Sometimes tests may pass on one browser but fail on another. This is especially common with cross-browser testing.
  Solution: Make sure you’re using the correct browser drivers (e.g., ChromeDriver for Chrome, GeckoDriver for Firefox) that are compatible with the version of the browser you are testing. Also, check if the issue is specific to the browser’s rendering engine.If you’re running tests across multiple browsers, using a cloud testing service like BrowserStack or Sauce Labs is a good way to avoid browser setup issues and test on real environments.

• Logging and Capturing Errors Another issue is tracking and logging errors effectively during the test execution. If you don’t capture logs, it can be hard to identify what went wrong in case of test failure.
• 
  Solution: Incorporating logging within your test can help you keep track of actions and errors, making it easier to identify issues.

• Example:

• import logging
  
  # Set up logging
  logging.basicConfig(level=logging.INFO)
  
  def test_login(driver):
      logging.info("Opening login page")
      driver.get("https://example.com/login")
  
      logging.info("Filling in login credentials")
      driver.find_element(By.ID, "username").send_keys("user")
      driver.find_element(By.ID, "password").send_keys("pass")
  
      logging.info("Submitting the form")
      driver.find_element(By.ID, "submit").click()
  
      logging.info("Verifying login success")
      assert "Welcome" in driver.page_source
  

  You can view the log output to trace the sequence of events in case a failure occurs.

• Pytest Assertion Errors: Another common issue is assertion errors when the expected value does not match the actual value returned by the test.
• 
  Solution: When you’re running tests with Pytest, ensure that your assertions are meaningful and validate what you really care about. Sometimes, comparing strings or numbers directly may lead to errors if the values have different formats.

• Example:

• def test_title(driver):
      driver.get("https://example.com")
      assert driver.title == "Expected Title", f"Expected 'Expected Title' but got {driver.title}"
  

  This assertion helps ensure that the test fails gracefully, providing helpful error messages to debug.

• Pytest Markers and Test CategorizationWhen you have a large test suite, running all tests every time can slow down development. Using Pytest markers to categorize tests (e.g., @pytest.mark.smoke) can help you run only relevant tests, making debugging easier..
• 
  Solution: Use markers to tag tests for different categories, such as smoke tests, regression tests, etc.

• Example:

  import pytest
  
  @pytest.mark.smoke
  def test_login(driver):
      driver.get("https://example.com/login")
      assert "Login" in driver.title
  
  @pytest.mark.regression
  def test_logout(driver):
      driver.get("https://example.com/logout")
      assert "Logout Successful" in driver.page_source
  

  Then run only smoke tests or regression tests by specifying the marker:
  pytest -m smoke

Conclusion

Debugging Selenium tests with Pytest can be tricky, but by understanding common pitfalls and applying simple solutions, you can save time and improve test reliability. Here’s a quick recap of what we covered:

• Use explicit waits to handle dynamic elements and timeouts.
• Re-locate elements if you run into StaleElementReferenceException.
• Ensure elements are interactable before clicking.
• Use logging to track the flow and errors in your tests.
• Leverage Pytest markers to run relevant tests and make debugging easier.

By following these best practices, you’ll become more effective at identifying and resolving issues in your Selenium tests. Happy debugging!

Introduction

Flaky tests—those that fail intermittently—are a common headache for test automation teams. They can be especially frustrating in Selenium tests because of the dynamic nature of web applications. Elements might take time to load, page navigation could be slow, or JavaScript-heavy applications might delay interactions. These issues lead to false negatives in tests, where tests fail even though the application works fine.

In this blog, we’ll explore how to use Pytest retries and explicit/implicit waits to improve the stability of your Selenium tests and reduce flaky test failures.

Why Selenium Tests Are Flaky

Flaky tests typically fail due to the following issues:

• Dynamic Content: Elements that take time to load (like AJAX content) or slow-rendering pages.
• Network Issues: Delays or failures in loading resources or API calls.
• Timing Issues: Trying to interact with elements before they’re fully loaded or ready.

