As businesses in all industries continue to grapple with inflation, economic volatility, geopolitical concerns and lingering supply chain issues, leaders are working diligently to increase revenue, deliver on customer experience expectations, and provide greater operational efficiency.
Software development is a core revenue driver for all businesses today due to the strong correlation between a successful Agile development team and great customer experiences. Consumers have very little patience for subpar experiences, which has led companies to be intensely focused on ensuring high-quality applications are being delivered. Unfortunately, software development life cycle (SDLC) bottlenecks due to quality engineering (QE) efforts can significantly delay time to market, opening the door for competition. At the same time, organizations are looking at ways to significantly reduce their IT operating costs. Fortunately, achieving the operational efficiency goals for the business does not have to come at the expense of quality and customer experience.
Automated testing processes enable teams to quickly and easily increase their productivity and decrease the risk for human errors within the SDLC. Test automation technology has been mature for the past decade. For the first time, with the advancements achieved with AI, QE teams are able to maintain the same pace as their software development counterparts and provide quick feedback, informing them if they will diminish the customer experience with the release of their code.
Application teams usually have two primary goals during a release cycle: (1) to not break the customer experience and (2) to make it better with the newly released code. There is greater focus on ensuring that the customer experience is not negatively impacted compared to the effort to ensure new features work. And that’s where test automation can not only help lower the TCO, but also do a much better job in ensuring the current customer experience is not broken compared to non-automated approaches.
There are six primary areas where successful companies are improving the total cost of ownership of software testing:
Shifting from manual to automated testing
By increasing the level of test automation in the software development life cycle, especially in regression testing, quality engineers can focus their efforts on defining the complex test scenarios for the new features being developed. This can be accomplished effortlessly with the latest iterations of AI tools. Zero-maintenance automated tests can be generated based on real user data, which means any impact to customer experience in the current code base will be identified prior to release in a fraction of the time compared to before.
Democratizing test automation through low-code/no-code solutions
The biggest barriers preventing a QE team from automating tests are the steep learning curve, the lack of time to undergo training, and the high cost of test automation engineers. That’s where low-code/no-code automated testing solutions help QE teams create automated tests without requiring them to go through deep technical enablement. They can stay focused on leveraging their SME knowledge to build the best test coverage possible to avoid negative customer impacts, while decreasing the TCO by spending less time running slow, manual tests.
Identifying defects earlier in the testing cycle
When developers must fix a bug from code written several days earlier, it brings their productivity down. They have to fix old code instead of writing new code, and spend much time and effort to understand the previous code’s context before effectively fixing it. Having automated tests run as part of the Continuous Integration (CI) process ends context switching for developers. They receive immediate feedback on whether their new code is going to break customer experience (i.e. app regression). They can then immediately address issues before starting to work on the next story from the backlog, which directly translates to time and effort savings.
Consolidating point solutions within a comprehensive software quality platform
At the heart of any cost optimization effort is technology or tool rationalization. Reducing the number of tools and vendors in any IT ecosystem is proven to deliver savings while increasing team productivity. Having a common, all-inclusive platform to create, maintain, run, manage and analyze tests enables cross-team collaboration and reusing testing assets that would otherwise need to be re-created if each team was using their own point solutions. That directly drives down the software testing TCO, while promoting testing coverage across teams that minimize the impact on customer experiences.
Shifting testing environments to the cloud
When it comes to ensuring the best customer experience, companies look for running tests against the broadest variety of browsers and mobile devices, reflecting how users interact with the company’s applications. Building and maintaining the infrastructure to host those browsers and mobile devices is expensive and inefficient. Companies that choose a common, all-inclusive testing platform typically realize savings of 66% in software testing TCO, while delivering a much better customer experience with the broadest testing combination of browsers and mobile devices.
Applying AI across the lifecycle to accelerate time-to-value
The hype around AI is obfuscating the real use cases that can augment QE teams productivity through capabilities that (1) accelerate progress, (2) generate insights and (3) drive optimizations across the software testing lifecycle. One such AI-powered use case to lower software testing TCO is through automatically generating zero-maintenance regression tests. This enables QE teams to focus on new feature testing while still ensuring no impact on customer experiences on the next release.
Successfully managing software testing TCO in the current business landscape involves a strategic approach that balances cost efficiency without compromising quality and, subsequently, customer experience. By shifting towards automated testing, leveraging low-code/no-code solutions, identifying defects promptly, consolidating tools, migrating testing environments to the cloud, and harnessing the power of AI, companies can strategically streamline their software testing processes. This approach ultimately delivers exceptional customer experience while effectively managing the TCO of software testing amidst economic challenges and rapidly evolving market demands.