Agile Testing Trends to Watch Out in 2021

Agile Testing Trends to Watch Out in 2021

Technology is continually changing. What was ultra-modern a few years back might be obsolete now. Presently, the software development and testing industry is innovating ways to include evolving technologies like artificial intelligence, machine learning, big data, etc.

Be it browser compatibility testing, selenium test automation, or any other kind of testing process, each of these testing processes is consistently transforming with the vision to bring better products. Presently enterprises concentrate more on agility to align better testing methodology based on agile principles.

The contribution of DevOps, Continuous Testing and other factors are expected to elevate agile testing soon. This article analyses some of the crucial agile testing trends expected to impact organizations wishing for quality software testing in 2021.

 AI & ML in Agile Methodologies

The coherent usage of artificial intelligence (AI) and machine learning (ML) in agile methodologies creates an ideal data analysis method. Such collaboration allows software teams to achieve better productivity and efficiency associated with testers and developers. AI & ML together delivers real-time information. Ans also offers a precise prediction of the expected time of the release phase of the project. The inclusion of innovative technologies such as robotics, IoT, quantum computing, etc., is much possible with AI & ML in the software development process.

Sun Technologies’ agile approach with AI & ML provides a good understanding of the best methods for creating testing code. Our experts evaluate code and associated tests to remove bugs. We use innovative technologies for accelerating software development & time-to-market.

Software Quality Engineering 

When you manage testing tasks in an agile environment, the dependency on quality engineering is more. What is the dissimilarity between quality engineering and quality assurance? Quality engineering deploys continuous testing of the related product with the extensive implication of automation. It ensures that the product is placed under efficient testing to make it error-free.

Benefits:

  • Provides faster feedback on the software product because continuous testing is done across various platforms and operating systems
  • Minimizes software failures and availability of early feedback

Shift-Left Testing Approach

The shift-left process emphasizes various types of testing performed simultaneously with software development— testers collaborate with developers to frame test cases. The method also includes what-if scenarios, and the tests are used for streamlined development.

In the usual software development process, the incorporation of testing is seen as a blockage to the release process. Testers operate on less time due to tight schedules, thereby hindering testing efforts and identify errors. However, by the shift-left process, testers get a sufficient amount of time to test the software’s usability by comfortably teaming up with UI & API developers.

Benefits:

  • Faster time-to-market for early release
  • More rapid identification of bottlenecks to avoid software failures
  • Provides high performing software under minimal time

Agile Test Management

For any software testing services, the collaboration of testers and developers is a must. Such cooperation includes the structure, execution, and report of testing once the results are out.

The agile test management’s involvement helps determine the processes and tools that allow the overall team to maintain testing progress. The process brings together everyone on the same path. For experts working in distributed environments, a cloud-based test management tool’s availability confirms easy access to testing information anytime and anywhere.

Benefits:

  • Allows team members to trace testing efforts and increase collaboration
  • Minimizes the bugs and accelerates the release of high-quality software
  • Ensures real-time feedback

DevOps Process 

The concept of DevOps is based on lean management. It concentrates on combining development and operations to create a suitable environment. A DevOps approach refines the software development lifecycle and eliminates junk, and accelerates software delivery. The combination of Agile and DevOps can fine-tune team relationships and communication, thereby minimizing software failures. Moreover, DevOps skillfully combines continuous testing into the development process to ensure code quality.

Benefits:

  • Set up a proper collaborative culture
  • Integrates development and testing processes 
  • Combines operations within the team to reduce downstream testing concerns

Continuous Testing 

The process of Continuous Testing includes redundant executing tests that deploy testers into cross-functional teams. This arrangement helps refine testing functionalities and offers rapid feedback. Continuous testing supports early testing along with shift-left, agile test management, and quality engineering.

Benefits:

  • Reduces software failure by early detection
  • Enhances software quality through ongoing reviews and reports
  • Improve test suites to identify business risks

Lean Portfolio Management 

Lean portfolio management follows a different methodology that focuses on streamlining operations to measure outcomes based on organizational goals and planning. It mainly follows a continuous process used to assign tasks within teams based on the priority and well-organized strategy.

As per Lean portfolio management, the paramount importance is on the collaboration running from top to bottom, covering factors connected with goal measurement, planning, and work transparency.

 Benefits:

  • Enhances the relationship between organizational strategy and individual projects
  • Fine-tune business value to get clarity about the software 

Businesses cannot ignore the impact of agile testing in today’s progressive testing environment. Therefore, technological upgrades to enhance agile testing is the main motto for most software companies. At Sun Technologies, we deliver world class QA services. Get in touch with one of our solution consultants today to understand your business requirements and find suitable solutions. Discover more about the various attributes of agile testing with us

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AI and Digital Transformation: 4 Ways to Greater Automation

AI and Digital Transformation: 4 Ways to Greater Automation

The initial phase of digital transformation is still an ongoing process for many enterprises, centering around the digitalization of products, services, and business processes. The second phase uses AI to enhance the quality of decision-making, strengthen the relationships with customers, and optimize organizational productivity. 

While various enterprises are at various stages of maturity of digital transformation, many companies have already been experimenting with AI separately to discover how it could benefit the business in the second phase of digital transformation.

