Know what good software looks like.
The new system opens and every feature is present. But can your team complete a busy day's work without delays, corrections, or calls for help?
Agree what a good result means
Software quality starts with a business task and evidence that the system can support it. Agree the expected behavior, review it during delivery, and keep checking the important parts after launch.
For an illustrative trade supplier, “customers can place orders” leaves too much open. Can a customer find the correct product, see their agreed price, submit once, and receive a clear confirmation? What happens if stock changes or the accounting connection stops responding?
Those questions become acceptance checks. Cybercube offers fixed prices for agreed scope, so the required behavior, operating conditions, and checks should be part of that scope. Additional features and stronger requirements need an explicit decision about price and timing. A defect against the agreed behavior is a different conversation.
Software quality KPIs worth discussing
A key performance indicator should help someone make a decision. Choose measures that match the application and record how each is collected. The following five areas provide a practical starting point; the examples are suggestions, not Cybercube customer results or universal service guarantees.
Usability: can people complete the task?
Useful measures include task completion rate, completion time, user errors, adoption, and training time.
For the supplier's ordering portal, count successful orders against genuine order attempts. Decide how to handle abandoned sessions, test accounts, and customers who were only browsing. Observe where people ask for assistance. A faster checkout is not an improvement if staff must correct more orders afterward.
Availability: can people rely on it when needed?
Track successful availability during agreed operating hours, incident frequency, time to detect a failure, and time to restore service. Mean time between failures can help reveal reliability trends when enough incidents have been recorded.
Check an important end-to-end action, not only whether the homepage responds. If customers can sign in but cannot submit an order, their workflow is unavailable. Define how planned maintenance and partial failures enter the measure, and keep individual incident durations visible alongside averages.
Performance: does it respond under realistic demand?
Measure response time, completed operations per second, and capacity under an agreed workload. Record slower experiences as well as the average: a 95th-percentile response time describes the time within which 95% of measured responses finish.
For a stock search, specify the size of the catalogue, concurrent users, and connection conditions. Compare results using the same setup. An empty demonstration database tells you little about Monday morning with years of order history loaded.
Security: are weaknesses being addressed?
Useful indicators include unresolved vulnerabilities by severity and age, time to remediate verified findings, and security incidents. Test findings need a named owner and a record of whether the fix was checked.
A small incident count alone cannot demonstrate protection; it may also reflect limited detection. For the example portal, verify that one customer's account cannot retrieve another customer's prices or order history. Keep the evidence of that check separate from the overall incident total.
Code quality: can the software be changed safely?
Engineering measures such as complexity, coupling between components, and maintainability scores can identify areas worth reviewing. Review findings and defects that recur after changes add useful context.
Ask the team to explain the business implication. If changing a pricing rule repeatedly breaks invoices, that dependency deserves attention. Lines of code or a single maintainability score should not become a productivity target or a promise that future changes will be easy.
Give every measure a definition and an owner
Before adding a KPI to a report, write down:
- Workflow: the action and users it covers.
- Method: the data source, calculation, and exclusions.
- Baseline: the current result over a representative period.
- Target: the agreed result and conditions under which it applies.
- Response: who investigates a missed target and what happens next.
For example, an illustrative acceptance check could require a representative office user to submit a valid order without assistance and without creating duplicate records. A performance check would separately specify a response-time target under a defined workload. Keep these checks distinct so a quick response cannot disguise an incorrect result.
Avoid filling a report with measures nobody uses. Start with the workflow's most consequential failures, then add detail when it helps explain a problem.
Check quality throughout delivery
Review requirements and designs before building. During development, combine code review and automated checks with hands-on testing of real workflows. Before launch, ask the business users to check the agreed acceptance scenarios and record any unresolved issues with their impact and owner.
Handover should include the evidence needed to operate the application: test results, known limitations, user guidance, and support responsibilities. After release, review the selected KPIs and test affected workflows when changes are made.
Delivery measures such as schedule progress, budget, and completed features answer a separate question: how the project is progressing. Keep those visible alongside the quality measures. A project can finish on time while leaving the daily workflow frustrating.
From agreed scope to everyday use explains the delivery stages. Keeping software useful after launch covers ongoing care. Start your own quality discussion with one essential task, its likely failure points, and the evidence you would need to approve it.