Skip to main content

The 2018 State of Testing Survey Says ...

Bria Grangard

The results from the latest State of Testing Survey by SmartBear were recently released. Designed to establish benchmarks for the software testing industry regarding the methodologies, practices, and tools used by QA professionals, this second-annual, 45-question survey grabbed more than 2,600 responses from various job roles across 16 major industries globally.

Software over the last two decades has revolutionized the way we live. From flip phones and floppy disks, to 3D-video games, pacemakers, and the birth of artificial intelligence-based devices like Alexa, technology has advanced at an unimaginable rate. The rise in what software can accomplish has correlated with a boom in technological dependency. With software having seeped into almost every aspect of day-to-day life, the value and importance of software development and testing has skyrocketed.

The spectrum of practices and tools available to QA professionals is just as broad as the products and tools available in the market — and the goal of this survey was to better understand what is going on in the software testing world today. Here are some of the highlights:

The industry reliance on API testing is growing

This year, 80 percent of respondents reported testing some kind of API or web service, up from 71 percent in 2017. API consumption has skyrocketed over the past few years, a growth that is not surprising and will continue. As the connective-tissue of application development, APIs are the key to connecting the vast network of systems, platforms, and apps that make up the Internet of Things (IoT). To ensure a flawless user experience, it is crucial to test APIs within the context of their consumption across multiple browsers, devices, and resolutions.

The role of testing continues to expand beyond the traditional tester

With the importance of testing having grown exponentially in recent years, the survey audience spends more than 40 percent of their week testing with QA engineers spending 75 percent of their time at it. Developers reported spending 40 percent of their week testing, proving that teams are indeed shifting left.

Automation is not growing as fast as we might expect

It's been widely acknowledged across the industry that automated testing is essential for teams looking to adopt more iterative development cycles and to release faster. But while it feels that automation is picking up steam, it's not growing as quickly as the industry might expect. The use of automated API testing grew from 49 percent in 2017 to 54 percent in 2018.

API testing is increasing

API testing is increasing with third party API usage having grown by more than double in the last year. The breakdown of types of APIs tested has drastically shifted year-over year. More developers and testers are testing third party and external facing APIs, rather than just the internal APIs that make their application functional in a SOA or Microservice Architecture. Using third party APIs for testing can be notoriously expensive and difficult, as API providers can charge teams each time they use the API.

One quarter of teams are releasing software at least once per day or multiple times per day

Fifteen percent of respondents reported releasing multiple times a day, as compared to 6 percent in 2017. A higher percentage of individuals also reported releasing on a daily and weekly basis in 2018 than in 2017.

The survey is filled with many key insights including emerging trends and year-over-year analysis of what teams are doing, and what teams globally are thinking the future of testing will hold. Discover more of what your peers are doing today in testing.

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 ...

The 2018 State of Testing Survey Says ...

Bria Grangard

The results from the latest State of Testing Survey by SmartBear were recently released. Designed to establish benchmarks for the software testing industry regarding the methodologies, practices, and tools used by QA professionals, this second-annual, 45-question survey grabbed more than 2,600 responses from various job roles across 16 major industries globally.

Software over the last two decades has revolutionized the way we live. From flip phones and floppy disks, to 3D-video games, pacemakers, and the birth of artificial intelligence-based devices like Alexa, technology has advanced at an unimaginable rate. The rise in what software can accomplish has correlated with a boom in technological dependency. With software having seeped into almost every aspect of day-to-day life, the value and importance of software development and testing has skyrocketed.

The spectrum of practices and tools available to QA professionals is just as broad as the products and tools available in the market — and the goal of this survey was to better understand what is going on in the software testing world today. Here are some of the highlights:

The industry reliance on API testing is growing

This year, 80 percent of respondents reported testing some kind of API or web service, up from 71 percent in 2017. API consumption has skyrocketed over the past few years, a growth that is not surprising and will continue. As the connective-tissue of application development, APIs are the key to connecting the vast network of systems, platforms, and apps that make up the Internet of Things (IoT). To ensure a flawless user experience, it is crucial to test APIs within the context of their consumption across multiple browsers, devices, and resolutions.

The role of testing continues to expand beyond the traditional tester

With the importance of testing having grown exponentially in recent years, the survey audience spends more than 40 percent of their week testing with QA engineers spending 75 percent of their time at it. Developers reported spending 40 percent of their week testing, proving that teams are indeed shifting left.

Automation is not growing as fast as we might expect

It's been widely acknowledged across the industry that automated testing is essential for teams looking to adopt more iterative development cycles and to release faster. But while it feels that automation is picking up steam, it's not growing as quickly as the industry might expect. The use of automated API testing grew from 49 percent in 2017 to 54 percent in 2018.

API testing is increasing

API testing is increasing with third party API usage having grown by more than double in the last year. The breakdown of types of APIs tested has drastically shifted year-over year. More developers and testers are testing third party and external facing APIs, rather than just the internal APIs that make their application functional in a SOA or Microservice Architecture. Using third party APIs for testing can be notoriously expensive and difficult, as API providers can charge teams each time they use the API.

One quarter of teams are releasing software at least once per day or multiple times per day

Fifteen percent of respondents reported releasing multiple times a day, as compared to 6 percent in 2017. A higher percentage of individuals also reported releasing on a daily and weekly basis in 2018 than in 2017.

The survey is filled with many key insights including emerging trends and year-over-year analysis of what teams are doing, and what teams globally are thinking the future of testing will hold. Discover more of what your peers are doing today in testing.

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 ...