One of the crucial reasons for the second phase of digital transformation’s unsymmetrical results is the understanding or lack of knowledge of what Artificial Intelligence can do. There has been a general illusion that artificial general intelligence (AGI) can solve any issue when truly artificial narrow intelligence (ANI) is the ultra-modern.

AI in Digital Transformation

The digital transformation journey has crossed from digitization to digitalization. It also covers different data technologies across various industry verticals. To the extent that data-driven innovations are helping to manifest several advances in digital technologies as actionable, the next boundary in re-molding enterprises is AI transformation. Progression in AI is forcing digital enterprises toward becoming intelligent enterprises. AI technologies are already revolutionizing not just how we recognize and do business, but also the business environment and its’ overall landscape.

1.Augmented Analytics

Business intelligence is going far beyond dashboards. AI and machine learning are becoming a much more user-friendly process for inexperienced workers as augmented analytics are embedded into platforms.

Organizations are struggling to get their data management and machine learning practices up. This is where augmented analytics are arriving at the rescue. What’s more, it could also help with utilizing machine learning for production purposes, which has been a problem for many enterprises.

The benefits of this have the potential to extend far beyond business intelligence. The passion for implementing AI in enterprises was high at the initial stage. By adding automation to parts of business operations, such as data pipelines and data management, augmented analytics can be one of the elements of the solution to acquiring AI into enterprise production.

2.Automation

Automation has moved past the workforce to white-collar tasks that are repetitive. Just like with some robotic process automation (RPA) tools and chatbots, several automated systems are not necessarily “intelligent” because they are inevitably programmed. i.e., a given input produces a given output. AI enlarges the scope of what automated systems can do and moves enterprises to the second phase of digital transformation.

3.Customer Engagement and Resources

More than half of organizations acknowledge not having a formal customer engagement program established. Because of this, those organizations had no grasp on the number of customers they’d actually lost in a one year period. On the other hand, most customers expect a consistent experience wherever they engage. Digital transformation using AI can help optimize customer engagement by dynamically aligning the website content with the customer’s preferences.

4.AI-Digitized supply chains

AI solutions and tools help analyze large datasets in real-time, plan production efficiently, balance supply and demand gaps, schedule factory activities effectively, and develop error-free SCM plans and strategy. AI can also help estimate the market requirement and manage production accordingly to avoid overproduction or shortage of products, either of which would result in loss.

Conclusion

The future of connected digital transformation includes more IoT and industrial IoT devices and the coordination of AI.

IoT and IoT devices are already offering businesses the clarity at the edge, which they lacked before. When combined with AI, the sensors are helping to change the ways companies operate to yield optimization.

Enterprises with and without AI-enabled digital transformation models may adopt some AI by default because it has been embedded in the software tools, applications, and platforms they already utilize. Data scientists should aim to solve complex issues and bring more value from AI in the second phase of digital transformation. Simultaneously, citizen developers (power users) can tackle easier problems, such as optimizing business processes and tasks within their department.

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Sun Technologies’ Test Automation Framework Improved a US Furniture Retailer’s App Performance by 42%

Case Study

Sun Technologies' Test Automation Framework Improved a US Furniture Retailer's App Performance by 42%

Whom we worked with

The Client is an Omni-channel retailer of furniture products headquartered in Atlanta, Georgia, and has over 100 brick and mortar stores across 16 states in the USA.

Our Solution

  • Evaluated various open source and licensed tools and picked the right tools for the test solutions
  • Identified a minimal number of end to end scenario with maximum test coverage
  • Designed and Developed a Hybrid Selenium framework to tailor it to the client’s e-commerce and sales applications
  • Accelerated knowledge transition
  • Quick ramp up and ramp down of resources
  • Perfectly synchronized on-site/offshore set up
  • High Test Coverage
  • Continuous Process improvements
  • Comprehensive reusable test cases

Challenges

  • Insufficient regression testing coverage using a manual approach
  • No Dedicated QA automation team and hence no QA process followed by client
  • The client was using an eCommerce platform built on legacy technology and had several internal functionality gaps due to which it failed to complete the online orders successfully

Impact

  • Reduced testing life cycle time
  • Increased test coverage to 100%.
  • Record time product releases.
  • Test automation resulted in regression run test reduction from 20 hours to 4 hours
  • Ensured that quality is engineered into the application right from the beginning of the development cycle. This strategic shift resulted in early detection of up to 70% of the defects in the SDLC, leading to a 35% improvement in the application’s quality. We enabled 100% traceability to facilitate both backward and forward tracing of requirements through defects and vice versa

How we helped

  • Assessed the requirements, tools, and processes involving automated testing for their applications and presented a high-level test plan
  • Our test automation framework countered the lack of automation in the sprint cycles
  • Over 282 test automation scripts developed so far for component-level testing, we implemented the shift-left approach for performance testing the web application
  • Implemented Test automation for regression run tests.
  • Ability to run test automation scripts with just one click (using a batch file)
  • The performance engineering approach of our testers made sure that quality is engineered into the application right from the beginning of the development cycle.
  • Provided a Dashboard/Web UI to control all the aspects of test automation like selecting environment, application, test cases, test data set, and so on