Friday afternoon arrives, development is finished, and the release is still waiting. Regression needs another run. Two automated tests failed for reasons nobody can reproduce. Staging contains the wrong data for one scenario. A tester found a problem in a workflow completed several days earlier, so the developer has already moved to another task.
Calling this a testing problem misses part of what is happening. Slow QA often begins somewhere earlier. Feedback arrives too late. Automation spends time on low-value checks. Test environments behave unpredictably. Manual regression grows with every feature. Requirements reach testers after important decisions have already been made.
Adding testers can increase capacity. Fixing delivery speed usually requires finding where the waiting enters the process.
Nine QA partners approach that work from different engineering backgrounds.
1. Avenga: when important questions reach QA too late
Finding a defect after development costs time. Finding an unanswered product question after development can cost even more.
Imagine a team building a payment cancellation flow. Developers finish the feature, and QA starts testing. One scenario raises a simple question: what should happen when cancellation is requested after the payment provider has accepted the transaction but before confirmation returns?
Nobody defined the expected behavior. Testing stops while product, engineering, and business specialists work it out. Code may need changes afterwards. Regression starts again. Earlier QA involvement can expose questions like this while implementation is still underway.
Avenga follows a shift-left approach and provides functional and nonfunctional testing, automation, performance, security, compliance, usability, data quality, mobile, and embedded testing.
Early QA work can include:
- Reviewing acceptance criteria for missing states and exceptions;
- Identifying dependencies before implementation reaches them;
- Preparing test data while development continues;
- Deciding which scenarios need automation;
- Discussing security, performance, or compliance concerns before release testing begins.
Feedback then arrives closer to the work that created it. Avenga also connects automated testing with CI/CD and applies AI-supported methods in areas including test coverage and defect prediction. Finance, healthcare, telecom, e-commerce, and other industry experience can help when delays come from business rules rather than purely technical defects.
Continuous visibility into delivery performance and one accountable delivery team support the same working model. Avenga states that its approach can support software delivery up to 50% faster.
Avenga deserves consideration when QA repeatedly discovers important issues near the end of a delivery cycle, and earlier testing involvement could move those conversations forward.
2. Codica: when regression keeps growing with the product
Successful products accumulate things nobody wants to break. Another payment method appears. Subscription rules expand. Administrators receive new controls. Marketplace sellers get additional order states. Customers keep using older plans while new plans arrive.
Regression grows quietly around all of it. Running everything after every meaningful change eventually becomes difficult. Removing tests without understanding the product can be equally risky.
Codica works with SaaS products, marketplaces, web and mobile applications, DevOps, UX/UI, and QA. Product engineering experience gives testing decisions useful commercial and technical context.
Regression can be divided by purpose rather than treated as one permanent block:
- Release critical coverage protects transactions, access, payments, and other high-impact behavior.
- Change-related coverage follows dependencies around the functionality being modified.
- Historical coverage protects against important defects previously seen in production.
- Specialized coverage handles devices, browsers, permissions, data states, or customer groups relevant to particular releases.
- Scheduled wider coverage can run without holding every smaller change in the delivery pipeline.
Such decisions require familiarity with the product. A test for an old subscription plan may look obsolete until active customer data shows that thousands of accounts still depend on it. Another group of tests can survive long after the feature they protected has disappeared.
Codica’s QA work fits ongoing product development because testers can connect coverage with how SaaS platforms and marketplaces actually change over time.
Delivery becomes easier when regression grows selectively instead of expanding automatically after every release.
3. N-iX: when automated tests are producing too much waiting
Automation was supposed to shorten feedback. Then the suite started taking two hours.
Several tests fail intermittently. Developers rerun them before opening a defect. Parallel jobs compete for shared data. UI checks dominate execution time. One failed environment can invalidate an otherwise useful run.
At sufficient scale, automation needs engineering attention of its own. N-iX provides test automation, functional and nonfunctional testing, continuous testing, test environment work, defect management, QA process development, and CI/CD support. AI-related capabilities include test generation, prioritization, anomaly detection, defect analysis, UI discrepancy detection, and performance analysis.
