Tech Talks With Sachin Sharma

Since the world has become more informed than ever in terms of digital familiarity, people need rapid access to technology.  

From the introduction of automation to the integration of artificial intelligence, businesses have become incredibly dependent and inclined towards business-specific and user-dedicated applications.  

Besides, the growing demand for technology has even fostered the need for quality assurance. Be it meeting the productivity requirements or creating high-performing and efficient solutions, quality assurance creates space for more dynamic output.  

As the ever-growing business landscape needs to nurture quality assurance in order to align with the digital transformation goals, we at BugRaptors always keep on extending our network within the community to gain a unique vision on changing QA perspective.  

This time, our in-house expert, Kanika Vatsyayan, interacted with Sachin Sharma, Senior Manager QA, Adidas. With hands-on experience of more than a decade, Sachin holds CSM®, SAFe® 4.5 and ISTQB testing certifications (CTFL - Foundation, Agile Ext and ISTQB Advance - Test Manager). 

Sachin is flexible and always open to feedback with an intrinsic skill to self-coach. He is motivated and extremely results-driven professional who holds a dynamic perspective on Quality assurance and Process management.  

With all that detail about Sachin’s background and experience as a QA, let us quickly dive into the conversation to understand his vision of QA challenges, QA modernization, Agile, DevOps, and more.  

Kanika: According to you, what is the biggest challenge the QA industry is facing today? 

Sachin: In my opinion, one of the biggest challenges the Quality Assurance (QA) industry is facing today is keeping pace with rapidly evolving technologies and software development methodologies. The QA industry plays a critical role in ensuring the quality and reliability of software products, but the increasing complexity and diversity of software systems present significant challenges. 

Here are a few specific challenges: 

1. Agile and DevOps Practices: The adoption of Agile development methodologies and DevOps practices has brought about faster release cycles and continuous integration and deployment. QA teams need to adapt their processes and strategies to fit into these accelerated development cycles and ensure effective testing within shorter timeframes. 
2. Automation and Test Coverage: With the growing complexity of software applications, manual testing alone is no longer sufficient. QA teams are increasingly relying on test automation to improve efficiency and increase test coverage. However, developing and maintaining robust automated test suites can be challenging, particularly when dealing with dynamic and rapidly changing software environments. 
3. Mobile and Cross-Platform Testing: The proliferation of mobile devices and the need for cross-platform compatibility pose challenges for QA professionals. Testing applications across different operating systems, screen sizes, and hardware configurations requires specialized knowledge and tools. 
4. Security and Performance Testing: Ensuring the security and performance of software applications is becoming increasingly important. QA teams need to have a thorough understanding of security vulnerabilities and performance bottlenecks to effectively test and identify potential risks. 
5. Artificial Intelligence and Machine Learning: The rise of AI and machine learning technologies introduces new challenges for QA professionals. Testing AI models, ensuring fairness and avoiding bias, and validating the outputs of AI systems require specialized skills and approaches. 
6. Collaboration and Communication: Effective collaboration and communication between QA teams, developers, and other stakeholders are crucial for successful software development. Ensuring clear requirements, timely bug reporting, and efficient issue tracking can be challenging, particularly in distributed or remote work environments. 

Addressing these challenges requires QA professionals to stay updated with the latest industry trends, acquire new skills, and embrace innovative testing approaches and tools. 

Kanika: QA Modernization - What are your thoughts on it? 

Sachin: Adhering to my recent experiences, QA modernization is crucial for staying competitive and ensuring the delivery of high-quality software products. Here are some thoughts on QA modernization: 

1. Automation: Modernizing QA involves embracing automation tools and frameworks to streamline and accelerate testing processes. Automation can help in executing repetitive tasks, regression testing, and performance testing, allowing QA teams to focus on more complex and critical aspects of software testing. 
2. Shift-left Testing: QA modernization emphasizes early involvement of QA professionals in the software development lifecycle. By shifting testing activities to earlier stages, such as requirements gathering and design, defects can be identified and resolved early, reducing the cost and time associated with fixing issues in later stages. 
3. Agile and DevOps Integration: Modern QA practices integrate seamlessly with agile and DevOps methodologies. QA teams collaborate closely with development and operations teams, enabling continuous testing and faster feedback loops. This integration helps in identifying and addressing defects early, improving overall software quality. 
4. Test Environment and Data Management: QA modernization involves efficient management of test environments and test data. Provisioning and maintaining representative test environments and ensuring relevant and realistic test data sets are available are essential for accurate and comprehensive testing. 
5. Performance Engineering: With the increasing emphasis on performance, scalability, and user experience, QA modernization includes performance engineering practices. Performance testing, load testing, and stress testing are essential to identify and resolve performance bottlenecks early in the development lifecycle. 
6. AI and ML in Testing: QA modernization explores the use of artificial intelligence (AI) and machine learning (ML) techniques in testing. These technologies can assist in test automation, intelligent test case generation, anomaly detection, and predictive analysis, enhancing the effectiveness and efficiency of QA processes. 

