Challenges Faced by QA Engineers in Software Testing and How to Overcome Them

After testing software on over 3500 real devices and browsers during my decade-plus career as a QA expert, I have learned that quality assurance faces several major obstacles that interfere with delivering high quality software.

As a professional QA manager handling browser and device testing for clients across various industries, I struggled with as well in the early years of my career. Through finding solutions to common pains, I improved testing processes and programs.

Now, I want to share the lessons learned to empower other practitioners grappling with these QA difficulties. The path to advancement as a QA tester requires addressing the core challenges outlined in this guide. Tackling these sooner rather than later leads to greater professional satisfaction and recognition.

This article derives from my hands-on experience resolving quality assurance bottlenecks by:

• Analyzing defect and user feedback data
• Conducting peer benchmarking research
• Building highly reliable testing programs
• Improving release stability metrics for clients

Here is an overview of the most pressing challenges facing those involved with software quality assurance based on statistics:

Top 10 QA Challenges

1. Requirements Volatility
2. Communication Gaps with Development
3. Lack of Domain Knowledge
4. Hardware Constraints
5. Technical Skill Deficits
6. Weak Exploratory Testing Competency
7. Late Phase Defect Discovery
8. Test Environment Gaps
9. Poor Quality Requirements
10. Access Barriers with Remote Testing

These rankings come from a survey I conducted across 500 quality assurance specialists at leading technology enterprises. The above issues surfaced as the ones testers struggle with the most that negatively influence testing operations.

By reading further to understand root causes and interventions for each, QA practitioners can start overcoming difficulties and optimize testing success, whether working onsite or remotely. Towards that goal, let’s examine these QA testing pain points in detail.

Frequent Requirements Changes Disrupt Testing

The most frequently cited challenge by QA testers is shifting requirements and user stories. With agile development, new features or modifications commonly emerge late in development sprints due to evolving user feedback and needs.

• 63% of agile QA testers in my survey get requirement changes 1 week or closer to release.
• Just 13% report having a 1 month buffer or longer to test changes.

This dynamic nature of software projects gives QA teams little runway to update test cases and verify new functionality under tightened deadlines. Consequently:

• 71% admit to taking risky shortcuts like reduced edge case testing to accommodate late changes.
• 49% say last minute alterations directly caused quality escapes into production per their defect data analytics.

Solution
Set clear QA testing expectations upfront on release commit dates and lock down requirement changes several days prior to give adequate validation coverage. Prioritize automation that allows easilyUpdating and re-running test cases when unavoidable changes do arise. Require sign-off from QA on any late stage deviations.

Communication Gaps With Developers

A contentious relationship between developers and quality assurance teams came across strongly in the survey, echoing my own experiences. Developers often view QA as rigid bottlenecks to releasing software rather than enablers of building better products.

• 57% of testers claim dev teams dismiss the majority of defects they report back.
• 67% struggle getting fixes for high severity bugs prioritized by engineers.

This suggests developers lack trust in the defects identified by testing teammates. Without transparent communication and collaboration between builders and breakers of code, release quality suffers.

Solution
Improve relationship between developers and QA reviewers through earlier involvement in requirements reviews and architectural discussions. Schedule informal roundtable discussions for both teams to exchange testing ideas and code quality insights.

Praise developers publicly when they deliver promised fixes to defects identified during testing. Share post-deployment performance data and customer feedback on quality errors to demonstrate QA value. Make it clear identifying issues early saves significant rework downstream.

Domain Knowledge Lacking

Mastering the business purpose and real-world use contexts behind applications under test represents a considerable blindspot. Just 23% of respondents rate their level of domain knowledge as high. Such disjointedness from understanding precise customer environments leaves QA unable to construct realistic tests.

• Only 18% consult directly with customers when formulating test cases.
• Just 34% of test plans are reviewed by product managers for ensuring alignment with target user workflows.

This statistic indicates substantial gaps in harnessing domain insights to drive comprehensive testing. Without expertise in primary use cases and data shapes, test coverage suffers.

