Discussion on Test Observability - A Paradigm shift from Automation to Autonomous to deep observability by Vijay Kumar Sharma | Testμ 23

:mag: Explore the paradigm shift in testing with Vijay Kumar Sharma

Discover the power of continuous insights for enhanced product reliability, stability, and ROI.

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Here are the some of the questions and answers from the Q&A round!

Can you provide examples of how test observability can enhance testing processes and outcomes?

Vijay: Test observability is like having a special tool that makes testing better. For instance, if a test doesn’t work, observability can quickly show what’s wrong in the code. It also helps us know which tests are most crucial and need attention first, making testing more effective and efficient.

What access do I need to use test observability?

Vijay: To use test observability, you’ll need access to tools or systems that gather information from your tests. These tools show you what’s happening in your tests, making it easier to find and fix issues.

As software systems become more complex, how does the concept of deep observability transform the way organizations approach troubleshooting, root cause analysis, and continuous improvement?

Vijay: Lot of the testing teams find the root cause when the failure happens. So, if we take the data-driven approach there, approx. 80% of the efforts can be saved and then you only focus on the continuous improvement part. Other things can be taken care through the data and insights of that tool.

Could you share an example of a scenario where insights from test observability led to significant improvements in a software development project?

Vijay: Imagine a world where 20 teams are working on 100 microservices pushing them via CI/CD pipeline and they merged to the first test environment. Now assume hundred or thousands engineers contributing to it. Any change by one developer can conflict with other microservices and things can break. The point is whatever solution you have implemented, are you able to measure the efficiency of it or not. So it boils down to keeping looking the act what you are doing, and keep double clicking the insights you get.

Now let’s look at some of the unanswered questions:

How do we fit Observability within QA & Testing?

Could you elaborate on the key drivers behind the paradigm shift towards autonomous systems in the software industry, and how this shift enhances both development agility and end-user satisfaction?

What is the significance of the paradigm shift from Automation to Autonomous to Deep Observability in testing, and how does it contribute to achieving better ROI in the software industry?

What are the parameters to judge if its the right time for Test Observability journey?

How is test observability implemented in mobile app testing?

What are some potential challenges that organizations might face when implementing test observability practices?

Hi there,

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In my experience, observability in the context of Quality Assurance (QA) and Testing refers to the ability to understand and gain insights into the system’s internal state based on its external outputs. Integrating observability within QA and Testing enhances the team’s capability to identify, diagnose, and resolve issues efficiently, ensuring a more reliable and robust software product.

This is how you can implement Observability in QA & testing:

  1. Incorporate Monitoring Tools
  2. Leverage Tracing Technologies
  3. Apply Logging Practices
  4. Integrate Feedback Loops
  5. Collaboration Between Teams

Integrating observability within QA and Testing is crucial for modern software development practices aiming at delivering high-quality products. Observability tools and practices provide invaluable insights into the system, enabling teams to identify, understand, and resolve issues efficiently and proactively. Through careful implementation and continuous improvement, observability becomes a cornerstone for achieving excellence in QA and Testing processes. I hope this answers your question!

In my experience as a software tester, the software industry is experiencing a significant paradigm shift towards autonomous systems. This transition is driven by the need for more efficient, reliable, and agile development processes, ultimately aiming to enhance end-user satisfaction. Below are the key drivers behind this shift and their impact on development agility and user satisfaction.

  1. Demand for Faster Delivery
  2. Increased Complexity
  3. Need for Scalability
  4. Focus on User Experience
  5. Cost Efficiency

Enhancing Development Agility:

  • Automated Testing: Autonomous systems support automated testing, ensuring that any changes or additions to the code are validated promptly, reducing the time to market.
  • Continuous Integration/Continuous Deployment (CI/CD): These practices are facilitated, allowing for automatic code integration and deployment, which speeds up the development process.

Boosting End-User Satisfaction:

  • Reliability: With automated testing and monitoring, autonomous systems contribute to the development of more reliable and error-free software, enhancing user trust and satisfaction.
  • Faster Updates: Users receive new features and improvements more quickly, keeping the software up-to-date and aligned with their needs and expectations.

The shift towards autonomous systems in the software industry is a response to the demands for faster delivery, increased complexity, scalability needs, enhanced user experience, and cost efficiency. This transition not only empowers developers with more agility but also significantly contributes to end-user satisfaction by delivering reliable, up-to-date, and user-centric software solutions.