The key to reducing flaky tests lies in two techniques: retries and waits.

Using Pytest Retries for Flaky Tests with pytest-rerunfailures

A simple solution to mitigate flaky tests is to retry failed tests a certain number of times. The pytest-rerunfailures plugin allows you to automatically rerun tests that fail, thus reducing the impact of intermittent failures.

1. Installation: Install the pytest-rerunfailures plugin:

   bash
   pip install pytest-rerunfailures
   

2. Configuration: To enable retries, use the –reruns option when running your tests. For example, to retry a failed test 3 times, run:

   bash
   pytest --reruns 3
   

   You can also set the number of retries in your pytest.ini configuration file:

   ini
   
   [pytest]
   
   reruns = 3
   
   rerunsDelay = 2  #Delay between retries in seconds

    

3. Example of Retries: Let’s say you have a test that clicks a button to submit a form. Sometimes, due to timing issues, the button might not be clickable. By adding retries, the test will automatically retry if the failure is due to a transient issue.

   def test_submit_button(mocker):
       # Simulate flaky behavior
       mocker.patch('selenium.webdriver.common.by.By.ID', return_value='submit')
       # Trigger a click action on the button
       button = driver.find_element_by_id('submit')
       button.click()
       assert button.is_enabled()  # Check button state
   

Using Waits to Ensure Elements Are Ready

In Selenium, waits are crucial to ensure that the elements you want to interact with are available and ready. There are two types of waits: implicit and explicit.

1. Implicit Waits: Implicit waits instruct the WebDriver to wait a certain amount of time for elements to appear before throwing an exception.

   driver.implicitly_wait(10)  # Waits for 10 seconds for elements to load

   While easy to use, implicit waits can sometimes slow down tests and make debugging more difficult because they apply globally to all elements.

2. Explicit Waits: Explicit waits are more powerful and precise. They allow you to wait for specific conditions before proceeding with interactions. WebDriverWait combined with expected conditions is commonly used.Example: Wait for an element to be clickable before clicking on it:

   from selenium.webdriver.support.ui import WebDriverWait
   from selenium.webdriver.support import expected_conditions as EC
   from selenium.webdriver.common.by import By
   
   wait = WebDriverWait(driver, 10)
   element = wait.until(EC.element_to_be_clickable((By.ID, "submit_button")))
   element.click()
   

3. Using Waits for AJAX Content: Often, web pages use AJAX to load content dynamically. Explicit waits are perfect for waiting until AJAX calls finish loading.

   # Wait until the AJAX content is visible
   wait.until(EC.visibility_of_element_located((By.ID, "ajax-content")))
   

Best Practices for Waits and Retries

• Use explicit waits for better control: Explicit waits allow you to wait for specific conditions (like visibility or clickability), improving test reliability.
• Combine retries with waits: Ensure that retries are only triggered after sufficient wait time to account for potential page load or element rendering delays.
• Optimize test timing: Use waits for specific elements rather than using global implicit waits, which can slow down tests.

Conclusion

By using Pytest retries and explicit/implicit waits, you can significantly improve the stability of your Selenium tests. Retries help handle intermittent failures, while waits ensure that elements are ready before interacting with them. Together, these strategies reduce flaky test results, making your test suite more reliable and consistent. Happy Testing!

The relationship between Quality Assurance (QA) and Software Development teams is often marked by tension and conflicting priorities. But what if this friction could be the spark that ignites innovation and leads to unbreakable products? 

The Power of Productive Tension 

It’s no secret that QA and Development teams sometimes clash. QA and testing professionals are tasked with finding flaws and ensuring stability, while developers are focused on building features, focusing on speed and innovation. This natural tension, however, can be a powerful force when channeled correctly. 

 One of the key challenges in harnessing this synergy is breaking down the traditional silos between QA and Development and aligning teams early in the development process. 

1. Shared Goals: Align both teams around common objectives that prioritize both quality and innovation.
2. Cross-Functional Teams: Encourage collaboration by integrating QA professionals into development sprints from the start.
3. Continuous Feedback: Implement systems that allow for rapid, ongoing communication between teams.

