AI & Automation To Propel QA To The Next Level
When it comes to digital transformation, the vision and focus for most enterprises is on customer experience, service, reliability, business outcomes, revenue, reshaping infrastructure, and applications.
Every digital program immediately runs into an agile development framework or DevOps. The focus is on shorter releases which put pressure on teams to deliver quality code in a shorter timeframe.
In most cases, the QA strategy is overlooked when planning additional controls and DevOps. This is due to Quality Assurance (QA) being deemed as an appendix and not a requirement. Therefore, teams need to consider QA as a crucial link between development and operations.
Forces Driving QA
Two significant forces drive QA. First is agility, meaning testing is performed faster to market, and AI backs automation. For the QA teams to keep pace with the full agile development mode, there must be a clear understanding that traditional test automation is not enough. Adopting Artificial Intelligence (AI) is necessary and unavoidable when it comes to testing automation.
During the initial stages of automation, the focus was primarily UI and regression-based, which has since evolved. Automation has become a keyword, data, and hybrid-driven process which transitioned to a business process-driven framework; and later materialized as small savings for clients. Although the savings were minimal, the focus on automation regression testing made little to no impact on businesses. Today, testing organizations must continuously innovate by learning and adapting to new and emerging technology solutions, focusing on implementation automation.
Testing and UI Automation
In a sense, testing has advanced from a black box to a white box methodology. As of today, the focus is leaning toward UI as well as multi-tier and multi-stack automation. In theory, this approach may enhance the overall efficiency and product time to market. As automation evolves and comes to market, organizations should explore the best strategies and most effective tools required for automation solutions.
In cases when teams are unable to identify solutions to address and resolve an entire issue, it’s not feasible to go through the process of more predictable testing, especially when it comes to AI. Shifting from defect detection to defect prevention Robotic Process Automation (RPA), few organizations use RPA tools, which have gone beyond traditional activities (ex., self-driving cars).
Teams may wonder, “should testers move away from the entire testing process and instead have a robot perform the work?
The focus of companies is usually on three significant areas, such as the right time to market and quality décor and cost reduction. However, more organizations are concluding that the quality output makes a direct impact on the end-user experience.
Traditional testing focuses on specific use case scenarios that effectively discover and validate the intended functionality while understanding how the system operates. However, any framework, such as Test-Driven Development (TDD) or Behavior Driven Development (BDD), specifically focuses on business processes. But is that enough? Don’t forget that no matter the amount of manual testing executed, bugs not detected in pre-production will most definitely materialize in post-production.
AI is the solution
AI is the solution to testing efficiently, and it does not overburden staff or exhaust resources.
Another considerable challenge testers have the comfort level that all functionality was tested. Testers are only as good as the requirements. When requirements are fully captured, testers have a better chance of being successful during the testing phase. If conditions are incomplete, testers have little to no chance of performing an accurate and complete job.
It is highly crucial to submit output quality. Testers should be able to pinpoint and communicate all evaluated areas and identify functionality that requires no testing.
AI-driven automation testing
Automation testing is about generating effective methods to identify bugs in coding, scripting, manual testing, and recording without human intervention. In short, it helps to increase coverage with little to no manual labor. Therefore, AI can play a huge role in integrating into QA for functional testing, non-functional testing, and security aspects.
The aim is to have the testing team perform tests efficiently and accurately without becoming overburdened or burned out from writing a new set of chords, new scripting, manual testing, and new recording. However, testers need to have support and enlarge as a team to have a successful outcome. That said, this is precisely the point where the IQ becomes useful.
AI IQ helps augment testers by removing the human element, which reduces testing capacity. Also, it ensures testing is efficient throughout the entire testing cycle.
One may think that if AI is doing a majority of the work, at what point is there genuine human involvement? Before providing an answer, let’s break down the definition of the blueprinting template.
What is a blueprinting template? It is a template that teaches the AI engine how to play with an application. Organizations work alongside testers to teach the AI engine how to adhere to business rules.
Business rules encompass AI hinting. As per the blueprinting template, the human-assisted machine learning will have some human factors uploaded to the AI engine, such as what to do, what not to do, where to start, where to stop, and what to execute every time, most of the time, some of the time or not at all.
But it doesn’t stop there. Upon the completion of the blueprinting template, testers must run the process at each bill. Therefore, testers must once again get involved by verifying the template is acceptable. Please remember that the blueprint template is effective for performance and security testing during any interface installation. However, testing is a blueprint of an application covering a core flow perspective rather than a testing perspective.
Predictive & prescriptive perspectives
The maintenance of the script is the most significant pain point or area for any automation.
QA can transform into an AI QA, which opens an opportunity for testers to focus on improving code quality and functionality, resulting in a positive impact on the end-user. AI enhances the ability to deliver by visualizing what QA must do to send a solid product to the market without the need for reuse or rework without increasing coverage. QA can take on different shapes, and the adoption of AI will the experience for the testers and customers.
Why Kairos Technologies?
Kairos Quality Engineering Center of Excellence (QECOE) is an integrated solution to achieve Enterprise Quality Governance (EQG).
Tap the value from our QECOE in three quick ways from our live EQG Dashboard:
• Visualization into the consumption of test assets and centralized results.
• Actionable information in critical decision-making from a Quality standpoint.
• Accountability of the QA/testing time, costs, resources, and other investments made.
The discrete test processes, technologies, and methods produce the best results when standardized. Today, you can optimize continuous testing efforts with various techniques and innovative tools beyond traditional Test Automation.
Kairos QECOE is a ready framework of our internal test organization, used to establish a value-based service for our enterprise customers.
Testing costs are mounting upwards in most enterprises, notably when the teams and resources are isolated across projects. Over time, we have pioneered creating a shared on-demand pool of testers that get provisioned to meet the specific (and changing) needs to assure quality.
Founded in 2003 and based out of Irving, Texas, Kairos Technologies is a US-based customer-first technology company.
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