Software QA for SaaS Companies – The Main Challenges


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The SaaS model is now an industry mainstay. It has shifted the way users perceive digital products, from discrete software version releases to a living product that is always available and continually evolving. SaaS product development must successfully scale to meet performance and demand against release cycles that no longer support lengthy testing processes. We asked co-founder and Chief Operating Officer at ProdPerfect, Erik Fogg, to take us through the numerous challenges companies face when adopting traditional testing procedures to SaaS applications.

The Rise of SaaS 

The rise of SaaS brings some notable differences to how software is developed, distributed, and tested compared to the on-premise software installations of the past. Notably, the SaaS distribution model makes it far easier to get your product into the hands of your customers and on their devices. The cloud-based nature of SaaS products brings benefits, like maintaining version parity for all customers, but it also presents its own challenges. When releases are no longer every quarter but every week or even every day, how do you keep up ever tighter release cycles without sacrificing QA?  

The Evolving Need for Quality SaaS Software QA 

Having complete control over the distribution of your SaaS product and having the latest version available at all times is great from a technical and go-to-market perspective but it also presents its own challenges. For one, the low barrier to entry has created enormous pressure on SaaS companies in the form of plentiful competition and high consumer expectations. Users have a low tolerance for failure and will expect defects to be caught and fixed in a matter of hours, not days or weeks.  

SaaS development methodologies have disrupted the traditional software development life cycle (SDLC). Instead of devoting discrete chunks of time to development and testing processes, SaaS companies are now moving to a continuous deployment model, which also necessitates the need for continuous testing. Automated testing is no longer an advantage, it is a necessity. Teams must look to streamline and automate the QA process wherever possible without sacrificing security.  

Testing and QA for SaaS products presents some unique challenges. Certain things don’t have to be tested – there is no installation process, you don’t need to support multiple product versions, and you don’t have to support different platforms or backends. However, you do have much more complex testing requirements. Security must be rock-solid – handling private customer data and knowing that each customer will have multiple agents, each with their own privilege levels and privacy requirements complicating testing. A failure in securing customer data can destroy a company overnight.  

How Software QA for SaaS is Different 

Workflows tend to be much more complex in SaaS applications as compared to e-commerce applications. SaaS users have multiple agents who each have their own access levels and user workflows, all of which must be tested. However, knowing what those workflows are in order to develop tests for them is difficult. Understanding user behavior is the key to developing the right tests. However, you also have to be quick. Testing is part of the continuous deployment process, so tests cannot take days to run.  

Test cycles need to be short and fast. Most of these testing processes need to be automated. Testing needs to become a part of the SDLC as early on in the iteration process as possible, not at the end of a feature development cycle. These tests need to be broken down into chunks that can be run on different cycles, depending on need and time restraints. 

QA engineers are constantly required to adapt to these challenges and innovate. Testing and release cycles are both becoming increasingly automated. Automating regression testing, simulating incomplete components with service visualization, and unit testing enables teams to continuously test a product throughout development, rather than waiting until the end of a cycle, which can be a killer for SaaS applications on extremely short SDLCs. 

Overcoming These Challenges 

User behavior data and product analytics provide the tools required to generate meaningful insights into your user base and assess the performance of your SaaS application. Your tests and product scalability must be powered by actual user performance and usage patterns, not theoretical figures and assumptions. 

Testers should use customer analytics to understand their users and different audiences, and the end-to-end testing process must be well-mapped to the actual user experience. Know which browsers and platforms your customers are using to know what to test. Develop tests around actual user workflows and track key metrics like performance and error rate to prioritize the testing of problem areas. 

Using this data to prioritize is the key to achieving high levels of quality assurance with short testing cycles. SaaS companies simply can’t afford to deploy sprawling regression test suites in the hopes that they have deployed enough tests to catch critical bugs. Your customers are telling you how they’re using the application: rely on that understanding to fiercely decide what belongs in the test suite and what doesn’t. 

A short SDLC presents a number of challenges to any SaaS application, but the benefits of a successful QA process enable companies to grow and adapt to market trends at a quick pace, something that is key to survival in a crowded marketplace. 

The Future of SaaS and SQA Testing 

QA engineers are continuously looking for ways to adapt to meet user demands and deliver rapidly in ever shorter release cycles. Testers must become customer experts – they must understand their audience thoroughly, using data rather than intuition. This will involve developing models based on user behavior in order to better understand customer desires and predict future trends. 

SaaS applications are becoming increasingly complicated, with some tools now integrating artificial intelligence and machine learning into the software development and testing process. Understanding all theoretical pathways through an application, tracking user behavior patterns, and developing models of customers based on that data enables the creation of meaningful tests in very short SDLCs. QA engineers can use models to predict future trends and stay on top of a constantly evolving product. The ability to not only meet current user demand but to have data-driven insights into possible future trends enables SaaS applications to stay ahead in a crowded market. 

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