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New Year’s Resolutions: Rethinking Quality in 2024

It's a new year, and many of us in IT and testing are reflecting on how we can improve our processes and strategies. As we set our 2024 quality resolutions, let's reconsider our impulse toward ever-increasing test automation. Are we falling into the trap of trying to eat faster to lose weight? By only accelerating our efforts, we fail to confront the real root causes of inefficiencies.

Just as diet fads promise thinness through gimmicks, we’ve been sold a fantasy that more test automation will solve all problems. But while judicious automation provides value, many teams over-invest relative to the challenges they face. When you have a hammer, everything looks like a nail, so teams hammer away endlessly to construct vast automated architectures. Meanwhile, quality lingers at the same mediocre levels.

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The 3 Stages of an Effective Test Data Strategy

With the rise of agile and DevOps practices, software testing is more important than ever for delivering high-quality applications at speed. However, providing testers with the right test data remains a major bottleneck.

Many organizations have turned to test data virtualization and synthetic data generation tools to help alleviate these test data challenges. But, while helpful, these solutions alone are not enough for implementing a truly effective test data strategy.

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Model-Based Testing Can Lead the Way in IT Change

IT change remains a persistent struggle for most organizations today. Software teams are aware of the need to move faster and be more agile, yet they are dealing with growing complexity and the weight of unknowns within the ecosystem of their current IT architecture estate. The misinterpretation of Agile principles has seen a culture where documentation (of which test design is a part) has fallen by the wayside. Fortunately, for teams who appreciate that software engineering is a complex, emergent discipline, there are techniques for turning this situation around.  

Testing is a key part of this solution. Testers can help uncover and formally document knowledge needed to: 

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Accidental Complexity Is Killing Your Testing Efforts and IT Budget

You’re working hard to transform your ways of working with a range of different goals. Common aims of digital transformations include:

  1. To become more Agile;
  2. To deliver faster through DevOps;
  3. To migrate all of your systems to the cloud;
  4. To enable regular change.

Whatever your desired outcome, there’s one common problem that most (everybody really) ignore. Yet, overlooking this problem ultimately means that the initiative will fail, become delayed, cost too much, or generally become severely hampered going forward.

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Going Lean on Your Testing Approach

When teams are looking to transform, optimize, or cut costs in testing, where do they first look? More often than not, they follow the advice given by outside consultancies: “You haven’t got enough test automation,” or “Your peers have achieved xx% tests executed through automation.”

There are a number of problems with this advice:

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Test Data Compliance: How to Rewrite Your Organization’s DNA

“We mustn’t use live data for testing.” This is the reason why most organizations start to look at superficial solutions to certain challenges that are ingrained in their DNA. For years, this aversion has driven the way that organizations have changed their “best” practices, struggling to wean themselves off deep-set habits.

These organizations often start with low-hanging fruit and create a capability to replace live data with either masked/obfuscated data or synthetic alternatives. They then believe that’s “job done!” It isn’t. It doesn’t tackle or even reduce many of the core challenges associated with using production in test, let alone the systemic problems that led the organization to test using production data in the first place.

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Continuous Development: Building the Thing Right, to Build the Right Thing

Test automation is vital to any organization wanting to adopt Agile or DevOps, or simply wanting to deliver IT change faster.

If you ask a stakeholder “What do you hope to achieve from your testing?” the common answer you receive is to assure quality. But the more you delve into organization dynamics of test automation, the answer appears to be different.