Following the initial transition from traditional testing to modern quality engineering, numerous enterprises now view continuous innovation and evolution as the new battleground. According to many analyst firms, this transformation around AI/ML, blockchain, cloud computing, data, and cyber security can act like growth drivers for IT, with quality engineering expenses in 2022 reckoned to exceed 100 billion USD.
As predicted, this year saw the advent of several novel technologies that can help organisations transform their IT workforces across all major verticals. However, the quality engineering domain has several major service lines that are geared to accommodate and expedite comprehensive technology transformation.
Every organisation strives to increase team productivity and deliver high-quality products and services in a timely manner. But how should you go about it is the question, and we are happy to assist you. Learn more about our All-in-One Smart Test Automation Platform by
Before we dive deep into two of the most sought-after emerging technologies, let’s briefly discuss digital quality engineering.
The long-held definition of quality engineering is: “the discipline of engineering concerned with the principles and practise of product and service quality assurance and control.” (Source: Wikipedia) The field of digital quality engineering extends beyond software testing.
Stress detection through stress testing is an essential component of quality assurance. Initially, manual quality assurance was used to support the mainframe era, which was followed by the introduction of automation through the World Wide Web (WWW), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and so on. Businesses have evolved into an “Intelligent Digital Mesh” (IDM), and the Cloud Computing revolution has ushered in the “digital quality assurance” method.
The three components of speed, quality, and intelligence combine in digital QA to establish a unified approach to accelerated Continuous Quality Improvement (CQI). As organisations scramble to attain a digital makeover, digital quality checkpoints shine through with enhanced risk mitigation and holistic customer-centric solutions. Many aspects of digital QA, such as cognitive QA, IoT testing, cloud testing, chatbot testing, and voice bot testing, aid in digital transformation.
As companies mature in addressing this new IT environment, Digital QA will evolve to address the end-to-end automation of test ecosystems, improve the velocity of quality delivery, and enable Agile and DevOps frameworks and AI (AI). One such strategy that incorporates AI and ML (Machine Learning) into automation is autonomous testing.
Best Read: Testing a bank application: A Success Story
Many sectors, including software, strive for flawless user experiences. However, to deliver swiftly, organisations must reduce delivery cycles from months or weeks to hours or less. This is where automation comes in. Before automation, test cycles were frequently lengthy. Handwritten tests have a set of stages and checks that require involvement.
When the first wave of test automation arrived, it completely transformed the test execution process. Automation allowed tests to run more quickly and constantly. However, the disadvantages of manual triggers and maintenance issues quickly became apparent. The CI/CD methods further blurred the line between developers and testers. At this moment, the automation sector appears to be at a standstill.
Some experts argue that automation testing, when faced with fast-paced new-age practices, failed to fully deliver on its promises. Some of the main challenges with automation are listed below:
A high degree of skill and domain expertise is essential to creating high-quality automation scripts. These include proficiency in programming languages, mastery of the tools, and thoroughness with client requirements. Manual creation of tests remains the major bottleneck for automation.
It would not be incorrect to say that automation is a maintenance nightmare in and of itself. It incurs high costs and is time-consuming. Sometimes a minor update to the GUI would result in re-recording an entire script. The fact that debugging a script is equally troublesome adds to the difficulty.
Considering the most widely used Selenium automation tool, most scripts rely on XPath. Any change in it, and the scripts become useless. Also, the scripts don’t consider that the infrastructure is inherently unstable, so tests “fail” when they time out or crash.
We read out both to test in automation, considering all forms of testing as verification of interaction. What if we could automate dictation entirely? To do this, autonomous testing makes use of AI and ML.
Autonomous testing is the automated creation and maintenance of tests and the analysis of results using an application blueprint. Using machine learning algorithms, these tools cut through redundant data in testing and spot irregularities.
The transition from testing automation to fully autonomous testing is in and of itself a challenge. Most businesses’ current automation approaches can be summarized as follows:
While autonomous testing seeks to reduce test cycle times, chaos testing employs “Testing in Production” (TiP) to increase the fault tolerance of a built system.
Testing in production is based on the premise that only real-time input can successfully supplement the quality assurance process. Every version release is, in theory, a production test with high value-to-cost risk.
Chaos testing assesses the resilience of a system to unexpected events by inducing failure. Experts explain this by stating that it is preferable to break a system intentionally at the optimal time than to have it crash unexpectedly. With the rising importance of “mean time to repair” (MTTR), it is vital to minimise the recovery time of a business.
The origin of chaos testing dates to when Netflix transitioned to the cloud via AWS. The unexpected AWS outage caused the Netflix platform to be unavailable for several hours, inducing them to implement the concept of chaos engineering. Today, many tools are available to perform Chaos testing, such as:
Chaos testing is thus a proactive approach to testing, which can begin with small trials in controlled environments. It can then be effectively implemented and scaled up through proper rollback planning and appropriate tools.
Through the uphoriX Test-Accelerator, Indium has taken a step towards autonomy by providing low code automation, intelligent scripting, Script once NFT, a native test data generator, and swift compatibility. Indium considers smart test automation as an emerging vertical that ‘addresses one of the fundamental aspects of cloud-native initiatives – the velocity of code deployment’ and a prerequisite to the ongoing digital transformation.
By Uma Raj
By Uma Raj
By Abishek Balakumar