Overall, QA modernization is crucial to keep pace with the evolving software landscape, improve efficiency, and deliver high-quality software products. It involves embracing automation, shifting left, integrating with agile and DevOps, improving test environment and data management, focusing on performance engineering, and exploring AI/ML in testing. 

Kanika: What does leadership look like in the age of Agile and DevOps? 

Sachin: Leadership in the age of Agile and DevOps takes on several key characteristics that align with the principles and practices of these methodologies. Here are some aspects of leadership that are particularly relevant in this context: 

1. Empowering Teams: Agile and DevOps emphasize self-organizing and cross-functional teams. Leaders in this context empower their teams by providing a clear vision, setting goals, and creating an environment where teams have autonomy to make decisions and take ownership of their work. Leader’s support and trust their teams to deliver results. 
2. Servant Leadership: In Agile and DevOps, leaders serve as facilitators and enablers rather than traditional "command and control" managers. They actively support their teams by removing obstacles, providing resources, and fostering a collaborative and inclusive culture. Servant leaders empower and enable their teams to excel. 
3. Collaboration and Communication: Agile and DevOps encourage frequent collaboration and open communication among team members, stakeholders, and leaders. Effective leaders facilitate communication channels, promote transparency, and foster a culture of trust and collaboration. They ensure that information flows freely, and decisions are made collectively. 
4. Agile Mindset: Leaders in the age of Agile and DevOps embrace the agile mindset themselves. They are open to change, adapt quickly to new information, and encourage experimentation and learning. They value agility, flexibility, and continuous improvement, and they encourage their teams to embrace these principles as well. 
5. Continuous Learning and Growth: Leaders promote a culture of continuous learning, growth, and knowledge sharing. They encourage their teams to acquire new skills, experiment with innovative practices, and embrace a culture of continuous improvement. Leaders also invest in their own development, staying informed about industry trends and advancements. 
6. Outcome-Oriented Focus: Agile and DevOps emphasize delivering value and outcomes over rigid adherence to plans and processes. Leaders focus on setting clear objectives, defining key results, and enabling teams to achieve those outcomes. They promote a results-driven culture that values customer satisfaction and business impact. 
7. Embracing Risk and Failure: Agile and DevOps encourage a culture that embraces risk and views failure as an opportunity for learning and growth. Leaders create a safe environment where teams are encouraged to take calculated risks, experiment, and learn from failures. They support a blameless culture that focuses on identifying improvements rather than assigning blame. 
8. Continuous Alignment with Business Goals: Leaders ensure that Agile and DevOps initiatives are aligned with broader business objectives. They bridge the gap between the technical and business sides, promoting collaboration and shared understanding. They provide guidance and direction to ensure that agile and DevOps practices contribute to the organization's strategic goals. 

In summary, leadership in the age of Agile and DevOps revolves around empowering teams, serving as facilitators, fostering collaboration and communication, embracing the agile mindset, promoting continuous learning and growth, focusing on outcomes, embracing risk and failure, and aligning with business goals. These leadership qualities enable organizations to effectively navigate the dynamic and fast-paced nature of Agile and DevOps environments. 

Kanika: 3 major metrics through which we can uplift the QA teams? 

Sachin: Uplifting QA teams involves measuring their performance and progress in meaningful ways. Here are three major metrics that can be used to assess and uplift QA teams: 

1. Defect Detection and Prevention Metrics:  

These metrics focus on the effectiveness of the QA team in detecting and preventing defects throughout the software development lifecycle. They provide insights into the quality of the software being tested and the team's ability to identify and address issues. Some relevant metrics include: 

• Defect Detection Rate: This metric measures the number of defects identified by the QA team during testing. It provides an indication of the team's thoroughness in detecting issues. 
• Defect Escape Rate: This metric measures the number of defects that are found by users or in production after the software has been released. A lower defect escape rate indicates effective prevention measures and a higher level of quality in the software. 
• Test Coverage: This metric assesses the percentage of the software application that is covered by the QA team's test cases. It helps evaluate the completeness of testing efforts and identifies areas that may require additional attention. 

2. Test Efficiency and Effectiveness Metrics:  

These metrics focus on the efficiency and effectiveness of the QA team's testing efforts. They provide insights into the team's productivity and the impact of their testing activities. Some relevant metrics include: 

• Test Execution Cycle Time: This metric measures the time it takes for the QA team to execute a set of test cases. A lower cycle time indicates efficient testing processes and optimized test execution. 
• Test Case Effectiveness: This metric assesses the percentage of test cases that successfully identify defects or validate software functionality. It helps evaluate the quality and relevance of the test cases being executed. 
• Test Automation Coverage: This metric measures the percentage of test cases that are automated. A higher automation coverage indicates increased testing efficiency, faster feedback loops, and improved productivity of the QA team. 

3. Customer Satisfaction Metrics:  

These metrics focus on the satisfaction of the end users or customers with the quality of the software. They provide insights into the impact of the QA team's efforts on the overall user experience and customer satisfaction. Some relevant metrics include: 

• Customer Reported Defects: This metric measures the number of defects reported by customers or end users. It reflects the quality of the software as perceived by the users and helps identify areas for improvement. 
• Customer Satisfaction Surveys: These surveys capture feedback from customers regarding their satisfaction with the software. The results provide valuable insights into the overall user experience and the quality of the product. 
• Time to Resolution: This metric measures the time it takes for the QA team to address and resolve customer-reported defects. A shorter time to resolution indicates prompt and effective defect management, leading to improved customer satisfaction. 

