Bridge the QA Talent Gaps with Low-Code Test Automation
The U.S. Department of Labor estimated that by 2030, there may be an 85 million shortage of software engineers globally. Fields such as healthcare software quality assurance (QA) face even greater challenges due to accelerated digitization and increasing volumes of regulations about patient data. Yet the QA talent gaps may get even worse because of the following reasons:
- With every electronic medical record (EMR) vendor under pressure to bring out innovative products, there is a sheer dearth of QA specialists to keep up with the frequent updates and releases.
- Test automation comes with a learning curve as it requires multiple coding languages but also in-depth technical knowledge about libraries, frameworks, and more.
- Technology is moving at a much faster pace than it is possible to train or upskill the existing workforce in most healthcare organizations.
In this blog, we'll uncover the three causes of healthcare's QA talent gap, and how codeless test automation technologies can help overcome those challenges and revolutionize healthcare software testing.
Three Reasons behind Healthcare's QA Talent Shortage
There are three main challenges that healthcare organizations face in finding skilled QA professionals.
First, most providers are facing the daunting challenge of juggling multiple EMR systems, with an average of 16 per health system, according to the HIMSS data. Because these technologies are new, finding QA professionals skilled in Epic, Cerner, Meditech, or any of the other EMR products is becoming increasingly difficult. This results in significant costs, time, and effort spent on finding and training the right people.
Figure 1: The average US provider operated 16 EMR software according to HIMSS' 2018 database.
Besides, testers need to not only process a range of programming skills but also understand test automation frameworks. Due to increasing regulations over patient data security (i.e., HIPAA in the US), QA testers must divide their focus between testing, documentation, and compliance maintenance. This further stretches QA teams, making it difficult to keep up with the increasing volume of test requirements. The long-term consequences include loss of productivity, slower time to market, increased risk, and amplified costs.
Moreover, some organizations may not fully understand the critical role QA plays in patient safety and care outcomes. Consequently, they may have limited resources to attract top talent or provide upskilling or re-skilling programs for existing staff to adopt test automation tools.
To overcome these challenges, healthcare organizations can benefit from using codeless test automation to help fill the alarming gaps in their current testing practices to ensu43 compliance with the latest healthcare regulations.
What is codeless test automation?
Codeless test automation is an innovative approach to alleviating the QA talent shortage in healthcare. It eliminates the need for testers to write scripts manually. Instead, it usually has a graphic user interface (GUI) that allows testers to interact with the system, build models, and debug workflows without needing traditional programming skills. In this way, it empowers non-technical users to create and execute test cases, reducing the burden on highly skilled QA professionals.
Of course, as with any computer program, underlying these codeless interfaces are lines of code. Codeless test automation solutions simply obscure this code by having the user interact with a graphical layer on top of this code. This layer could be something akin to a flowchart or rely on keywords that reference particular sets of commands.
See it in action: A no-code modeling interface of Eggplant Test
Five Advantages of codeless test automation
#1 Building a versatile and effective QA team
EMR testing requires a combination of QA knowledge, domain expertise, and resources that can span across the organization. By automating the testing process, codeless test automation eliminates the problem of teams having to understand someone else’s code. This allows domain experts such as nurses and clinicians to be more involved in QA even if they have little or no coding experience. For example, a person with intimate knowledge of a clinical process can build a model to automate via drag-and-drop interfaces in minutes that their colleagues on product or test teams will be able to understand. This enables specialists from different disciplines to come together and reach a common understanding of what needs to be done.
#2 Reduced maintenance workload
With traditional test automation, any change in the application under test requires scripts to be updated accordingly, which can be a tedious and time-consuming task, especially as the scope of automation testing increases.
In contrast, codeless test automation relies on a visual interface or flowchart, allowing users to create automation workflows without writing scripts manually. Moreover, codeless test automation solutions often have built-in maintenance features that detect changes in the application and automatically update the test cases accordingly. This means that teams can spend less time maintaining code and more time exploring various edge cases that impact the user experience, improving the overall quality of the releases.
#3 Lower barrier to entry
By virtue of simpler tools that don’t require in-depth coding knowledge, more people can build automation workflows with ease. For example, Eggplant uses an English-like language, such as SenseTalk, making it more accessible and understandable to users who may not have a strong programming background.
For example, to check whether a quantity is divisible by 3, the SenseTalk script could use the expression:
"if quantity is divisible by 3 …" or "if quantity is a multiple of 3 …"
More traditional programming languages (C, Java, Python, etc.) would typically be written as:
"if (quantity % 3) == 0 …"
The first example emphasizes readability for users even if they lack in-depth coding knowledge. However, the latter focuses on machine operations to determine the result, rather than on creating something that is easy for humans to read and understand.
#4 Making quality everyone’s responsibility
With simple syntax and visual elements, users with different technical backgrounds can make use of codeless test automation tools. A wide range of users can be involved in building testing models and seeing the output of these tests, which creates a sense of shared responsibility for quality.
In addition, codeless test automation solutions often have built-in reporting and analytics capabilities that allow stakeholders to easily gain actionable insights, which help facilitate communications between QA and Dev teams to understand the testing process and take ownership of quality.
#5 Less reliance on manual testing and data migration
In traditional testing, manual testing is often time-consuming and prone to errors, especially when dealing with large amounts of data. With codeless test automation, users can automate mundane tasks such as data entry and validation. Thus, testers can focus on more human-centric testing, such as looking at usability, accessibility, or user experience.
The Age of Codeless + AI
The future of codeless test automation is looking bright thanks to advancements in artificial intelligence (AI) and machine learning (ML).
Computer vision is a field of AI that allows computers to gain understanding from images and video. This technology allows testing software like Eggplant Test to intelligently interpret onscreen actions in a system under test (SUT). There are two broad advantages here:
- In combination with optical character recognition (OCR), Eggplant can intelligently understand an interface and navigate it as a human would
- Eggplant can identify visual discrepancies that could impact the user experience. For example, computer vision could identify that a crucial button is hidden or obscured by another element, a no doubt frustrating experience for a real user
AI can also help testers identify user journeys that have not been tested yet. With the help of AI, testers can conduct exploratory tests and detect application issues that traditional linear test scripts would overlook. Additionally, AI can observe real user behavior in production environments and use it to train ML algorithms that will feed into future tests, ensuring that the testing process remains accurate, reliable, and constantly evolving.
Moreover, computer vision technology allows for non-invasive testing, which is critical for highly regulated and secure systems. With this technology, anything seen onscreen can be tested without needing permission to access the underlying source code, ensuring that testing remains secure and compliant with industry regulations. These advancements in codeless test automation hold great promise for improving the testing process, increasing productivity, and ultimately leading to faster delivery of high-quality healthcare software.
As the healthcare industry undergoes a rapid digital transformation, the need for high-quality software testing becomes more critical than ever. Unfortunately, the widening tech talent gap in healthcare software testing can lead to significant delays or software defects that pose increasing risks to patient lives.
Codeless test automation provides a robust solution to these challenges, automating the testing process and eliminating the need for in-depth technical backgrounds.
Are you curious about what's in store for the future of codeless test automation? How will it transform QA in the healthcare industry and beyond?
Don't miss out on a unique opportunity to hear from the inventor of SenseTalk, Doug Simons, at a special event on April 25th. During this event, you'll gain valuable insights into the future of low-code software testing and learn how codeless test automation is bridging the tech talent gap to create rapid value. Seats for this event are limited, so don't wait to register!