That said, many organizations struggle with achieving a reliable and consistent level of quality for their digital applications and products. According to recent research, poor software quality cost organizations $2.41 trillion in the U.S. alone in 2022. There are a few reasons behind this digital quality pain point facing brands today, including a lack of both expertise and a consistently executed digital quality strategy. Additionally, because modern digital applications and platforms are often quite complex with multiple interfaces and technologies, it can be a challenge to manage and prioritize quality.

A dedicated focus on digital quality is necessary for companies to achieve the level of quality they have their sights set on for digital products. Quality needs to be built into and prioritized within the software development life cycle (SDLC), and expertise across all quality disciplines (functional, usability, localization, user experience, etc.) must be part of this approach. Unfortunately, the resources and talent required to build and add digital expertise in house can prove to be challenging for many companies. However, this growing demand for digital quality expertise has led to the rise of a new service model, Digital Quality as a Service (DQaaS).

Defining Digital Quality as a Service

Digital Quality as a Service (DQaaS) refers to a managed-service engagement in which a provider commits to achieve a pre-agreed upon level of quality outcomes for a client. The methodology for achieving that outcome is usually left up to the provider, and is likely to involve a mix of elements including exploratory testing, accessibility testing, usability testing, test case execution, and automation.

A provider will write and execute test cases, deliver feedback, and ensure alignment with the client’s roadmap of the product. Aligning and collaborating should provide a frictionless journey where the provider and client are on the same page for level of test coverage and where in the SDLC testing should take place.

Organizations leveraging DQaaS gain access to shared service teams that can support their own software development teams. The shared services team offers consultation, expertise on quality, and provides resources to specific product teams depending on priorities and goals.

Digital Quality as a Service Benefits

The benefits an organization receives from adopting a DQaaS approach to their product development and deployment include:

● Expertise: With a shared service team model, an organization isn’t just getting a group of testers to find bugs. These are experts who can assess and prioritize bugs, manage a team of testers and deeply understand different types of testing. They also know how to implement testing approaches and improve processes by adding elements like automation and customer journey testing.

● Holistic Approach: Conceptually, DQaaS approaches quality holistically. A shared services team provides experience and expertise across functional, usability, performance, payments, accessibility testing and more. The purpose of the team is to deliver end-to-end quality for organizations, ensuring features and entire products are built up to a certain quality standard and encompassing the entire customer journey.

● Broader Quality Perspective: Without being tied to a specific department, a shared services team can see the bigger picture after working with individuals and different teams. Learnings and best practices from one team can then be applied to others, leading to a more aligned, collaborative and educated overall team.

● Plug-In Service: Organizations often know they can be doing more when it comes to quality for their software, but they don’t have the resources to hire someone full time to fill in the gaps, or have the expertise to know exactly what needs fixing. A shared service team plugs right into current workflows, providing that expertise and those resources.

● Bespoke Strategy: The concept of digital quality is not the same for everyone. While there are frameworks and best practices, a variety of factors, including available resources, Agile teams, features and number of products, all weigh into the overall strategy that goes into quality. A shared services team can advise clients on what the approach to quality should look like for their specific organization.

Crowdtesting and Digital Quality as a Service

Crowdtesting digital applications and products can go hand-in-hand with the implementation of DQaaS. Crowdtesting provides real-world testing with real devices. When this is part of a DQaaS agreement, dedicated resources find and manage bugs across different software types, devices, and locations, while also consulting with QA on next steps and helping to drive testing maturity through the creation of automated regression suites.

Companies leveraging this approach benefit from having real user feedback and perspective during product development, while simultaneously having the ability to improve QA processes to meet the quality requirements they are looking for before a product is pushed live.

Building a Culture of Quality

Consistent product quality goes a long way toward defining a brand and building customer loyalty. Investing in the resources and expertise needed to make sure the highest level of quality is achieved for a product helps to build an overall culture of organizational quality. From individual developers, to QA teams, and business leaders, prioritizing quality and making it part of an organization’s culture is a worthwhile endeavor with the results speaking for themselves.

To learn more about how Applause uses Digital Quality as a Service, visit the website here.

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The post The Next Evolution of Software Testing: Digital Quality as a Service appeared first on Digital IT News.

It’s (Still) All About The Data

The use cases for AI are getting more and more complex as organizations from retail, banking, automotive, healthcare and more look to implement AI. Many are finding these implementations much more difficult than expected because they underestimate the work that goes into proper data collection and training models properly. These organizations need different data sets and inputs, made up of authentic voices, documents, images and sounds, depending on the algorithms requirements. Essentially it’s source quality data at scale.

Foundation models (deep learning algorithms based on broad sets of unlabeled data) can help, but raise crucial questions about ethics and compliance. If the foundational data is flawed, or biased, so too will the outcomes be. It is difficult for algorithms to ‘unlearn’ patterns, so it is important that biases are not built into the algorithm from the earliest phases of implementation. For example, the most powerful language model ever created – the Generative Pre-Trained Transformers 4 (GPT-4) released in March this year does not reveal what data sets it is trained on citing competitive reasons. This raises serious ethical questions. Organizations will need to build governance and compliance into their development process and timelines to ensure that their machine learning models are not amplifying existing biases in datasets.

Test….and Test Again

While AI language models have been and will continue to be trained using large amounts of data, organizations continue to underestimate how much data they actually need. More training data means more learning for algorithms. Early or smaller sample sizes make it difficult to identify trends and make accurate correlations. Getting it right requires sufficiently representing all attributes of human nature, which means that organizations will still need people to test, develop and improve AI.

We’ve all witnessed how even the largest of tech companies have recognized the need for rigorous testing that combines real world external feedback with internal testing.

Avoid Costly Mistakes

Crowd-based testing can introduce a human element to help uncover issues that lab-based or structured test cases can miss, and can curate training data.

Crowdtesting allows companies to get feedback from a diverse group of users, which can help identify potential sources of bias in the model. By testing the model with a wide range of users in real-world scenarios, companies can identify issues that may have been missed during the development process and take steps to address them. This method also helps identify issues related to user experience and helps establish a feedback loop between companies and their users to ensure that the AI application continues to evolve and improve.

By leveraging the power of the crowd, companies can speed up the amount of time it takes to label large amounts of data, ensure quality control, and improve the diversity and relevance of their training data. By having multiple people label each data point, companies can identify discrepancies and errors, and take steps to address them. They can also have testers generate new examples of data that are relevant to the task at hand, improving the overall diversity and quality of their training data.

While the excitement and activity so far around AI is incredible, the remainder of 2023 will be a year of learning for AI teams and users. Incorporating and working with data from a broad variety of people with different backgrounds, experiences, and ways of thinking and behaving, will be time well spent toward eliminating bias and further advancing AI.

For more information visit the Applause website HERE.

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