TL;DR
- Software validation demonstrates that a system is fit for its intended use in a defined research environment.
- Start with intended use and risk assessment—not a one-size-fits-all testing checklist.
- Test the workflows that can affect research data, calculations, sample traceability, and reproducibility.
- Maintain validation evidence as software, configurations, integrations, and research processes change.
Research teams depend on software for far more than storing information. LIMS platforms track samples, electronic lab notebooks capture experiments, instrument applications generate results, and analytical tools transform raw data into research findings.
When these systems do not work as expected, the consequences can extend beyond a technical issue. Researchers may lose experimental context, use incorrect calculations, introduce inconsistencies during data transfer, or struggle to reproduce earlier work.
Software validation helps reduce these risks by establishing documented evidence that a system performs its intended functions under defined conditions. The level of assurance should reflect how the software is used, the importance of the data it handles, and the consequences of failure.
Define the Intended Use Before Testing
The first step in software validation is to establish what the application is expected to do—and what it is not expected to do.
A research team might use the same software platform for experiment documentation, sample tracking, inventory management, data analysis, and reporting. Each function can have different requirements and risks.
Document the software's purpose
Create a clear intended-use statement covering:
- The research activities supported by the software
- The users and teams who will access it
- The types of data it creates, processes, stores, or transfers
- The decisions or downstream workflows that depend on its outputs
- Any interfaces with instruments or other applications
For example, a LIMS used to track sample locations has different validation priorities from an application that calculates assay results used in a critical research decision.
Translate use cases into testable requirements
Requirements should describe observable system behavior. Avoid vague statements such as "the software should be reliable."
Instead, define requirements such as:
- Users can identify a sample through a unique identifier.
- Only authorized users can edit specified records.
- Calculations produce expected outputs for defined inputs.
- Changes to research records are traceable where required.
- Data transfers preserve relevant identifiers, units, and metadata.
Testable requirements make it easier to determine whether the software performs as expected.
Use Risk-Based Validation
Not every research application requires the same depth of validation. A risk-based approach helps teams focus effort on functions where failure could have the greatest impact.
Assess the consequences of software failure
Consider three practical questions:
- What could go wrong? Examples include incorrect calculations, missing records, altered sample identifiers, or failed data transfers.
- How likely is it? Consider system complexity, known limitations, manual workarounds, and the frequency of use.
- What would the impact be? Evaluate potential effects on data integrity, research conclusions, reproducibility, sample traceability, and downstream decisions.
A spreadsheet used for exploratory calculations may need documented formula checks and access controls. A system supporting regulated study records may require a more formal validation approach.
Match testing effort to risk
Prioritize functions that can materially affect research outcomes:
- Sample identification and tracking
- Data capture and instrument transfers
- Calculations, transformations, and analysis outputs
- User permissions and record changes
- Data export, reporting, and archival
- Backup and recovery
Document why particular functions receive more or less testing. Risk-based validation should mean proportionate assurance—not skipping important checks.
For laboratories operating under applicable GxP, GLP, clinical, or other regulatory requirements, determine the relevant obligations separately. The validation approach must reflect the system's regulated intended use, not simply its label as "research software."
Test Real Research Workflows, Not Just Individual Features
A software feature may work correctly in isolation but fail when used as part of a complete laboratory process. Validation should therefore include representative end-to-end workflows.
Build realistic test scenarios
Use workflows that reflect how researchers actually work. For example:
- Register a sample and assign its identifier.
- Record an experiment in an electronic lab notebook.
- Import data from an instrument.
- Apply a calculation or transformation.
- Review and approve the resulting record, where applicable.
- Generate a report and retrieve its supporting data.
Include normal scenarios as well as unusual conditions, such as missing values, duplicate identifiers, interrupted transfers, invalid inputs, and unexpected units.
Verify integrations and data movement
Research environments often use multiple applications. Data may move between instruments, LIMS, ELNs, analysis tools, and reporting systems.
Test whether:
- Records reach the correct destination.
- Sample and experiment identifiers remain consistent.
- Units, timestamps, and metadata are preserved.
- Failed transfers are visible and recoverable.
- Duplicate or incomplete records are handled appropriately.
For labs connecting multiple systems, an integration layer such as Scispot GLUE can help coordinate data movement across laboratory applications. Integration behavior should still be tested against the lab's specific workflows and requirements.
Protect Data Integrity and Reproducibility
Research software should help teams understand where data came from, how it changed, and how results were produced.
Validation should assess not only whether an application runs, but also whether it preserves the context needed to interpret and reproduce research.
Check data accuracy and completeness
Use representative test datasets to verify that data is captured, processed, stored, and exported correctly. Check boundary values, missing fields, decimal precision, units, and calculation logic.
Where calculations are involved, compare software outputs against independently verified expected results.
Review access, changes, and traceability
Confirm that access permissions reflect users' responsibilities. Where the research process requires it, verify that record changes can be traced to the relevant user, time, and action.
Also check whether original data and relevant metadata remain available after edits, exports, or workflow transitions.
Test backup, recovery, and retrieval
A backup process is only useful if records can be restored and retrieved. Test recovery procedures, confirm that restored data remains usable, and document the results.
For research teams, the ability to retrieve the original dataset, associated metadata, experimental context, and analysis history is particularly important when reproducing or reviewing earlier work.
Maintain Validation as Software Changes
Validation is not a one-time activity completed at implementation. Research software evolves through upgrades, configuration changes, new integrations, workflow updates, and changes in how teams use the system.
Establish change control
Document changes that could affect intended use or system behavior. For each change, assess:
- Which requirements or workflows are affected?
- Could data integrity or calculation outputs change?
- Are integrations or permissions affected?
- What testing or regression checks are necessary?
- Who reviews and approves the change?
A minor interface change may require limited verification. A change to a calculation, sample identifier structure, or data-transfer process may need broader testing.
Keep an organized validation record
Maintain a record of the validation approach and its outcomes. Depending on the software's intended use and risk, this may include:
- Intended-use statement and scope
- User requirements
- Risk assessment
- Test plans and acceptance criteria
- Executed test cases and results
- Defect and deviation records
- Change and regression-testing history
- Final review and approval
For regulated systems, the documentation should also meet the applicable regulatory and quality-system requirements.
Review software periodically
Set a review cadence based on system risk and the pace of change. Revisit the software's intended use, user access, known issues, integrations, backup and recovery arrangements, and outstanding risks.
This helps ensure that the system remains suitable as research activities and data requirements evolve.
Apply Validation Principles Across Common Research Software
Different applications require different validation priorities. A practical approach is to focus on the functions that matter most to each system's intended use.
A connected laboratory platform can reduce the number of disconnected workflows researchers need to manage. However, connecting applications does not remove the need to verify how data moves between them.
Scispot brings laboratory applications, instrument data, and workflows together through its Lab Operating System. For teams evaluating a more connected research environment, the platform can provide a foundation for governed data and coordinated workflows while validation remains tied to each laboratory's intended use.
Build More Reliable Research Workflows with Scispot
As research teams add instruments, applications, and data sources, maintaining consistency across disconnected systems becomes harder. A connected laboratory environment can help bring sample records, experiments, instrument data, and workflows into a more traceable structure.
Bring your lab data, instruments, and workflows together with Scispot. Explore Scispot
