These 3 Quality Assurance Issues Are An Obstacle to Strategic QA Success

As technology evolves, business areas that were once cumbersome cost centers transform themselves into key strategic assets.

It happened with customer support and experience. It happened with the supply chain (think about how much business next-day delivery and free returns drive for Amazon). Now it's happening with software testing and QA.

Quality assurance issues, once seen as part and parcel of building digital products, can now be solved through smarter testing processes and innovative use of AI technology. More efficient QA processes are now an indispensable tool for deploying better quality products, faster.

As the most successful product teams deploy code 200 times more frequently than their competitors, transforming your QA into a strategic asset is vital if you want to thrive in an increasingly dog-eat-dog market.

Here are 3 quality assurance issues holding you back, and how to turn them around to unleash QA's strategic potential.

3 Common QA Challenges And How to Solve Them

1. Test Maintenance Workload

Tests flake or need updating for a variety of reasons - but one of the most frustrating and time consuming is UI/DOM fragility. What do you mean that every small UI change we make results in more tedious test maintenance? Urgh.

As you automate more tests, and the tests you automate become more complex, the time you spend on maintenance increases. It's easy enough not to notice a once-manageable test maintenance workload eating into your team's schedules - until it's become a bigger problem than it needs to.

How to Tell That Test Maintenance Is Holding You Back
Are your QA team underestimating testing timescales or missing internal deadlines? Or, if your engineers test their own code, are they finding it difficult to dedicate time to writing or updating product code? Test maintenance commitments could be the reason.

Flaky tests produce unreliable results. If you're seeing an influx of bugs in code you swore you'd tested extensively, it might be because a poorly maintained test suite is throwing out false positives. Whether it's poorly maintained due to lack of effort or lack of time, that's a problem that needs solving asap.

Why This Impacts Your Organization
Not dedicating enough resources to test maintenance results in bugs slipping through to production, which creates a poor experience for your users.

On the other hand, dedicating too many of your resources to test maintenance restricts your ability to release updates and new features that keep you ahead of your competitors.

It's the definition of 'between a rock and a hard place'. Maintaining the right balance requires constant evaluation of your existing resources, your business goals, and your test coverage.

How To Streamline Test Maintenance and Improve Test Quality
Automated tests (especially complex ones): coded by people prone to human error, liable to flake and produce unreliable results without extensive maintenance.

Tests automated with AI-enabled software: low code for minimum error potential, self-healing with no maintenance required (even complex end to end ones). They automatically update when you make changes to the DOM, so you're not constantly maintaining your tests manually as your UI evolves.

Test maintenance isn't fun. It doesn't result in cool new features for your app. It doesn't stretch your team's brains in a way that's engaging - and bored, unengaged engineers let errors slip through. If you want fast, accurate QA that can keep pace with your development schedule, it's time to hand test maintenance over to the machines.

2. Siloed Engineering and QA Teams

How involved are your engineers in testing the code that they write? Does your QA team understand your codebase well enough to create strong, relevant test cases, and to direct their efforts where there's most risk?

If your QA and engineering teams are completely separated, chances are the answer to both of these questions is 'no' - and that's one of the most significant software testing challenges if you want to release quality digital products at pace.

How to Identify Problematic Silos
If you run your software testing using an 'over the wall' approach to QA, the chances of silos developing between engineering and QA are pretty high. Telltale signs to look for include delayed feedback cycles, poor communication between teams, and a reduced sense of ownership over code and testing processes.

Why This Impacts Your Organization
Silos cause inefficiencies that result in poor-quality code, frequent bugs, and slower time to market. In a landscape crammed with potential competitors, that's a fast-track to dwindling revenues and a depleted user base.

Separating your engineers from testing processes doesn't encourage them to think about the testability of their code. So, engineers create code that's hard to test - which slows the process down even further and risks building unhelpful technical debt that hampers long-term growth.

How to Solve Engineering/QA Silos
You'll need to move away from the hand-off model if you want your testing processes to support the speed of development your business needs to thrive.

Instead, use the 'Shift Left' approach to software testing to run development and testing processes at the same time. You test early and often, pick up bugs when they are easier and cheaper to fix, and remove testing team bottlenecks that add unwelcome days and weeks onto release cycles.

Another way to eliminate engineering/QA silos: get rid of traditional QA roles entirely and adopt a 'You build it, you own it' approach to testing code.

With the help of AI tools to minimize workload, engineers test the code they create. This makes code easier to test, facilitates full testing integration into CI/CD processes, encourages uptake of the Shift Left Approach, and speeds up feedback loops by cutting out the middleman.

3. The Resources Squeeze

It's a tough old market out there. Organizations are tightening their belts in the face of recession, tariffs, and rounds of layoffs, and budgets are looking leaner as a result. Engineering teams need to face up to the fact that the levels of resources they are used to aren't there anymore, and learn to do more with less.

How To Identify Resource Issues
Most engineering teams would love more resources at their disposal - but there's a difference between daydreaming about onboarding an extra pair of hands and hanging onto release schedules by the skin of your teeth.

If your engineers are putting in large amounts of unpaid overtime, deadlines are getting missed, and routine tasks are lingering on the to-do list for unwelcome days and weeks, chances are you're under-resourced.

Why This Impacts Your Organization
Overworked engineers take shortcuts to hit key deadlines. You don't want them to find the fastest way to create a test case, because that's a recipe for poor test code and unreliable results. You want them to find the best way.

Also, piling more work on your engineering team whilst offering neither a salary increase that reflects their extra workload, or any resources to help them manage it, is simply painting over the cracks. Long-term, this will cause increased employee burnout, dissatisfaction, and turnover - which means massive productivity losses for your organization.

How to Do More With Less Time and Money
There's a reason that around 77% of tech businesses are either actively investing, or planning to invest in, AI-led QA tools.

With AI, you don't have to choose between 'faster' and 'better' - you get both, at a fraction of the cost of maintaining an external QA team. Natural language processing allows you to build automated tests in minutes, no coding required, so you can significantly expand your test coverage whilst using fewer resources than ever before.

This means that, even on a shoestring budget, you get a) better QA with no room for human error, b) major efficiency gains via AI features, and c) big savings in both engineer hours and budget, with the need for external QA significantly reduced.

Momentic: The Answer to Your Quality Assurance Issues

“Momentic makes it 3x faster for our team to write and maintain end to end tests.”
Alex Cui, CTO, GPTZero

Momentic's AI testing tools eliminate bottlenecks, expand your test coverage, test your code faster, and allow you to test early and often. The result? Your QA challenges solved - quality code with minimal bugs, shipped faster than ever before.

If, like Alex and his team, you're keen to save over two thirds of the time you spend on key testing processes, why not schedule a conversation with our team?