 Leveraging Automation and AI 

Automation and artificial intelligence are playing an increasingly crucial role in bridging the gap between QA and Software Development Teams: 

1. Automated Testing: Frees up QA teams to focus on more complex, exploratory testing scenarios.
2. AI-Powered Analysis: Helps identify patterns and potential issues that human testers might miss.
3. Predictive Quality Assurance: Uses machine learning to anticipate potential bugs before they even occur.

 Best Practices  

Achieving true synergy between QA and Development isn’t always easy, but it’s well worth the effort. Here are some best practices to keep in mind: 

1. Encourage Open Communication: Create an environment where team members feel comfortable sharing ideas and concerns early and often.
2. Celebrate Collaborative Wins: Recognize and reward instances where QA-Dev cooperation leads to significant improvements.
3. Continuous Learning: Invest in training programs that help both teams understand each other’s perspectives and challenges.
4. Embrace Failure as a Learning Opportunity: Use setbacks as a chance to improve processes and strengthen the relationship between teams.

As business leaders are tasked with doing more with less, the relationship between QA and Development will only become more crucial. By embracing the productive tension between these teams and implementing strategies to foster collaboration, organizations can unlock new levels of innovation and product quality. 

Are you ready to turn your development and testing friction into a strategic advantage?

Software development requires testing as an essential component. It ensures that your code operates as intended and finds errors before being used in production. Test writing, meanwhile, can occasionally seem like a tedious task. Fortunately, this procedure may be accelerated and improved upon by using GitHub Copilot, an AI-powered coding assistant. In this blog post, we’ll look at how to create tests for your code and optimize your development process with GitHub Copilot.

What is GitHub Copilot?

GitHub Copilot is an intelligent code suggestion tool created by GitHub and OpenAI to help with code authoring. It can assist you with writing boilerplate code, intricate algorithms, and—most significantly for this blog—creating test cases for your functions and procedures.

Getting Started with GitHub Copilot

1. Obtain GitHub Copilot: GitHub Copilot is available through a subscription. After obtaining a subscription, ensure your chosen code editor has it installed and activated.
2. Install the Extension: In the Extensions view, the GitHub Copilot extension is available for Visual Studio Code users. Just type “GitHub Copilot” into the search bar and select “Install.”

Creating Tests with GitHub Copilot

Open Your Codebase

Open your project in Visual Studio Code or any other suitable code editor. Ensure you know where your test files are going and that your code is arranged correctly. In the GitHub Copilot Chat, you can also ask the copilot to write down a code for you.

The chat window looks like this:

Start Writing Code

Suppose you have a function like the one below in Python, which validates the password:

Generate Test Files

• Create a Test File: In your project directory, create a new file for your tests. For example, you might create test_passChecker.py if you’re working with Python.
• Ask Copilot to generate a Test Function: Begin by writing a basic test function or simply saying, “Hey, write down a test for the password checker.”

Leverage Copilot for Test Generation

• Start Typing: GitHub Copilot will suggest additional test cases as you type your test function. These might include edge cases or different types of input.
• Review Suggestions: Copilot’s suggestions will appear in a dropdown or inline in your editor. You can accept a suggestion by clicking on it or pressing Tab. Review these suggestions to ensure they align with what you want to test.

Refine and Customize

• Adjust Test Cases: Sometimes, the generated tests might require adjustments. Feel free to modify them to better fit your specific use cases or to enhance coverage.
• Add More Complex Tests: For more complex functions, you might need to write custom test cases. Use Copilot’s suggestions as a starting point and build upon them.

Run Your Tests

Once you’ve generated and refined your test cases, it’s time to run them. You can use pytest or unit test to execute your tests and verify that everything works as expected.

Benefits of Using GitHub Copilot for Testing

• Efficiency: Quickly generate multiple test cases without manually writing each one. This speeds up your testing process and reduces repetitive work.
• Coverage: Get suggestions for a variety of test scenarios, including edge cases that you might not have considered. This helps improve the robustness of your tests.
• Learning Aid: For beginners, Copilot can be used as a learning tool by showing the standard format of tests and offering recommended practices.

