In today’s competitive landscape, delivering high-quality software solutions is paramount to success. Whether it’s a customer-facing application, an internal business tool, or a complex enterprise system, quality assurance and testing are essential to help your software function as intended, meet user expectations, and give users a smooth experience. Oblytech’s Quality Assurance & Testing services provide a comprehensive and rigorous approach to software testing, helping your solutions stay reliable, secure, and well tuned for performance.
We collaborate with you to define your testing objectives, scope, and methodologies, creating a tailored test plan that aligns with your project requirements and budget.
We thoroughly test all functionalities of your software to ensure they meet the specified requirements and perform as expected.
We assess your software’s performance under various load conditions to identify bottlenecks and optimize its speed, responsiveness, and scalability.
We conduct security testing to identify vulnerabilities and ensure your software is protected from cyber threats and data breaches.
We evaluate your software’s user interface and user experience to ensure it is intuitive, user-friendly, and meets the needs of your target audience.
We test your software across different browsers, devices, and operating systems to ensure compatibility and a consistent user experience.
We specialize in testing mobile applications across various devices and platforms, ensuring optimal performance, functionality, and user experience.
We implement test automation frameworks to automate repetitive testing tasks, improving efficiency and test coverage.
A release owner usually asks a testing provider for a number before asking what the number is meant to protect. The better question is which failures would hurt the business most if they reached users, and how likely each one is given what has changed. A change to a payment rule carries a different risk from a change to a report heading, and an approach that gives both the same coverage wastes effort on one and underprotects the other. Risk-based planning starts with the release content, ranks the affected paths by business consequence, and then decides how deep each path must be tested.
This route fits a team that has a release date, a set of changes that touch shared behavior, and no agreed way to decide what gets tested first. A team that wants a blanket sign-off on a system nobody has described gets little from it, because testing an undefined system produces results that cannot be interpreted.
The quality assurance service page names functional, performance, security, usability, compatibility, mobile and automation testing. Each answers a different question, and the table below summarizes the failure each one is designed to surface. The point of the table is to show where a team can stop, not to imply that every release needs every row.
| Test type | Question it answers | Typical gap it exposes |
|---|---|---|
| Functional | Does each rule behave as the requirement states? | Logic errors, missing validation, wrong defaults |
| Performance | Does the system hold up under expected peak use? | Slow queries, timeouts, capacity limits |
| Security | Can the system be misused or accessed beyond its role? | Weak access control, injection paths, exposed data |
| Usability | Can the intended user complete the task without help? | Confusing sequences, hidden actions, poor error messages |
| Compatibility | Does it behave the same across the browsers and devices in use? | Layout failures, blocked features on older clients |
| Mobile | Does the mobile experience work with real connectivity and screen constraints? | Touch targets, offline states, interrupted sessions |
| Automation | Can stable checks run on every change? | Regressions that repeat across releases |
A performance test that uses a generic load of a few hundred virtual users tells the team very little if production traffic arrives in bursts at the start of a shift, or if a single batch job competes with interactive use. The useful input is a load profile built from logs: the peak hour, the mix of transactions, the number of concurrent sessions and the data volume the system holds. Without that profile the results can look reassuring and still miss the failure that users will actually meet.
The output should identify where the system stops behaving acceptably, which component limits it, and whether the limit is in code, configuration, infrastructure or the database. A finding that says the system is slow is a symptom. A finding that names the transaction and the query behind it is something a developer can fix.
Functional testing confirms that the system does what it should. Security testing checks that it does not do what it should not. That difference matters because a feature can pass every functional case and still allow a user to see records belonging to another department, or to change a value they should only read. Security testing looks at access rules across roles, at input handling, at how sessions and tokens are managed and at what the system reveals in its error messages. The scope should be agreed in writing, including which environments may be tested and which accounts the testers will use.
Testers should not be given production data to make a test feel realistic. Representative but synthetic data usually serves the purpose, and it keeps the exercise from creating a new exposure.
Automation is worth building for paths that are stable, repeated on every release and expensive to test by hand. Login, a core approval, an integration handshake and a daily report are typical candidates. Automation is a poor investment for an interface that is redesigned every month, because the scripts will be rewritten faster than they find defects. The service page mentions implementing automation frameworks to reduce repetitive testing tasks. The practical measure of success is whether the automated suite is trusted enough to block a release, and whether its failures are investigated rather than rerun until they pass.