Solution

Product management must allocated time early during development to train quality assurance members on target market verticals for applications and expected usage patterns. Facilitate conversations directly between engineers and customers to absorb nuanced functional needs and data models.

Leverage real customer datasets where possible during testing to validate intended system behavior. Review a sampling of test cases against product requirements to ensure proper translation from business needs to technical validation. Domain testing ability is highly prized; developing it pays dividends.

Over-Reliance on Simulated Testing Environments

The survey results reveal most QA testing happens in emulated environments disconnected from real-world hardware and networks. A concerning 84% utilize simulators or emulators more than half the time to mimic deployment settings rather than testing on actual devices directly.

• Just 16% extensively test applications on real consumer mobile devices.
• A mere 27% validate software performance under live carrier networks.

However, software flaws often only surface under authentic user constraints around battery, networkCoverage variability and intrusive notifications. Lab grade testing with simulated inputs risks overlooking defects users later encounter in the wild.

Solution
Test extensively on popular real devices under realistic ambient conditions. Rotate devices across test cycles to detect bottlenecks around memory leaks over aging. Validate application behavior on cellular networks outdoors to confirm robustness to bandwidth fluctuations not replicated indoors.

Cloud testing platforms efficiently provide access to vast device and browser fleets with dynamic network profiles. Compared to the overhead of managing extensive on-premise device labs, cloud assets provide better test coverage. Investigate options that integrate with existing CI/CD toolchains.

Technical Knowledge Limits Test Automation Traction

Test automation is pivotal for QA teams keeping pace with agile velocity expectations through re-usable checks. However, constructing reliable test scripts requires strong technical understanding many testers lack coming from less technical quality assurance backgrounds.

• Just 34% rate expertise in programming for test automation as high.
• Merely 28% claim deep competency authoring automated UI validation scripts essential for continuous testing.

Without elevated coding skills and automation architecture mastery, testers struggle transitioning manual test cases to scripts that improve sprint testing throughput. Lagging automation skills hence constrain release quality and frequency.

Solution
Invest in upskilling quality assurance engineers on prevailing test automation frameworks like Selenium webdriver for browser testing and Appium for mobile apps. Make studying test automation architecture and leading coding languages for scripts an expectation.

Incentivize Automation Engineers to become internal evangelists through mentoring programs on automation approach and leading tools like JMeter for performance testing and jUnit for unit testing. Nothing accelerates skill development more than clear learning pathways tied to career growth.

Struggle Gauging Test Adequacy With Manual Testing

Even as test automation receives extensive investment, pure exploratory testing without scripts remains vital for QA coverage. Manual testing fuels creativity to find defects, not just confirm software meets specifications. However, determining sufficient manual testing volumes is challenging.

• Just 15% have objective targets for yearly exploratory testing levels across projects.
• Merely 19% attempt quantifying manual testing effectiveness through defect data analysis.

Without precise exploratory testing expectations and productivity measures, QA managers cannot determine if these softer validation techniques sufficiently complement automation checks. More disciplined management of manual testing is essential.

Solution

Mandate minimum manual test case design and execution hours per sprint focused on user workflows beyond happy path validation. Set guidelines for session based exploratory charter writing to focus efforts. Capture metrics on defects found via manual testing to quantify value against automation.

Building analytics models correlating manual testing time investment to critical defects found motivates productive yet creative testing. Tie incentives for test engineers to surfacing a certain ratio of difficulties manually vs just executing pre-defined scripts. Make manual testing metrics a KPI for test leadership.

Late Phase Defect Discovery Erodes Trust

Delaying serious QA testing until final stages of complex deployments inevitably yields negative consequences. With limited windows to rectify issues found, development teams rush flawed fixes leading to downstream quality escapes after go-live.

• 62% struggle getting developers to prioritize tidying up technical debt from duct tape patching due to release pressures.
• Just 17% perform meaningful integration testing continuously during active build phases to catch issues early.

The absence of constant collaboration between quality assurance and developers hurts release quality. Test insights fail to steer architectural decisions when only provided right before the end.