While GitHub Copilot is a powerful tool for generating code and tests, it’s important to be aware of some of the potential downsides. Here’s a breakdown of the cons of using GitHub Copilot:

• Code Quality and Reliability: GitHub Copilot generates code based on patterns it has learned from existing codebases. This means the code it suggests might not always follow best practices or be free from bugs. Make sure to check and test the code that is generated thoroughly.
• Over-Reliance: Relying heavily on Copilot might hinder your growth as a developer. It’s important to understand how code works and be able to write it from scratch, as this will deepen your problem-solving skills and understanding of programming concepts.
• Cost and Accessibility: GitHub Copilot is a subscription service. Some developers or organizations may be concerned about the expense, particularly if they have a limited budget. The free version of GitHub Copilot has limits, and you may need to purchase a subscription to utilize all of its capabilities.

Ultimately, GitHub Copilot should be used in addition to your coding procedures, not as a substitute for them. By combining its recommendations with your knowledge and meticulous review procedures, you may maximize its benefits and minimize any problems.

GitHub Copilot is a powerful tool for your testing process. You may write and maintain tests more quickly by utilizing its AI-driven recommendations, freeing up time to concentrate on coding rather than tedious activities. You can successfully include GitHub Copilot in your development process and improve the quality of your software by following the instructions in this guide.

Data Warehouse/ETL Testing

Data warehouse testing is a process of verifying data loaded in a data warehouse to ensure the data meets the business requirements. This is done by certifying data transformations, integrations, execution, and scheduling order of various data processes.

Extract, transform, and load (ETL) Testing is the process of verifying the combined data from multiple sources into a large, central repository called a data warehouse.

Conventional Testing tools are designed for UI-based applications, whereas a data warehouse testing tool is purposefully built for data-centric systems and designed to automate data warehouse testing and generating results. It is also used during the development phase of DWH.

iCEDQ

Integrity Check Engine For Data Quality (iCEDQ) is one of the tools used for data warehouse testing which aims to overcome some of the challenges associated with conventional methods of data warehouse testing, such as manual testing, time-consuming processes, and the potential for human error.

It is an Automation Platform with a rules-based auditing approach enabling organizations to automate various test strategies like ETL Testing, Data Migration Testing, Big Data Testing, BI Testing, and Production Data Monitoring.

It tests data transformation processes and ensures compliance with business rules in a Data Warehouse.

Qualities of iCEDQ

Let us see some of the traits where testing extends its uses.

Automation

It is a data testing and monitoring platform for all sizes of files and databases. It automates ETL Testing and helps maintain the sanctity of your data by making sure everything is valid.

Design

It is designed with a greater ability to identify any data issues in and across structured and semi-structured data.

Uniqueness

Testing And Monitoring:

Its unique in-memory engine with support for SQL, Apache Groovy, Java, and APIs allows organizations to implement end-to-end automation for Data Testing and Monitoring.

User Friendly Design:

This tool provides customers an easy way to set up an automated solution for end-to-end testing of their data-centric projects and it provides Email Support to its customers

Supported Platforms:

Mostly widely used by Enterprises and Business Users and used in platforms like Web apps and Windows. Does not support MAC, Android, and IOS.

Execution Speed:

New Big Data Edition test 1.7 Billion rows in less than 2 minutes and Recon Rule with around 20 expressions for 1.7 billion rows in less than 30 minutes.

With a myriad of capabilities, iCEDQ seamlessly empowers users to automate data testing, ensuring versatility and reliability for diverse data-centric projects.

