No matter how skilled the DevOps team is, there are almost always conflicts and errors that occur in software development. Features, functionality, and integration can solve one problem while creating another. Even the most skilled teams often fall victim to software testing problems. These 10 most common software testing pitfalls generally occur in three distinct areas, revolving around the clarity of scope and objectives, test coverage and environments, and gaps in the testing process. Let’s take a look.
Problem Area: Clarity of Scope and Objectives
Before a DevOps team performs a single test, it’s essential that they have a clear understanding of their scope and objectives to avoid these mistakes:
1. Not Having Clear Pass/Fail Criteria
Many test teams start their work without formalized expectations around what constitutes a “passed” or “failed” test. This leads to inconsistencies and inaccuracies in reporting.
Example: A bug that has a minor impact on the user may be logged the same as a critical crash, making it difficult to discern signal from noise. Teams end up wasting cycles debating severity rather than solving issues.
Organizations should invest upfront in defining detailed pass/fail guidelines aligned to user impact. Granular grading rubrics and severity classifications allow testers to log issues accurately and consistently, facilitating better decision-making.
2. The Team Isn’t Sure Why It’s Testing
Surprisingly, many test initiatives kick off without first establishing testing objectives. With no guiding purpose, test execution ends up arbitrary rather than focused where it matters most. This can quickly lead to delays, cost overruns, and major defects slipping through.
Example: Leadership kicks off performance testing to handle the expected holiday load but doesn’t clarify the specific metrics that indicate success, leading testers to make arbitrary judgments.
Product and engineering leaders must align on core goals for each testing milestone, whether they are related to usability, security, platform coverage, or other outcomes. These goals can dictate the proportional investment of both time and resources.
3. No Agreements on Types of Problems
Closely related to the above issue, teams often lack consensus around what categories of problems they are testing for. With today’s complex tech stacks and dependence on third parties, there are many layers where software can fail, each of which demands specialized testing approaches.
Example: The team spends long hours security testing without guidance on what attack vectors are in scope, allowing SQL injection flaws to slip through.
To maximize risk coverage, organizations must identify priority problem domains for the initiative. Aligning everyone on “how” systems can break avoids blind spots. This enables more impactful discovery and remediation.
Problem Area: Test Coverage and Environments
Most DevOps teams can agree that there’s never enough time for testing. As a result, it’s imperative that teams are strategic in their test coverage to avoid critical errors.
4. Testing Too Late In The Development Cycle
Due to compressed delivery timelines, testing is often the last activity before release. However, issues found late in the production lifecycle are generally much more expensive and time-consuming to fix. What takes an hour during initial coding may consume weeks right before launch due to rework across layers of functionality and content built on top.
Example: Major architectural changes are made right before launch, not allowing enough time to sufficiently test and validate the impacts across subsystems.
Test strategy should focus first on upstream components, interfaces, and foundational services. By front-loading, defects surface at the source before propagating downstream. This preventive approach reduces the multiplier effects of late-stage bugs.
5. Testing The Wrong Things
With hundreds of features and endless test cases, prioritization is key. However, without guidance, testers default to covering superficial functions that may be inconsequential to users. This expends effort better spent confirming that high-risk areas actually work as expected under load.
Example: The team focuses extensively on filters, which are used occasionally, while minimal testing is done on the high-volume checkout payment process.
Product analytics and customer feedback should drive priority areas for testing. Exposing systems to real-world usage patterns is optimal.
6. Lack Of Test Environment Parity
Lab environments that differ too much from production can produce major gaps in coverage. Behaviors in a scaled-down or synthetic test environment often don’t reflect real-world usage. Differences in configurations, software versions, data, and dependencies mask defects that won’t manifest until an application is live.
Example: A major bug is missed in staging due to that environment’s synthetic user data versus the production data model, causing corruption.
The fix lies in test environment parity. All aspects of pre-production environments should mirror production to the greatest extent possible. While perfect parity isn’t possible, minimizing differences isolates bugs.
7. Not Testing Edge/Corner Cases
Too often, testing revolves around “happy path” scenarios only. However, the tricky bugs often hide in unexpected places like overflow conditions, race conditions between asynchronous operations, network dropouts, or unparsable inputs. Limiting test cases to positive paths leaves these dark corners unexplored.
Example: Invalid date formats entered in search crash the application, an edge case that was missed.
Equivalence class partitioning, boundary analyses, and fuzzy testing expose flaws missed in mainstream use. While this defect testing seems speculative, its value lies in building resilience against unpredictable factors that disable systems post-launch.
8. Testing Frontend Only, Not Backend
In today’s distributed application architectures, frontends are just one piece of the puzzle. Often capped by APIs, critical logic lives in backend services, databases, message queues, and workflows. Testing only visible UI renders an incomplete picture.
Example: The UI flow-focused mobile app testing team finds that transactions fail to complete reliably due to backend race conditions not caught earlier.
The full user journey traverses a complex system of interconnected components that must be validated holistically.
9. Not Retesting Fixes
DevOps teams want to move corrected code into production as quickly as possible. However, making fixes in one area can introduce risk into other areas of code.
Example: A patch to address slow response times ends up degrading search accuracy.
Disciplined retesting ensures updates don’t trade one problem for another.
10. Lack Of Test Automation
Manual testing cannot scale to the breadth of test cases required for modern software assurance. The complex and interconnection configuration and input combinations simply exceed human capacity, especially when testing at scale. Manual testing is also slower and inconsistent run to run.
Example: Engineers spend more time running repetitive tests than analyzing meaningful results and raising coverage.
The best strategy utilizes both test automation and human testers, leveraging tools for scale while manually validating complex use cases.
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