Tag: Quality Assurance

Hardly a week goes by without news of a ‘technical issue’ or outage. Sainsbury’s, Marks & Spencer and Tesco Bank are just some of the well-known brands that have experienced tech meltdowns in recent weeks.

We’ve come to expect IT crashes as a part of life, but if handled poorly, they can snowball into a major crisis, tarnishing a company’s reputation, eroding consumer trust and resulting in lost sales.

Here are five safeguards brand owners can put in place to protect their sites and minimise fallout from an IT crash.

Be alert

Do an audit of your site at least once a year with penetration (PEN) testing, where you look for any vulnerabilities in any of your systems. It’s not a guarantee that your site is absolutely secure and glitch-free, but as a brand, at the very least, you will have tried to identify any potential issues and protect your site and stored data.  Being proactive with system security alongside testing and QA reduces the risk of outages drastically.

Alongside PEN and code testing, you need to know when systems go down. There is nothing worse than a customer notifying you that your website or platform doesn’t work.

Setting up monitors for your systems to notify you is the first step of your action to an outage. Depending on your user, you can even make these publicly accessible, like Slack (https://slack-status.com/), and other platforms so your users are aware of this issue as it happens.     

Consider your site structure

It is possible to limit outages to specific parts of a site, but it will depend on how your website or platform is built and whether different parts of it are hosted on separate services, for example.  This approach could help contain the fallout from an outage. Take X, formerly Twitter: its likes and tweets are kept separate where microservices are used for each, so if ‘likes’ were to go down, tweets would still be visible. We would advise this type of structure for brands that would benefit from such an approach. A microservices set up would benefit anyone that’s creating a platform for users need to complete things in, such as banks, ecommerce but not needed for things like marketing websites and ‘brochure’ websites.

Post-crash follow ups

The level of testing required and the number of times this is needed will depend on the size of the company, its user base and its product.  It is also essential to take tech developments into account, all of which can impact even the most robust of sites. We recommend PEN tests once a year, but above all, be vigilant, take customers seriously and respect their data.

If you’ve suffered an outage, there’s nowhere to hide. Being proactive rather than reactive shows that you care, which can make a big difference to your reputation.

To Know More, Read Full Article @ https://ai-techpark.com/when-tech-glitches-threaten-your-brand-perception/ 

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We believe that the journey of developing software is as tough as quality assurance (QA) engineers want to release high-quality software products that meet customer expectations and run smoothly when implemented into their systems. Thus, in such cases, quality assurance (QA) and software testing are a must, as they play a crucial role in developing good software.

Manual testing has limitations and many repetitive tasks that cannot be automated because they require human intelligence, judgment, and supervision.

As a result, QA engineers have always been inclined toward using automation tools to help them with testing. These AI tools can help them understand problems such as finding bugs faster, and more consistently, improving testing quality, and saving time by automating routine tasks.

This article discusses the role of AI in the future of QA engineering. It also discusses the role of AI in creating and executing test cases, why QA engineers should trust AI, and how AI can be used as a job transformer.

The Role of AI in Creating and Executing Test Cases

Before the introduction of AI (artificial intelligence), automation testing and quality assurance were slow processes with a mix of manual and automatic processes.

Earlier software was tested using a collection of manual methodologies, and the QA team tested the software repetitively until and unless they achieved consistency, making the whole method time-consuming and expensive.

As software becomes more complex, the number of tests is naturally growing, making it more and more difficult to maintain the test suite and ensure sufficient code coverage.

AI has revolutionized QA testing by automating repetitive tasks such as test case generation, test data management, and defect detection, which increases accuracy, efficiency, and test coverage.

Apart from finding bugs quickly, the QA engineers use AI by using machine learning (ML) models to identify problems with the tested software. The ML models can analyze the data from past tests to understand and identify the patterns of the programs so that the software can be easily used in the real world.

AI as a Job Transformer for QA Professionals

Even though we are aware that AI has the potential to replace human roles, industrialists have emphasized that AI will bring revolutionary changes and transform the roles of QA testers and quality engineers.

Preliminary and heavy tasks like gathering initial ideas, research, and analysis can be handled by AI. AI assistance can be helpful in the formulation of strategies and the execution of these strategies by constructing a proper foundation.

The emergence of AI has brought speed to the process of software testing, which traditionally would take hours to complete. AI goes beyond saving mere minutes; it can also identify and manage risks based on set definitions and prior information.

To Know More, Read Full Article @ https://ai-techpark.com/ai-in-software-testing/

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