Features:

• Performance Metrics and Dashboard provides a comprehensive overview of system performance and visualizes key metrics for enhanced monitoring and analysis.
• Data Analysis, Test and data quality management ensures the accuracy, reliability, and effectiveness of data within a system.
• Testing approaches such as requirements-based testing and parameterized testing involve passing new parameter values during the execution of rules.
• Move and copy test cases and supports parallel execution.
• The Rule Wizard automatically generates a set of rules through a simple drag-and-drop feature, reducing user effort by almost 90%.
• Highly scalable in-memory engine to evaluate billions of records.
• Connect to Databases, Files, APIs, and BI Reports. Over 50 connectors are available.
• Enables DataOps by allowing integration with any Scheduling, GIT, or DevOps tool.
• Integration with enterprise products like Slack, Jira, ServiceNow, Alation, and Manta.
• Single Sign-On, Advanced RBAC, and Encryption features.
• Use the built-in Dashboard or enterprise reporting tools like Tableau, Power BI, and Qlik to generate reports for deeper insights.
• Deploy anywhere: On-Premises, AWS, Azure, or GCP.

Testing with iCEDQ:

ETL Testing:

There are few data validations and reconciliation the business data and validation can be done in ETL/Big data testing.

• ETL Reconciliation – Bridging the data integrity gap
• Source & Target Data Validation – Ensuring accuracy in the ETL pipeline
• Business Validation & Reconciliation – Aligning data with business rules

Migration Testing:

iCEDQ ensures accuracy by validating all data migrated from the legacy system to the new one.

Production Data Monitoring:

iCEDQ is mainly used for support projects to monitor after migrating to the PROD environment. It continuously monitors ETL jobs and notifies the data issues through a mail trigger.

Why iCEDQ?

Reduces project timeline by 33%, increases test coverage by 200%, and improves productivity by 70%.

In addition to its automation capabilities, iCEDQ offers unparalleled advantages, streamlining data testing processes, enhancing accuracy, and facilitating efficient management of diverse datasets. Moreover, the platform empowers users with comprehensive data quality insights, ensuring robust and reliable Data-Centric project outcomes.

Rule Types:

Users can create different types of rules in iCEDQ to automate the testing of their Data-Centric projects. Each rule performs a different type of test cases for the different datasets.

By leveraging iCEDQ, users can establish diverse rules, enabling testing automation for their Data-Centric projects. Tailoring each rule within the system to execute distinct test cases caters to the specific requirements of different datasets.

iCEDQ System Requirements

iCEDQ’s technical specifications and system requirements to determine if it’s compatible with the operating system and other software.

To successfully deploy iCEDQ, it is essential to consider its system requirements. Notably, the platform demands specific configurations and resources, ensuring optimal performance. Additionally, adherence to these requirements guarantees seamless integration, robust functionality, and efficient utilization of iCEDQ for comprehensive data testing and quality assurance.

Hence, iCEDQ is a powerful Data Mitigation and ETL/Data Warehouse Testing Automation Solution designed to give users total control over how they verify and compare data sets. With iCEDQ, they can build various types of tests or rules for data set validation and comparison.

Resources related to iCEDQ – https://icedq.com/resources

The Importance of Quality Assurance in Software Development

The pressure to deliver software quickly and cost-effectively is among the highest priorities of business leaders. However, recent high-profile tech failures serve as stark reminders of the critical importance of robust Quality Assurance (QA) practices. As a leader in digital consulting, we recognize that cutting corners on testing can lead to far-reaching consequences that extend well beyond temporary service disruptions.

The Ripple Effect of QA Failures

Imagine the early days when every product, from the food we eat to the devices we use, underwent rigorous testing before reaching us. Today, many businesses overlook the critical importance of thorough testing, often skipping essential QA steps to save costs. However, this short-term thinking can lead to long-term failures.

Take the recent outage that caused massive disruptions. A simple, overlooked step in the QA process led to significant downtime and loss. This is a stark reminder of how critical thorough QA is. The impact on your business reputation and bottom line is at stake with every customer interaction, software update, and product launch, and wide-scale disruptions are often preventable with the right measures in place.

When major tech companies experience widespread outages or security breaches, the impacts are often felt globally. Businesses grind to a halt, consumers lose trust, and the financial repercussions can be staggering. These incidents often trace back to a common root cause: insufficient testing and quality assurance.