It's important to note that these metrics should be used in conjunction with qualitative assessments, such as team feedback, individual growth, and collaboration, to provide a comprehensive view of the QA team's performance and to guide efforts in uplifting the team effectively. 

Kanika: What are the benefits of implementing Model-based testing? 

Sachin: Implementing model-based testing (MBT) can bring several benefits to the software testing process. Here are some key advantages: 

1. Improved Test Coverage: MBT enables comprehensive test coverage by using models to systematically generate test cases. Models provide a visual representation of the system or application under test, allowing testers to identify different states, actions, and transitions. By automatically generating test cases from these models, MBT helps ensure that all relevant scenarios are covered, including edge cases and complex interactions. 
2. Early Defect Detection: MBT allows for early defect detection by enabling testing activities to start during the requirements and design phase. Models can be created based on system specifications, and test cases can be generated and validated against these models early in the development process. This early feedback loop helps identify potential issues or ambiguities in the requirements, leading to faster resolution and reduced rework costs. 
3. Increased Efficiency and Productivity: MBT automates the process of test case generation, reducing the manual effort required for test case design. Test cases are derived from models, eliminating the need for manual scripting and reducing human error. This automation leads to increased efficiency and productivity, enabling testers to focus more on critical thinking, test execution, and analyzing test results. 
4. Maintainability and Reusability: Models used in MBT can be easily maintained and updated as the system evolves. Changes in requirements or design can be reflected in the models, and test cases can be regenerated accordingly. This ensures that the testing effort stays in sync with system changes and reduces the effort required to maintain test suites. Additionally, models and test cases can be reused across different versions or variants of the system, saving time and effort in test case creation. 
5. Traceability and Documentation: MBT provides traceability between the models, test cases, and requirements, allowing for better documentation and visibility into the testing process. It becomes easier to track which requirements have been covered by specific test cases and to demonstrate test coverage to stakeholders. This traceability helps ensure that testing activities are aligned with the intended system functionality and regulatory compliance. 
6. Scalability and Flexibility: MBT is highly scalable, as it allows for the generation of many test cases with minimal effort. This scalability is particularly beneficial when dealing with complex systems or extensive feature sets. MBT also provides flexibility in managing test variations and test configurations, enabling testers to generate different combinations of test cases based on system parameters or user profiles. 

It's worth noting that implementing MBT requires upfront investment in creating models and setting up the necessary tools and infrastructure. Additionally, the effectiveness of MBT depends on the quality of the models and their alignment with the system under test. However, when implemented properly, MBT can significantly enhance test coverage, early defect detection, efficiency, maintainability, and documentation in the software testing process. 

Kanika: People have multiple myths about low code automation testing, what do you think about it? 

Sachin: Low code automation testing is a relatively new approach to software testing that has gained popularity in recent years. It involves using graphical user interfaces and drag-and-drop functionality to create automated test cases, instead of traditional coding-based approaches. Low code automation testing tools aim to simplify and accelerate the automation process by providing an intuitive and user-friendly interface, reducing the need for technical expertise and coding skills. 

Low code automation testing has its benefits and limitations, and it is important to approach it with realistic expectations. While it can streamline the automation process and improve efficiency, it is not a one-size-fits-all solution and should be integrated as part of a comprehensive testing strategy that includes both manual and automated testing approaches. 

Additionally, low code automation testing should not be seen as a replacement for manual testing entirely. While it can automate certain repetitive and time-consuming tasks, manual testing is still essential for activities that require human judgment, exploratory testing, and usability testing. 

In summary, low code automation testing can be a valuable addition to a testing toolkit, but it should not be viewed as a silver bullet. Testers and organizations should assess their needs, capabilities, and objectives when considering low code automation testing and determine how it can best complement their existing testing practices. 

Kanika: Where can people go to find out more about you and your work? 

Sachin: As an avid user of LinkedIn, I invite you to connect with me and explore a deeper understanding of my thoughts, beliefs, and professional endeavors. Additionally, I am proud to serve as a co-founder of The Test Chat community.  

By joining our community on YouTube, LinkedIn, Twitter, Telegram, you will gain access to valuable insights and stay updated on the latest happenings in the testing industry. It's a fantastic opportunity to expand your knowledge and engage with like-minded individuals passionate about the subject matter. 

When it comes to digital success, quality assurance opens all the ways to advanced development. Besides, getting timely access to advanced practices like Agile, DevOps, and Automation complements more streamlined development. 

From bringing together the community to exploring changing dynamics of testing, we at BugRaptors never miss out on anything that is vital to generate value to the world. 

For more such interesting updates on quality assurance, stay connected with us by subscribing to our blog. 

In case, you need any expert advice, suggestions, or professional testing assistance, feel free to share your queries at info@bugraptors.com