A slow suite can be examined by where its minutes actually go:
| Source of delay | Question worth asking |
| Test execution | Which checks need to run after every change? |
| Data preparation | Can required states be created faster and independently? |
| Flaky failures | Which tests regularly consume investigation time without finding application defects? |
| Environment setup | How much of the pipeline waits for infrastructure? |
| UI coverage | Could lower-level tests provide the same feedback earlier? |
Reducing execution time alone does not solve much if developers still distrust the results.
Reliable feedback matters more. A 20-minute run full of ambiguous failures can interrupt development more than a 30-minute run whose failures consistently mean something.
N-iX becomes relevant when an established automation estate needs better execution strategy, environments, data management, prioritization, and maintenance.
4. ELEKS: when the same areas keep returning to regression
Some parts of a product seem to collect testing effort. A small change reaches one component, and suddenly a broad regression run becomes necessary. Another release touches the same area, and several old defects need checking again. Months later, another production incident appears nearby.
Extra testing protects the next release. Repeated patterns deserve another kind of attention. ELEKS provides functional, automated, performance, security, and accessibility testing and also works across software architecture, code, and connected systems.
Defect and regression history can help locate expensive areas:
- Recurring incidents around the same component;
- Tests repeatedly added after changes to one dependency;
- Fixes that create failures in unrelated functionality;
- Unusually large regression scope after small code changes;
- Repeated differences between test and production behavior.
Engineering context matters before drawing conclusions from any of these signals.
Frequently changed functionality naturally receives more defects and more testing. Business-critical areas also deserve wider regression even when their technical condition is healthy.
Repeated effort around the same dependency can still tell a useful story.
ELEKS can connect QA evidence with broader engineering work when teams need to understand why particular parts of a product repeatedly consume release time.
5. Itransition: when testers spend days following data between systems
One order can make a long trip before anybody ships it. Customer information enters a portal. Payment passes through another service. Inventory lives somewhere else. An internal platform creates the order. Logistics software receives the final details later.
Testing becomes slow when failures require somebody to inspect every stop manually. Itransition provides QA across web, desktop, mobile, and cloud applications, including functional, performance, usability, compatibility, automation, compliance-related testing, and consulting.
Cross-application testing becomes easier to investigate when evidence is collected at each handoff:
- Record the original business action and source data.
- Capture the request leaving the first application.
- Follow transformations, queues, and intermediate statuses.
- Record what each receiving application stores.
- Compare timestamps when asynchronous processing is involved.
- Verify the final business action produced from the data.
Better traceability can remove hours of guesswork.
A failed shipment should not require five teams to open five applications and compare screenshots. Logs, event histories, identifiers, API responses, and database records can narrow the search much earlier.
Itransition deserves consideration when release delays regularly come from testing and diagnosing workflows spread across several enterprise applications.
6. SoftServe: when modernization creates two QA workloads at once
Replacing part of a mature platform rarely makes the old testing disappear immediately. Existing services remain live. Replacement services need verification. Data moves between them. Temporary adapters appear. Some users stay on the old path while others move to the new one.
QA suddenly has two worlds to protect. SoftServe works across application modernization, cloud, DevOps, AI and machine learning, IoT, digital products, and managed services.
Modernization testing can become easier to manage once existing coverage is sorted by future value:
- Keep: business rules and customer journeys that remain unchanged.
- Rewrite: valuable scenarios tied too closely to retired technology.
- Add: migration, synchronization, rollback, and coexistence scenarios.
- Retire: tests protecting behavior that disappears with the old platform.
Blindly transferring an entire regression suite can preserve unnecessary work for years. Discarding it is equally risky. Older tests may contain knowledge created after production incidents, regulatory requirements, or rare customer cases.
SoftServe’s modernization and cloud background can help QA follow technical changes while deciding which previous coverage still protects something important.
7. ScienceSoft: when manual regression has become the release calendar
Manual testing can grow gradually enough that nobody notices the problem until release dates start moving around it.
Five hours becomes a day. A day becomes three. Additional browsers, roles, workflows, and integrations keep entering the regression scope.