As an automation tester, I believe the paradigm shift from Automation to Autonomous to Deep Observability in testing represents a significant evolution in the software industry’s approach to ensuring quality and reliability. This transition plays a crucial role in enhancing the Return on Investment (ROI) by improving efficiency, reducing costs, and delivering superior products.

From Automation to Autonomous:

Automation in testing refers to the use of specialized tools to conduct tests that would otherwise be performed manually. This shift to autonomous testing takes it a step further by not only automating tasks but also making intelligent decisions during the testing process.

  • Significance: Autonomous testing minimizes human intervention, reducing the risk of errors, and significantly speeding up the testing cycle. It can adapt to changes in the application under test, making the testing process more resilient and reliable.

  • ROI Contribution: With faster and more accurate testing cycles, products can be delivered more quickly to the market, reducing time-to-market and operational costs, which positively impacts ROI.

Transition to Deep Observability:

Deep Observability involves gaining insights into the internal workings of a system, providing a comprehensive understanding of its performance, reliability, and user experience.

  • Significance: Deep Observability allows teams to proactively identify and address issues, improving the quality and reliability of the software. It provides a holistic view of the system, making it easier to pinpoint and resolve problems, ultimately leading to a more stable and efficient application.

  • ROI Contribution: Improved software quality and reliability lead to increased customer satisfaction and reduced maintenance costs. The ability to preemptively identify and resolve issues means less downtime and better overall performance, which are crucial for maximizing ROI.

The paradigm shift from Automation to Autonomous to Deep Observability in testing is significant in the software industry, playing a pivotal role in improving ROI. This transition not only enhances efficiency, reduces costs, and improves product quality but also contributes to higher customer satisfaction, making it an essential strategy for organizations aiming for success in the competitive software market. I hope this answers your question.

In my experience, embarking on a Test Observability journey is crucial for enhancing the efficiency and effectiveness of your testing processes. However, it’s vital to initiate this journey at the right time to maximize its benefits. Below are key parameters to consider when evaluating the timing for implementing Test Observability.

  1. Complexity of the Software
  2. Volume of Data
  3. Frequency of Releases
  4. Customer Experience Focus
  5. System Downtime
  6. Scalability Concerns

Deciding on the right time to start the Test Observability journey depends on various factors, including the complexity of your software, data volume, release frequency, focus on customer experience, system downtime, and scalability concerns.

When these parameters indicate a growing need for deeper insights and more efficient testing processes, it’s advisable to consider implementing Test Observability to enhance your software’s reliability, performance, and user satisfaction. I hope this answers your question.

In my experience being a mobile app tester, test observability in mobile app testing is a crucial practice that provides deep insights into the application’s performance, functionality, and user experience during the testing phase. Implementing test observability involves utilizing various tools and practices designed to monitor and analyze the application’s behavior under different conditions and environments.

  1. Integration of Observability Tools
  2. Setting Up Metrics and Indicators
  3. Implementing Logging Practices
  4. Utilizing Tracing Technologies
  5. Continuous Feedback Loop

Implementing test observability in mobile app testing involves the integration of observability tools, setting up metrics and indicators, implementing logging practices, utilizing tracing technologies, and establishing a continuous feedback loop.

This approach provides testers with valuable insights into the app’s performance and behavior, supporting proactive issue identification, efficient troubleshooting, and improved test accuracy, ultimately leading to the delivery of high-quality mobile applications. I hope this answers your question.

In my experience, implementing test observability practices in organizations is crucial for monitoring and understanding the internal states of systems and applications during testing phases. However, organizations may encounter several challenges in this process:

  1. Lack of Expertise
  2. Integration Issues
  3. Data Overload
  4. Cost Constraints
  5. Cultural Resistance
  6. Security and Privacy Concerns
  7. Lack of Clear Objectives

To sum it up, while test observability offers valuable insights and enhances the testing process, organizations must navigate through these challenges carefully. Addressing these issues requires a combination of strategic planning, investment in training and tools, and fostering a culture that embraces new testing methodologies.

With careful consideration and planning, organizations can overcome these challenges and successfully implement test observability practices to improve their software development and testing processes. I hope this answers your question. Please feel free to ask any further questions related to this.