With the utilization of an Agile environment and DevOps becoming necessary in all software development companies, the QA and testing teams are facing new challenges.

Although Agile and DevOps are essential for an enterprise to meet the growing demands, it poses several testing and QA challenges as these departments are not equipped with the proper tools to meet the requirements of these new environments.

The concept of intelligence testing in comparatively new, but has helped QA and Testing teams in facilitating seamless test cases.

Let’s briefly discuss Intelligent Testing and how it can help in unlocking the power of data.

Intelligent Testing: An overview

Intelligent testing is the process of utilizing artificial intelligence and machine learning to automate the testing process. Intelligent testing services provide perspective analytics and automation for optimizing test cases, along with reducing defects in DevOps and Agile product lifecycle and improving test coverage and code quality.

Intelligent testing services help companies transform their testing strategies, and adopt an agile, comprehensive, and end-to-end approach that focuses on test automation & optimization, test equipment & tool rationalization, and test environment consolidation. Intelligent services are highly applicable and useful in multi-domain, multi-technology, multi-network, and multi-domain settings across pivotal industry verticals.

QA and testing challenges

Some common challenges enterprise development and test organizations encounter in agile environments are:

  • Wastage of time in communicating and clarifying test requirements
  • Increasing test environment costs along with a massive backlog of test cases
  • Frequent application changes leading to high script test maintenance costs
  • Wastage of time spend on fixing applications
  • Execution of similar testing apps leading to inefficient use of resources
  • Hurdles with managing and assessing test data
  • Lack of experience and expertise with the latest tools and technologies essential for providing predictive insights

How can Intelligent Testing services help?

Quality is everything that markets your presence both directly and indirectly. Yes, the importance of Quality is always highly expected in delivering either a service or a product, that it can either gain you or ruin you based on outcomes. So, it is always important to invest more time and money in ensuring the end quality, which in turn signifies your customer satisfaction.

Great Industrialists say that quality itself has the power to build your business enterprise alone. But, there are many hurdles when it comes to delivering the promised quality, that too, sometimes in limited budgets and tight timelines. So, there are many different approaches implemented to assess or check the quality of a product based on many terms. Data plays the hero role in all the quality check process and is considered as the soul of QA. So, here, techniques like Intelligent Testing services are helping a lot in unveiling the real stamina of data in delivering the desired quality of end products or services as required by the customers.

QA teams encounter several challenges, including feaster development, evolving requirements, short release cycles, and unclear accountability for quality. However, with the integration of artificial intelligence services with QA, the new QA tools are capable of encountering these challenges.

Here is how intelligent test services can help in unlocking the power of data.

#1. Easy test creation and maintenance

Existing automation frameworks require scripting expertise, which is not the case with the next-gen QA tools. Moreover, these tools will provide intuitive interfaces that will offer seamless test creation and management.

Therefore, intelligent testing will help QA teams extend their test coverage and transform their test coverage according to the evolving products and technologies.

#2. Seamless adaptation to change

Intelligence testing brings in durable and sophisticated methods for replicating user behavior in automated tests, which helps avoid brittleness that is associated with existing automation frameworks.

Tests will not be refrained to mercurial elements in the front-end codebase but will utilize machine learning to build and sustain sophisticated test models. It will help tests to adapt to changing testing conditions rather than failing when locators like paths change.

#3. Cloud-based quality analysis

Presently, many teams struggle with performance, operational overhead, and costs of on-premise testing frameworks. Intelligent testing helps the next-gen QA tools to take advantage of cloud computing resources for executing tests quickly and efficiently.

The cloud support helps in unlocking the power of data by providing robust data processing and data analytics services for delivering insights by analyzing test results. Moreover, it ensures delivery as a service, which offloads the operational and management strain to tool vendors.

#4. Integration with delivery pipeline

The new QA tools will be integrated into automated into CD/CI pipelines. When integrated, tests will automatically trigger when teams make modifications to a product under test and on-demand.

The intelligent testing tools will notify team members regarding potential issues, enabling them to address the problems before they perturb the users. The testing process will be simple: run tests and analyze outcomes across environments and builds. The integration of QA into the delivery pipeline will also automate the decisions around deployments, promotions, rollbacks, and releases.

#5. Enhanced quality

Traditional QA tools are limited to validating particular assertions in tests, whereas, intelligent testing services will use machine intelligence to detect and highlight probable regressions in the applications. Thus, instead of settling for the “Pass” or “Fail” results, QA will be more about degrading or improving the user experience.

It will help QA teams focus less more on product coverage and less on code coverage. Additionally, it will help organizations look beyond the passing and failing of tests, and will chase a more holistic approach to appraise the risk linked with a provided build or deployment.

Wrapping it up

Quality Assurance has always been a tedious task, primarily due to the accelerating pace of software development and technology evolution in recent years. However, the introduction of intelligent services has led to the use of next-gen QA tools that are powered by cutting-edge machine intelligence.

These tools are derived from the cloud and ingrained into modern development workflow to improve the effectiveness and efficiency of software quality assurance significantly.