Hiring more manual testers can shorten the queue temporarily. Repeating stable checks by hand every release still consumes capacity that could be spent on exploratory work and newly changed functionality.
ScienceSoft provides manual and automated testing across web, mobile, desktop, and enterprise software, with performance, security, usability, compatibility, and other QA services.
Automation candidates can be separated without trying to automate everything:
- Frequently repeated scenarios with stable expected results.
- High-risk workflows needed before most releases.
- Data-heavy checks where manual preparation consumes significant time.
- Cross-browser scenarios repeated with the same logic.
- API checks that can expose failures earlier than interface testing.
Exploratory testing remains valuable precisely because human attention is limited. Moving predictable repetition away from manual regression gives testers more time for unfamiliar behavior, new functionality, and cases nobody thought to script.
ScienceSoft can suit organizations trying to rebalance a mature manual QA process without treating automation percentage as the goal by itself.
8. Intellias: when environments are slowing testers before testing begins
A tester opens a task and spends the first hour trying to make staging usable. One service has the wrong version. Another contains yesterday’s build. Test accounts were modified by a different team. A connected API is unavailable. Nobody knows whether a failed scenario comes from the feature or the environment.
Release speed suffers before meaningful testing has started. Intellias works across software engineering, cloud, DevOps, data, and quality engineering, giving QA access to infrastructure and delivery concerns surrounding test execution.
Environment delays often come from several small sources:
- inconsistent application versions;
- shared test accounts and conflicting data;
- unavailable dependencies;
- manual deployment steps;
- configuration differences between environments;
- weak visibility into which build is currently being tested.
Reliable environments reduce wasted investigation as well as waiting.
Knowing exactly which build, configuration, data set, and dependency versions produced a failure makes defect reports easier for developers to reproduce.
Intellias becomes an interesting option when QA delays are closely connected with DevOps, environments, deployment processes, and the wider engineering setup around testing.
9. Luxoft: when release testing spans a large enterprise program
Some releases are slow because the software is large. Others are slow because the organization around the software is large.
Several teams contribute changes. Shared platforms have separate owners. Business acceptance happens elsewhere. External vendors maintain connected systems. Release approval requires evidence from several groups.
QA becomes partly a coordination problem. Luxoft has experience with large enterprise technology programs across industries including financial services and automotive, alongside software engineering and quality work.
Program-level testing needs information to move almost as carefully as code:
| Delivery question | Why it affects speed |
| Which change depends on another team? | Testing may start before required functionality is available. |
| Who owns each test environment? | Failures can sit unresolved while responsibility is unclear. |
| Which defects block release? | Different teams may apply different severity standards. |
| Where is acceptance recorded? | Approval can become a separate waiting stage. |
| Which evidence is required? | Teams may repeat testing when results are difficult to share. |
Coordination overhead grows quickly when each group maintains its own view of release readiness.
Shared criteria, traceable results, and agreed ownership can prevent completed testing from sitting in another queue waiting for interpretation.
Luxoft is worth considering when QA performance depends heavily on coordinating testing across a large enterprise delivery program.
Faster delivery starts with finding the waiting
A slow release does not automatically mean the QA team works slowly. Five hours can disappear while somebody waits for an environment. Another afternoon goes into rerunning flaky automation. Three days are reserved for manual regression because nobody has decided which scenarios deserve automation. A defect arrives late because testers first saw the requirement after development was finished.
Each delay points toward a different fix.
Avenga fits situations where earlier QA involvement can move important feedback closer to development. Codica suits evolving digital products where regression needs to grow with product context. N-iX can address automation estates that have become expensive to operate. ELEKS can help investigate areas repeatedly consuming testing effort. Itransition fits workflows spread across connected applications. SoftServe brings modernization context. ScienceSoft can help rebalance manual and automated regression. Intellias connects QA with environments and engineering infrastructure. Luxoft brings experience relevant to testing across large enterprise programs.
Before adding another testing stage, measure where the release actually waits. Queue time, reruns, environment problems, data preparation, defect investigation, manual repetition, and delayed decisions can tell a much better story than the total number of tests executed. Speed usually returns when work stops waiting.