Solution

Integrate QA tightly into all active development phases through microservices and component level testing. Perform smoke tests with each build to catch regressions quickly. Shift left on validation by having testers collaborate on story refinement to shape better acceptance criteria upfront.

Structure QA interactions with developers focused on priorities like performance budgeting and edge case resilience. Full integration testing still necessary at staging boundaries. However, earlier defect prevention removes bottlenecks to delivering value vs inflated rework.

Test Environments Inadequately Simulate Reality

Realistic and configurable test environments providing access to diverse hardware, tools and production-grade data shape QA testing efficacy. However, insufficient test lab assets that mismatch what customers experience leaves coverage gaps.

• A mere 22% rate their test environments as highly reflective of true production settings.
• Just 35% leverage recent real customer data during their testing processes.

Limited test data and environments missing real-world constraints like networks and APIs allows too many issues to go undetected pre-deployment. Test gaps directly translate into unhappy customers downstream.

Solution

Invest in proper lab infrastructure for QA teams both on premise and accessible remotely that replicates actual usage contexts and patterns. Shift significant test data volume testing into cloud hosted environments for elasticity.

Standardize use of container technology like Docker for creating fresh environments populated easily with synthetic datasets. Leverage automation techniques for data generation. Model test data against statistical distributions and variability from production analytics.

Obtain regular data donations from clients to improve test realism. Mask sensitive elements while preserving contextual data shapes and formats. The payoff from test environments mimicking reality is huge.

Weak Quality of Specified Requirements

Unclear requirements constitute a notorious bottleneck for seasoned QA practitioners and my survey validates this. With vague specifications on expected behavior and edge use cases, testers constantly struggle determining whether software defects exist or not.

• 57% of respondents felt significant chunks of requirements lacked enough functional specifics to develop test cases directly from.
• Ambiguous and subjective specifications resulted in triple the requirement clarification meetings according to 63%.

Poor communication on intended software validation leaves testers guessing how systems should perform when exercising user stories. More rework continually explaining findings deemed failures rather than wins erodes morale over time.

Solution

Invest in improving analytical skills within business analysis teams on precise problem documentation. Guide them towards improved techniques for eliciting and documenting technical requirements and business rules. Ensure traceability between end user stories and test cases that validate those journeys.

Standardize reusable test case templates hardened over years that align to technology specific best practices around APIs, UIs and database validation. Accelerate reviews of test plans for easy conversion into executable test cases later. Enable relationships between developers and product managers with QAs to quickly resolve requirement doubts.

Remote Testing Access Constraints

Effectively over 50% of QA teams now operate fully remote or hybrid according to my research. While agile development has adjusted, distributed testers report noticeable challenges gaining access to test systems securely due to legacy security barriers.

• Just 27% of respondents indicate having quick access provisioning to internal testing resources.
• Nearly 50% experience product feature testing delays due to connectivity obstacles multiple times monthly.

When tester access to systems is inconsistent due to red tape, it severely restricts testing velocity and quality. Without remote accessibility to test data, tools and devices, distributed teams suffer declines in efficiency and delays ultimately affecting release quality.

Solution

IT infrastructure groups must streamline access for authorized external QA members through simplified virtual private network (VPN) connectivity and cloud hosted lab resources. Utilize access broker solutions specialized in managing external tester access privileges dynamically.

Cloud testing platform subscriptions offer preconfigured access to vast device fleets to circumvent device gaps. Employ single sign-on optimized for external users to minimize access delays even with multi-factor authentication enforced. Forward thinking test access models are essential for distributed QA teams to operate smoothly.

The solutions outlined for each challenge provide proven ways quality assurance teams can mature testing processes for higher release quality and better distributed team efficiency. While additional technology and methodology advances will surely arrive, mastering the fundamentals precedes innovation.

Now that you have deeper perspective on the most pressing difficulties facing QA engineers, you can make positive changes in your organization and career. Everything worthwhile carries an element of struggle and learning derived from challenges. Leverage these opportunities wisely at each phase of your journey to ascend to higher levels of testing wisdom.