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How to Leverage Exploratory Testing to Uncover Bugs

Rob Mason
Applause

Development teams so often find themselves rushing to get a release out on time. When it comes time for testing, the software works fine in the lab. But, when it's released, customers report a bunch of bugs. How does this happen? Why weren't the flaws found in QA?

Welcome to defect fatigue, a common issue that occurs when testers execute the same repetitive automated and manual tests and as a result, skip over or miss defects. Testers need to get creative and investigative to find these well-hidden defects before they slip past QA and into the hands of customers.

Be an Investigator and Break Things

The purpose of exploratory testing is to find defects by breaking application functionality using manual and automated techniques without repetition. The "without repetition" piece is key, and the idea is that teams test to break, rather than confirm. Testers should manipulate connectivity, security, configuration settings, and different user navigation, among others. Other techniques include:

■ creating mind maps to find testing areas to investigate

■ forcing the application to function outside the known paths

■ triggering unexpected errors to discover missing error messaging paths

■ exercising back-end processing and third-party software integrations to see what can be interrupted or failed by unexpected user actions

If an application supports different user roles, testing should be done from these different perspectives and with their respective settings. Testers can also utilize existing browser development tools to find errors that are not always visible in the application UI, and to test and edit to see how the application responds.

Consider the People Element

Testers are also people that use applications every day. They should draw on their own personal experiences with typical application defects to try and break functionality. They should also consider the habits and behaviors of the members of the software development team.

As developers and product managers work more with an application over time, they start to develop habits that may influence how they interact with the software. For example, some developers may only develop code on a local machine while others may only do code reviews instead of pre-testing in a test environment. These are work habits that can lead to defects. On the product side, many product managers habitually create user stories and requirements in the same way, unintentionally leaving out a relevant workflow or configuration setting.

Finding hidden defects requires testing against the grain rather than verifying a function performs as expected. It also requires testing all possible paths that customers might take. Crowdtesting can supplement existing techniques by using real people to serve as proxies for customers. They can test for quality, user-experience and functionality outside the lab, and provide instant, useful feedback.

Testing repeatedly only to have bugs unearthed later by customers is a frustrating and potentially costly endeavor. When testers mix existing techniques with creativity and an understanding of human behavior, they will be able to dig deeper to find bugs and friction points that ultimately improve quality and customer experience before release.

Rob Mason is CTO of Applause

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How to Leverage Exploratory Testing to Uncover Bugs

Rob Mason
Applause

Development teams so often find themselves rushing to get a release out on time. When it comes time for testing, the software works fine in the lab. But, when it's released, customers report a bunch of bugs. How does this happen? Why weren't the flaws found in QA?

Welcome to defect fatigue, a common issue that occurs when testers execute the same repetitive automated and manual tests and as a result, skip over or miss defects. Testers need to get creative and investigative to find these well-hidden defects before they slip past QA and into the hands of customers.

Be an Investigator and Break Things

The purpose of exploratory testing is to find defects by breaking application functionality using manual and automated techniques without repetition. The "without repetition" piece is key, and the idea is that teams test to break, rather than confirm. Testers should manipulate connectivity, security, configuration settings, and different user navigation, among others. Other techniques include:

■ creating mind maps to find testing areas to investigate

■ forcing the application to function outside the known paths

■ triggering unexpected errors to discover missing error messaging paths

■ exercising back-end processing and third-party software integrations to see what can be interrupted or failed by unexpected user actions

If an application supports different user roles, testing should be done from these different perspectives and with their respective settings. Testers can also utilize existing browser development tools to find errors that are not always visible in the application UI, and to test and edit to see how the application responds.

Consider the People Element

Testers are also people that use applications every day. They should draw on their own personal experiences with typical application defects to try and break functionality. They should also consider the habits and behaviors of the members of the software development team.

As developers and product managers work more with an application over time, they start to develop habits that may influence how they interact with the software. For example, some developers may only develop code on a local machine while others may only do code reviews instead of pre-testing in a test environment. These are work habits that can lead to defects. On the product side, many product managers habitually create user stories and requirements in the same way, unintentionally leaving out a relevant workflow or configuration setting.

Finding hidden defects requires testing against the grain rather than verifying a function performs as expected. It also requires testing all possible paths that customers might take. Crowdtesting can supplement existing techniques by using real people to serve as proxies for customers. They can test for quality, user-experience and functionality outside the lab, and provide instant, useful feedback.

Testing repeatedly only to have bugs unearthed later by customers is a frustrating and potentially costly endeavor. When testers mix existing techniques with creativity and an understanding of human behavior, they will be able to dig deeper to find bugs and friction points that ultimately improve quality and customer experience before release.

Rob Mason is CTO of Applause

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...