Jul 30, 2020

Speed vs quality: Can automated testing solve this dilemma?

AI
Innovation
Technology
Steen Brahe, Product Manager, ...
4 min
lady at laptop
New applications and digital services that come with this increased pace of innovation must be tested thoroughly...

 The mainframe has dominated the IT space for more than half a century and its relevance shows no sign of waning. The platform continues to power the digital economy, with 71% of global Fortune 500 companies relying on it for critical transactions. What has changed, however, is the speed at which customers expect access to new and improved digital services. In today’s market, it’s the companies that can provide a constant cycle of ‘new’ that reap the most rewards – so the mainframe has to keep up with increasing demand for speed and innovation.

However, the new applications and digital services that come with this increased pace of innovation must be tested thoroughly, to ensure they perform as expected when put in front of the user. As the frequency of mainframe software updates continues to increase, and applications become ever more complex, the testing process inevitably takes longer, leaving less time to drive innovation. Research conducted by Vanson Bourne has shown that on average, development teams now spend 51% of their time on testing during the release of a new mainframe application, feature or functionality – in an age where speed to market can make the difference between failure and survival, this is simply unsustainable, but organisations cannot be forced into sacrificing quality for speed.

Acceleration or accuracy?

Digital transformation and an ever-growing need for speed has increased both the frequency of release cycles and the complexity of application environments. This has ramifications for developers when it comes to testing new digital services, as there are far more interdependencies and much more code to check. In fact, 92% of mainframe teams now spend more time than ever testing code during the release of a new application, feature or functionality to ensure services will work when deployed. This is largely due to companies continuing to use manual testing practices when it comes to the mainframe, creating bottlenecks that slow innovation and prevent companies from meeting key business goals.

The innovation bottleneck caused by the current testing status quo is only set to intensify, as companies face a growing mainframe skills gap – a Forrester Consulting study shows that only 37% of retiring mainframe developers have been replaced in the last few years. Naturally, companies relying on these shrinking teams of mainframe specialists will increasingly struggle if left with only manual means to rigorously test the code that powers their digital services – running the risk of bad code being sent into production as businesses prioritise speed of innovation. This is a real concern for application development managers, with the Vanson Bourne study revealing that 80% believe it is inevitable bad code will make its way into production unless they can automate more test cases.

Testing the solution

As customer demand for new and improved digital services shows no sign of slowing, IT departments must find a solution to help them deliver innovation quickly, without risking bad code going into production. As a result, many are looking to automated testing for the answer – in fact, 90% of businesses think automating more test cases is the single most important factor in accelerating innovation. “Shifting left” – performing software tests earlier in the development lifecycle – combined with automated testing, can help solve the issue of delivering software faster without compromising quality. This enables developers to automate repetitive tasks and execute tests earlier in the development cycle, so they get fast feedback on mainframe software updates. As a result, code quality issues are identified and solved quicker and the costs to resolve them are lower; innovation is accelerated while risks such as introducing problems that disrupt operations, present security risks, hinder customer experiences or impact business revenues, are minimised.

Automated testing also makes it easier for those new to the mainframe to do development and testing. Development teams need to focus their time on application development and other value-added tasks, and let automation take over the more mundane tasks. Ultimately, test automation will help businesses to quickly deliver new digital services and improve code quality, velocity and efficiency on the mainframe. This is crucial when it comes to companies meeting their business goals and keeping pace with increasingly agile digital native competitors.

Innovation is key

Large organisations feel more pressure than most to speed up innovation as they fight to compete with digital native challengers. As businesses aim to improve the pace of innovation to better serve customers, test automation and “shift-left testing” are crucial. Through this, development teams can benefit from a “force-multiplying” effect, getting more work accomplished by fewer, and speed can be achieved without compromising code quality or efficiency. The benefits in the long-term will enable companies to accelerate innovation and drive their business forward faster than ever before

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Jun 23, 2021

Google launches Visual Inspection AI tool for manufacturers

AI
Google
Manufacturing
ML
3 min
Google has launched Visual Inspection AI, a new Google Cloud Platform solution designed to help reduce defects during the manufacturing process

Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped. 

Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale. 

How does it work? 

The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.

Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI. 

  • Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor. 
  • Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps. 
  • Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers. 
  • Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
  • Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.

Industry use cases

The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing. 

Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”

Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture. 

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