It’s a tale as old as technology itself – in an attempt to save time or reduce costs, testing is rushed, automated checks are bypassed, or edge cases are ignored. The result? A ticking time bomb of potential failures that can explode at any moment, causing far more damage than the perceived savings ever justified.

The Hidden Costs of Inadequate Testing

Even with automation in place, many businesses don’t look under the hood to see how minor adjustments can significantly boost their speed and efficiency. Automation isn’t a one-time fix; it requires regular tuning to keep up with rapid development cycles. Without investing in QA teams, training, and motivation, companies risk major setbacks. Every product you trust and every service you rely on has undergone some degree of verification before reaching you. QA is indispensable, and investing in it is non-negotiable.

While it’s easy to focus on the immediate costs of a system failure – lost revenue, emergency fixes, and overtime hours – the true impact runs much deeper:

• Customer Trust and Loyalty: In an era where alternatives are just a click away, a single major outage can erode years of carefully built customer relationships.
• Brand Reputation: News of failures spreads quickly, potentially tarnishing a company’s image for years to come.
• Regulatory Scrutiny: Depending on the industry, failures can attract unwanted attention from regulators, leading to fines and increased oversight.
• Employee Morale: Constant firefighting and crisis management take a toll on team morale and can lead to burnout and turnover.
• Opportunity Cost: Resources diverted to crisis management are resources not spent on innovation and growth.

Our Approach to Quality Assurance

At Perficient, we believe that quality assurance is not just a phase in development but a philosophy that should permeate every aspect of the software lifecycle. Our three-step approach to QA ensures comprehensive coverage and minimizes the risk of costly failures:

1. Envision: We start by thoroughly understanding the system’s requirements and potential weak points. This involves meticulous planning, risk assessment, and the design of comprehensive test strategies.
2. Execute: Our execution phase goes beyond simple functionality checks. We employ a mix of automated and manual testing, stress tests, security audits, and user experience evaluations to ensure every aspect of the system performs as expected under various conditions.
3. Optimize: Quality assurance doesn’t end at launch. We continuously monitor, analyze, and refine our testing processes, incorporating lessons learned and adapting to new challenges.

Investing in Quality: A Business Imperative

In an age where digital systems are the backbone of nearly every business operation, treating QA as an afterthought is no longer an option. The initial investment in thorough testing pales in comparison to the potential costs of a major failure.

Consider this: A few extra days or weeks of testing might delay a launch, but a major outage could set a company back months or years in terms of customer trust and market position.

Let’s talk about how we can revolutionize your QA processes. We’re not just here to build it for you; we’re here to help you save money, eliminate unnecessary costs, and ensure that your systems are robust and reliable. By partnering with us, you can optimize your automation, enhance your team’s skills, and ensure faster, more reliable product releases.

 A Call for Quality-First Thinking

As we reflect on recent tech failures, let them serve not as cautionary tales, but as catalysts for change. It’s time for businesses to shift their perspective on quality assurance from a necessary evil to a strategic advantage.

By prioritizing QA and embracing a quality-first mindset, companies can not only avoid costly failures but also differentiate themselves in a crowded market. In the long run, the most successful businesses will be those that recognize quality isn’t just about preventing failures – it’s about building trust, fostering innovation, and delivering exceptional experiences.

At Perficient, we’re committed to helping our clients navigate this critical aspect of digital transformation. By leveraging our expertise and proven methodologies, we ensure that quality is never compromised in the pursuit of progress.

In the world of technology, an ounce of prevention is worth far more than a pound of cure. Invest in quality today, and reap the rewards of reliability, customer loyalty, and sustainable growth tomorrow. Reach out, and let’s explore how tailored QA solutions can propel your business forward, reducing risks and boosting efficiency. Together, we can make QA an integral part of your success story, ensuring you stay ahead of the curve and are always noticed for the right reasons. Investing in QA is not just about avoiding failures; it’s about building a foundation for success and growth.