A test report that lists the number of cases executed does not support a release decision. The report should show which risks were covered and how, which defects remain open with their severity and the workaround, which areas were not tested and why, and what the tester recommends. The last item is often the most useful. A tester who will say the release is not ready, and explain which two defects are the reason, is giving the owner something to act on.
Yes. Test design can begin from the requirements, and early scenarios often expose ambiguity that the development team can resolve before code is written. Execution waits for a build that is stable enough to test.
Quality assurance checks the system against its specification and against the risks identified in planning. User acceptance checks whether the business accepts that the system solves the problem. The two overlap, but they answer different questions and need different people.
It can add capacity and method, but the knowledge of how the business actually works stays with the client. Good engagements pair external testers with an internal subject matter expert for the workflows that matter most.
Steps to reproduce, the expected and actual result, the environment, the data used and a severity that reflects business impact rather than the tester’s frustration.
A release window is rarely long enough to test everything in sequence, so the order of work matters. The usual pattern runs risk-ranked functional checks as soon as a build is stable, followed by integration and access tests once the main paths pass, and then performance and security work on a build that is close to final. Usability and compatibility checks can run in parallel with these, because they depend more on people and devices than on code. Defects found late should be classified by business consequence before anyone decides whether the release slips, since a cosmetic fault in a rarely used screen rarely justifies a delay that a payment error would.
If a release is approaching and the test coverage is uncertain, start with a risk review of what is changing rather than with a larger test count. The quality assurance route under IT consulting covers quality assurance within IT consulting, and the custom software development services page covers the build work that testing usually follows. For ServiceNow releases, the automated testing and release assurance route covers which workflows to protect with repeatable tests and how results bear on a release decision. Discuss your release testing needs and the first conversation can focus on what would go wrong if the release went out untested.
Release exit criteria are the written conditions, agreed before testing starts, that decide whether a build may go live. They usually cover open defects by severity, completion of the risk-ranked test cases, and any untested area that a named owner has accepted. Without them, the release decision falls to whoever feels the most schedule pressure.
The criteria work best when they are short enough to read aloud in a release meeting. A workable set tends to contain these parts:
Agree the criteria with the people who will sign the release, not only with the testers. A tester can report that a criterion failed, but only the business owner can decide to accept the gap. Recording that decision with a name and a date protects both sides.
Criteria also need a rule for change: if a late build shrinks the test window, decide in advance which tier of cases drops first. Where automated checks run on every build, their results belong in the criteria as well, and the DevOps and automation service is the place to look for how that is arranged. For platform upgrades, the testing and acceptance checklist applies the same idea, and the quality assurance consulting page covers the wider approach.
Quality assurance is the set of practices that prevent defects, such as reviews, standards and test planning. Quality control checks a finished build to find the defects that remain. Testing is the main quality control activity, and a sound approach uses both.
Large enough to cover the paths the business cannot afford to break, and small enough to run on every release inside the available window. Most teams begin with core transactions and add a case whenever a defect escapes to production.
No. Defects that block a core path should be fixed first. Others can ship with a documented workaround if the business owner accepts the risk in writing and the fix is scheduled for the next release.
One that matches production in configuration, integrations and data shape, using synthetic or masked data. An environment that differs from production can pass a build that then fails live.
Most software testing misses defects for reasons the team could have seen coming. The tests were written from the build rather than from the requirements, so they confirm what the code does instead of what it should do. The data was too clean, the environment looked nothing like production, and the same happy path ran again in every cycle.
The first mistake is starting too late. When testing begins after development has closed, each defect costs a rework cycle, and the schedule absorbs the delay by dropping the checks that matter most. A requirement that two people read differently shows up as two different expected results long before anyone runs a script.
The second mistake is counting cases instead of covering risks. A suite of several hundred passing checks can still leave the approval path untested if none of those checks touch it. Each high-consequence workflow should have named cases, and the report should say which workflow each group protects. A quick test of a suite is to ask which business rule would break if a given case were deleted. If nobody can answer, the case probably protects nothing.
The third mistake is treating the test environment as a formality. Shared environments where developers push half-finished changes make failures hard to reproduce. Environments carrying a fraction of production data hide slow queries and permission problems. Keep one environment that matches production configuration closely, even at smaller data volume, and record the build number on every run so results can be traced to the code that produced them.
The fourth mistake is letting defects reach a release without a recorded decision. Open issues tend to close informally, through a chat message or a verbal assurance.
A practical first step is to check whether each requirement can be tested at all, which is the work covered by business analysis that gives testers a fixed reference. For ServiceNow platforms, the upgrades and release readiness section is the place to plan those checks against a specific release.