ServiceNow Software Testing Interview Questions & Answers

A shared checklist for when a story is complete, usually including code review, tests passing, no open critical bugs and documentation updated.

GET retrieves data and has parameters in the URL; POST sends data in the body to create or process something.

Using experience to guess where defects likely hide, such as empty fields, special characters or unexpected sequences, and designing tests for them.

Smoke tests run on every build for quick feedback; full regression runs less often, such as nightly or before release.

Success, failure and pending flows, timeouts, refunds, double-click payments, every method (UPI, cards, net banking), rounding, security (no card data stored) and reconciliation with bank reports.

A team meeting at the end of a sprint to discuss what went well, what did not and what to improve next.

Functional testing checks what the system does (login, checkout). Non-functional testing checks how well it does it (performance, security, usability, compatibility).

Testing at the edges of input ranges, such as 17, 18 and 19 for an age limit of 18, since errors cluster at boundaries.

Re-check environment, data, browser and steps; check the logs; try the reporter's setup; add video and console logs; mark it 'cannot reproduce' only after discussing with the developer, and keep monitoring.

Prioritising tests by the likelihood and impact of failure, so critical areas get the most coverage when time is limited.

Valid login, invalid login, forgot password, locked account and session handling are the main scenarios to cover.

Test 17, 18, 19, 59, 60 and 61.

Verify inserted, updated and deleted rows match UI actions, check constraints, duplicates, NULLs and totals with COUNT and SUM queries.

A final document summarising what was tested, defects found and open, metrics, lessons learned and the recommendation for release.

Use mocks or stubs from the API contract such as Swagger, write test cases early and validate again when the real service is available.

Unit, integration, system and acceptance testing, moving from individual components to the complete product in front of users.

Assess impact and risk, discuss with product and dev, decide to fix, defer with a workaround or delay release, and document the decision.

A clear title, steps to reproduce, expected vs actual result, environment/build, severity and priority, and evidence (screenshot, video, logs).

Testing that is continuous and collaborative within each sprint, with testers working alongside developers and product owners from the start.

The extent of requirements or code exercised by tests. Measure requirement coverage with a traceability matrix and code coverage with tools.

Verification: are we building the product right? (reviews, walkthroughs — no code run). Validation: are we building the right product? (actual testing against user needs).

New → Assigned → Open → Fixed → Retest → Verified → Closed, with Reopened, Rejected, Deferred and Duplicate as side paths.

Talk to developers and users, explore the product, review similar systems, create notes and mind maps, confirm assumptions and write test cases as you learn.

Internationalisation tests that the product can support many languages and formats; localisation tests that a specific locale is correct for text, currency, date and layout.

Learning, test design and execution at the same time, without scripted cases — time-boxed sessions with a charter (e.g. 'explore checkout with coupons'), noting bugs and ideas. Great for finding edge cases.

By business criticality, usage frequency, defect history, risk and dependencies, running high-priority cases first.

Measuring speed and stability: load (expected users), stress (beyond limits), spike (sudden surge), endurance (long duration) and scalability testing.

Create a request with method, URL, headers and body, send it, check status and body, add test scripts and save it in a collection.

Testing shows presence of defects, exhaustive testing is impossible, test early, defects cluster, the pesticide paradox, testing is context-dependent and absence-of-errors is a fallacy.

Valid card and PIN, wrong PIN, three failed attempts, amount above balance, amount above daily limit, invalid denomination, machine out of cash, network failure and receipt printing.

A technique that tests all pairs of input values instead of every combination, greatly reducing test cases while catching most defects.

Defects found after release that testing should have caught; leakage % = defects in production / total defects x 100.

Smoke testing checks that the critical features of a new build work (broad and shallow). Sanity testing checks that a specific fix or area works after a change (narrow and deep).

A defect that stops testing or release of a feature because no workaround exists, so it is highest priority.

A model with many fast unit tests at the base, fewer API or integration tests in the middle and a small number of UI tests at the top.

Valid login, wrong password, empty fields, case sensitivity, max length, SQL-injection strings, password masking, 'remember me', lock-out after N attempts and the forgot-password link.

Status code, response body and schema, headers, response time, error messages for invalid input, authentication (401/403) and that data is saved in the database.

User Acceptance Testing — end users or the client verify that the system meets business needs in a production-like environment before go-live.

List conditions and actions in a table and cover every combination of conditions (rules) — ideal for business rules like loan approval: salary > X AND credit score > Y → approve.

Break the work into modules and test cases, use past velocity or three-point estimation (optimistic, likely, pessimistic), add time for test data, environment issues, retesting and regression, and keep a risk buffer.

SDLC is the full software development life cycle; STLC is the testing life cycle: requirement analysis, test planning, test design, environment setup, execution and closure.

Designing tests from user flows described in use cases to verify complete real-world interactions.

Reproduce and assess impact, inform stakeholders, help developers fix and verify in staging, run regression and check monitoring after the hotfix, then do a post-mortem.

Re-testing existing features after a change. Automate stable, repetitive, high-value flows that run every release.

An epic is a large body of work; stories are user-facing slices of it; tasks are technical work items; bugs are defects.

A document defining scope, approach, resources, schedule, entry and exit criteria, risks and deliverables of testing.

An urgent patch released outside the normal schedule to fix a critical production defect, followed by regression testing and merge back to the main branch.

Rules such as NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY and CHECK that keep data valid and consistent.

Send page, size, sort and filter parameters, then verify counts, order, boundaries (first, last, empty page) and invalid parameter handling.

Peer review for clarity, coverage of requirements, correct expected results, test data and traceability, then update and baseline them.

An API style using HTTP methods on resources identified by URLs, usually exchanging JSON and staying stateless.

Allowed and disallowed formats, size limits, empty file, duplicate names, special-character names, cancel upload, slow network and multiple files.

A suite is a collection of related test cases; a run is one execution of a set of tests against a build.

Static testing reviews documents and code without executing; dynamic testing executes the software to observe behaviour.