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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How Artificial Intelligence and Machine Learning can optimize DevOps?

How Artificial Intelligence and Machine Learning can optimize DevOps?

DevOps boosted by AI/ML

Seven out of ten customers use DevOps and IT Services. Vendors are under tremendous pressure to fulfill clients’ evolving needs to create a self-healing system and increase automation. Sun Technologies uses AI/ML solutions to improve the efficiency of the DevOps Pipeline. With our tried-and-tested AI Solution, we help our customers create a self-healing system, reduce time-to-market, and improve efficiency using DevOps powering it with AI/ML solutions. 

In a few cases, it may be hard for some organizations to use AI and ML due to the complexities. Adapting to AI/ML solutions within DevOps is a cultural shift. 

With Machine Learning, several models can be created to analyze the DevOps metrics that include:

Our trained models help customers in: 

  • Analyze metrics
  • A deep-dive analysis of repeated failure
  • Executing automation sets
  • Predicting failure points before the occurrence

Based on the result-sets aggregated from the model, AI helps make automated prediction-based decisions to avoid failures.

Recent research states that around 85% of C-level officials trust the AI/ML can offer considerable value concerning accuracy and decision-making, prompting improved organizational productivity.

AI/ML on each phase of DevOps

There is a wide misconception across the industry in understanding DevOps. Automation is not the entire DevOps world, but just a yield. DevOps is a cultural shift for Developers, Business Users, Infrastructure engineers, and a few other key stakeholders. 

DevOps has various features that include Continuous Integration, Continues Delivery, Continuous Monitoring, Continuous Testing, and Continuous Security. AI/ML has its role in each feature sets. 

Sun Technologies helps customers build a cloud-agnostic and cloud-native DevOps pipeline and improve efficiency throughput using AI/ML solutions. We bring prediction, Learning, and Automation together. 

Few use-cases AI/ML within DevOps pipeline implemented by Sun Technologies includes: 

  • Automated Code rollback for wrong check-ins 
  • Automated Log analyzer to identify security threats such as intrusion, DDoS, DoS
  • Self-Healing Web-Application System
  • Alerting mechanism for potential (Futuristic Failures) 
  • Chatbot / Voice Automated Deploy Assistant
  • DevOps Advisor suggesting automation of repeatable tasks

DevOps optimization using AI/ML

  • The three well-defined capabilities AI brings are prediction, self-learning, and automation
  • AI and ML dispatch data with self-learning capabilities, making AI and ML techniques exceptionally advantageous if imbibed into the DevOps Pipelines
  • During SDLC, AI/ML can monitor and track production performance to which the end-user experience is being labeled by simulating different possible scenarios
  • With AI/ML incorporated into the DevOps process, the DevOps teams can know how the code is performing
  • AI allows managing the growing volumes of data in DevOps environments
  • With Chatbot/Voice assisted DevOps, Developers can check-in the code and make deployments with a single command 

Tangible benefits

  • Faster Time to Market using DevOps while AI/ML boosting it further to make it more efficient
  • Proactive decision making than reactive
  • Satisfied Business users
  • Low/No human intervention
  • Realistic Instant RoI
  • Adaptable and Maintainable DevOps pipeline
  • Huge Time Savings
  • Increased Efficiency

Conclusion

Enterprises can apply AI and ML to enhance their DevOps condition. AI can help predict complex data pipelines and make models that can enhance the application development process. However, implementing AI and ML for DevOps likewise exhibits various difficulties for enterprises.

Organizations envisioning DevOps have to set up a well-defined DevOps roadmap before full-fledged implementation. When the establishment is made, AI/ML should be viewed only as a booster to increase efficiency and effectiveness. AI/ML helps DevOps teams to concentrate on inventiveness and innovation by taking out negative aspects over the operational life cycle. It brings about automated improvement and expansion in the DevOps team’s effectiveness.

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Automation testing for a Leading Finance/Insurance company operating across USA

Case Study

Automation testing for a Leading Finance/Insurance company operating across USA

Whom we worked with

A top-tier insurance company that offers financial products and services including life insurance, annuities, mutual funds, disability income insurance, credit union products, retirement planning, and more.

Our Solution

  • Developed a framework to integrate with Test Management tool ALM
  • Created a completely new documented business process
  • Designed test plan and roadmap with milestones and estimates

Challenges

  • Lack of dedicated QA team with the client to support the automation script development
  • Integration of Selenium Framework with ALM/QC
  • Automating mainframe applications

Impact

  • Replaced 90% of manual validation with Test Automation
  • With one button click, they are able to validate applications after monthly patches deployed
  • After Test execution test results were reported in HTML files and these reports are emailed to all asset owners automatically
  • Reduced 70% of maintenance effort for Test automation scripts and provided 100% customizable test report
  • Saved Milions of Dollars in Testing costs by eliminating all manual effort

How we helped

  • Sun Technologies designed a high-level test plan which provided visibility on effort estimation, deliverables, and test approach
  • We developed a Robust Automation framework using Selenium which would allow triggering test execution from ALM which reduced 80% of manual effort
  • Automated the complex scenarios of 20 Mainframe applications
  • Accomplished an optimal level of automation for their 300+ applications in Production and Test environments
  • Test Automation replaced 90% of manual validation during monthly patch releases this helped the client in saving millions of dolars of testing costs by eliminating manual efforts.
  • Test automation scripts are used in daily batch executions to ensure all the 300+ applications are up and running
  • We have taken additional responsibilities with Release Management to assist the process to become more robust