Let’s kickstart our discussion on GraphQL by delving into the shortcomings of REST API. After all, if REST API were flawless, GraphQL wouldn’t have been necessary.

Challenges Inherent in REST API

Over the years, it has become evident that there are significant challenges associated with REST, and these problems are :

1. Fixed entity structure
2. Request response only

So let’s delve into these issues, starting with…

1. Fixed entity structure

This implies that in REST, the client lacks the ability to specify the specific parts of an entity it wishes to retrieve. Additionally, the structure of the returned entity is predetermined by the backend developer and cannot be customized on a per-query basis.

Let’s understand with an example:

With REST, there’s no mechanism to fetch only the required fields, resulting in the retrieval of the entire entity, thereby amplifying load times and network latency. This inefficiency is often observed in various organizations, prompting developers to craft specialized entity types tailored for specific queries.

So that’s one of the issues with REST API: its limitation to support only fixed entity structures.

2. Request response only

REST API exclusively adheres to the request-response pattern. So, in the scenario where there’s a client and a service, the service exposes a REST API. The client sends a request to this REST API and subsequently receives a response, adhering to the request-response model. Nevertheless, modern web applications necessitate a broader spectrum of client-server communication methods.

Let’s understand with an example:

Take the case of Facebook: push notifications are increasingly valuable and widespread. To effectively adopt this model, the request-response pattern isn’t suitable. This is because we aim to push data from the service to the client rather than retrieving it as part of a request and response. Essentially, what we require is a mechanism for the service to send notifications to the client.

How can we do that:

We can achieve this through methods like polling, where periodic requests are sent to the service to check for new notifications, or by utilizing more advanced protocols like WebSockets. However, regardless of the implementation approach, incorporating this functionality with REST API is not inherent to the protocol and can be complex to implement.

So, recognizing these two issues led to the realization that a new API type was necessary to address them: the fixed entity structure and the request-response limitation. This is where GraphQL comes into play.

GraphQL APIs:

GraphQL primarily functions as a specification rather than a singular implementation. It outlines the structure of data returned, operating in JSON format. It encompasses three types of operations, relies on a schema, and boasts cross-platform compatibility. So let’s go through all the characteristics of GraphQL APIs.

Specification

GraphQL delineates the semantics and components of a GraphQL API without furnishing concrete implementations. Various entities develop implementations in multiple languages, and the comprehensive specification of GraphQL can be found here.

Defines a Structure of Data Returned

GraphQL’s defining feature lies in its ability to structure the returned data, which is perhaps its most significant advantage. With GraphQL, we can precisely specify the desired parts of an entity to be returned and even request related entities as part of the query. Additionally, filtering can be specified directly within the query. This capability effectively resolves the issue of fixed entity structures, as GraphQL enables us to pinpoint the specific fields of interest within the data model.

GraphQL API Architecture

A GraphQL API is a robust tool for interacting with data stored in a graph database. Unlike traditional REST APIs, GraphQL optimizes data retrieval by modeling it in terms of nodes and edges, effectively representing objects and their relationships. This design allows developers to perform multiple operations on various nodes using just one HTTP request.

Via HTTP/HTTPS requests, each GraphQL endpoint is dedicated to a specific operation (GET, POST, PUT, DELETE), streamlining client interactions with the database. Developers send requests, encapsulating essential data in the request body or query parameters, and receive an appropriate HTTP/HTTPS status code along with the requested data in response.

For instance, imagine a server that holds data about authors, blog posts, and comments. In a typical REST API scenario, a client might have to issue three separate requests (/posts/abc, /authors/xyz, /posts/abc/comments) to fetch details concerning a specific blog post, its author, and comments.

Conversely, a GraphQL API enables clients to make a single request that retrieves data from all three resources simultaneously. Additionally, clients can specify the exact fields they need, offering enhanced control over the structure of the response. This efficiency and flexibility are key factors driving the increasing adoption of GraphQL APIs in contemporary web development.

References:

1. GraphQL
2. Introduction to the GraphQL.