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Envisioning Testing as a Three-Course Meal

Marcus Merrell
Sauce Labs

A Metaphorical Appetizer

Might we recommend a light and crisp prosecco?

In Italy, it is customary to treat wine as part of the meal. Wine isn't a beverage: wine is as integral to the meal as the food itself. The wine is the food. The food is the wine. They are inseparable, complementary.

Too often, testing is treated with the same reverence as the post-meal task of loading the dishwasher, when it should be treated like an elegant wine pairing: a test suite, properly written, gives you objective proof that your code is functioning properly, from the individual units to the full application. A passing suite of unit tests, executed after even a small change to someone else's code, gives you a dopamine hit almost as powerful as a sip of wine. If you treat testing like loading the dishwasher, you're probably overlooking details and missing steps. It's hard to care about details of any task you hate.

Like wine, tests can give you both pleasure and (given enough quantity) confidence.

To further stretch the metaphor: Testing can be off-loaded to a separate person or team — forgotten — just like loading the dishwasher. But everyone needs to eat, and everyone deserves the pleasure of a perfect pairing of wine with their dinner.


Photo from Mangia Michelle

The Main Course

Paired with a delightful Cabernet Sauvignon, or perhaps JUnit?

Unit Tests
Unit tests exist to ensure that a team's code works as correctly as it can. Few things are faster to execute than unit tests — we're talking about nanoseconds. You should have many of them, liberally sprinkled throughout the codebase. The bigger your team, the more you will thank yourself for requiring them. Unit tests apply equally to any kind of software project, including mobile apps.

Integration Tests
Integration tests ensure that one team's code interacts with other codebases as expected, and as it evolves and changes. They often talk to a microservice developed by another team. They don't execute as quickly as unit tests, but they are still blazingly fast — fractions of a second. Integration tests are critical to mobile testing in particular, because apps live and die by the function and efficiency of the APIs they use, and because so much of the mobile ecosystem beyond the API involves variables your team can't control.

Functional UI Tests
Functional UI tests are meant to string disparate parts of the system together, to ensure it works as a whole. These tests also tend to incorporate other kinds of software over which your team has zero control: web browsers, mobile devices, mobile operating systems, and background processes. As a result, these tests are exponentially slower than unit and integration tests.

As difficult as they are to develop and maintain, they are absolutely critical to the success of your app. Due to the complications of constant changes — to operating systems, libraries, and even mobile device hardware, the number of ways your code might be executed increases exponentially, and only testing can guarantee success.

Dessert

A Tawny Port will do the trick. Or some Selenium.

And thus we leave our culinary metaphor behind. While I expect to receive immediate gratification from my meal at a four star restaurant, succession planning requires a different motivation.

Automated testing is a form of succession planning. Unit, integration, Functional tests — these are created by people who understand the requirements that were used to create the software, and they evolve to changes made, staying relevant or being excised as requirements are altered.

Thus this is where our testing meal metaphor ends. While I expect to receive immediate gratification from my meal at a four star restaurant, succession planning requires a different motivation.

I don't write tests for myself, for today.

I write tests for myself, for a year from now, when I have to change my code and I can't remember what I was thinking.

I write tests for a new developer who inherits the code base later, after I'm gone.

My tests set my successors up for success. The seeds of good testing are planted today, to create the beautiful vintage, your team will savor for years.

Marcus Merrell is VP of Technology Strategy at Sauce Labs

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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 ...

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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 ...

Envisioning Testing as a Three-Course Meal

Marcus Merrell
Sauce Labs

A Metaphorical Appetizer

Might we recommend a light and crisp prosecco?

In Italy, it is customary to treat wine as part of the meal. Wine isn't a beverage: wine is as integral to the meal as the food itself. The wine is the food. The food is the wine. They are inseparable, complementary.

Too often, testing is treated with the same reverence as the post-meal task of loading the dishwasher, when it should be treated like an elegant wine pairing: a test suite, properly written, gives you objective proof that your code is functioning properly, from the individual units to the full application. A passing suite of unit tests, executed after even a small change to someone else's code, gives you a dopamine hit almost as powerful as a sip of wine. If you treat testing like loading the dishwasher, you're probably overlooking details and missing steps. It's hard to care about details of any task you hate.

Like wine, tests can give you both pleasure and (given enough quantity) confidence.

To further stretch the metaphor: Testing can be off-loaded to a separate person or team — forgotten — just like loading the dishwasher. But everyone needs to eat, and everyone deserves the pleasure of a perfect pairing of wine with their dinner.


Photo from Mangia Michelle

The Main Course

Paired with a delightful Cabernet Sauvignon, or perhaps JUnit?

Unit Tests
Unit tests exist to ensure that a team's code works as correctly as it can. Few things are faster to execute than unit tests — we're talking about nanoseconds. You should have many of them, liberally sprinkled throughout the codebase. The bigger your team, the more you will thank yourself for requiring them. Unit tests apply equally to any kind of software project, including mobile apps.

Integration Tests
Integration tests ensure that one team's code interacts with other codebases as expected, and as it evolves and changes. They often talk to a microservice developed by another team. They don't execute as quickly as unit tests, but they are still blazingly fast — fractions of a second. Integration tests are critical to mobile testing in particular, because apps live and die by the function and efficiency of the APIs they use, and because so much of the mobile ecosystem beyond the API involves variables your team can't control.

Functional UI Tests
Functional UI tests are meant to string disparate parts of the system together, to ensure it works as a whole. These tests also tend to incorporate other kinds of software over which your team has zero control: web browsers, mobile devices, mobile operating systems, and background processes. As a result, these tests are exponentially slower than unit and integration tests.

As difficult as they are to develop and maintain, they are absolutely critical to the success of your app. Due to the complications of constant changes — to operating systems, libraries, and even mobile device hardware, the number of ways your code might be executed increases exponentially, and only testing can guarantee success.

Dessert

A Tawny Port will do the trick. Or some Selenium.

And thus we leave our culinary metaphor behind. While I expect to receive immediate gratification from my meal at a four star restaurant, succession planning requires a different motivation.

Automated testing is a form of succession planning. Unit, integration, Functional tests — these are created by people who understand the requirements that were used to create the software, and they evolve to changes made, staying relevant or being excised as requirements are altered.

Thus this is where our testing meal metaphor ends. While I expect to receive immediate gratification from my meal at a four star restaurant, succession planning requires a different motivation.

I don't write tests for myself, for today.

I write tests for myself, for a year from now, when I have to change my code and I can't remember what I was thinking.

I write tests for a new developer who inherits the code base later, after I'm gone.

My tests set my successors up for success. The seeds of good testing are planted today, to create the beautiful vintage, your team will savor for years.

Marcus Merrell is VP of Technology Strategy at Sauce Labs

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 ...