Exporting Results from Jira: Complete Guide

Exporting results from Jira is often treated as a routine task. Most teams assume it is as simple as running a search, clicking Export, choosing CSV or Excel, and moving on. Reports get shared, data gets archived, and Jira quietly returns to being a tracking tool rather than a reporting one. That workflow feels familiar because it usually works well enough for day-to-day needs.

What tends to get overlooked is how much gets lost or constrained in that process. Field limits, partial datasets, formatting issues, and hard caps on export size quietly shape what actually leaves Jira. Over time, exports stop reflecting the real state of testing and delivery, even though they look complete at first glance.

That is where exporting results from Jira stops being a mechanical step and starts becoming a capability that needs intent. When exports are approached as a way to preserve context, accuracy, and traceability rather than just data rows, the entire workflow changes.

In this guide, I’ll show what exporting results from Jira really involves, where the native approach falls short, and how to make exports reliable enough.

Most Teams Spend 5+ Hours Exporting Data

Ad-hoc reporting and scattered test data slow exports. Auto-generate Jira reports to ensure full traceability.

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Exporting Results from Jira: Supported Formats

Jira provides multiple export formats, and each one follows a specific flow in the UI. The steps below show exactly how to export issues using each supported format and what needs to be set up beforehand to avoid incomplete or unusable data.

1. CSV (All Fields)

This format exports every system and custom field linked to the issue type.

Steps:

1. Open Jira and select Issues from the top navigation.
2. Apply filters using basic search or switch to Advanced issue search and use JQL.
3. Verify that the result set includes all required issues.
4. Click Export in the top-right corner.
5. Select CSV (All fields).
6. Download the generated file once Jira completes processing.

2. CSV (Current Fields)

This format exports only the fields currently visible in the issue search view.

Steps:

1. Open Issues and run the required search or JQL.
2. Select Columns > Configure columns.
3. Add, remove, and reorder columns to match the required output.
4. Confirm the column configuration.
5. Click Export in the top-right corner.
6. Select CSV (Current fields).
7. Download the file.

3. Excel CSV

This option creates a CSV file optimized for direct use in Excel.

Steps:

1. Open the Issues view and apply filters or JQL.
2. Configure visible columns if required.
3. Select Export in the top-right corner.
4. Choose Export Excel CSV.
5. Open the downloaded file in Excel.

This follows the same column rules as CSV (Current fields) but improves Excel compatibility.

4. XLSX (Form Data)

This format is used when exporting data collected through Jira forms attached to issues.

Steps:

1. Open a project that contains issues with forms.
2. Navigate to the issue list or individual issues with completed forms.
3. Open the Export menu.
4. Select Export XLSX (form data).
5. Download the file containing only form responses.

How to Export Search Results from Jira

Exporting search results in Jira is driven entirely by how the issue search is constructed. Jira does not validate intent during export, it simply exports the result set produced by the active search mode. Because of this, understanding how each search mode scopes data is critical before downloading any export.

1. Using Basic Search

Basic search uses Jira’s guided filtering interface, where conditions are built through predefined fields and operators. Each filter selection translates into an underlying query, but that logic remains implicit and cannot be fine-tuned beyond the available UI controls.

This makes basic search reliable for straightforward exports where criteria are stable and limited to common fields, but unsuitable for complex reporting that requires historical states, nested conditions, or cross-field logic.

Because basic search applies filters sequentially and visibly, it reduces the risk of accidental exclusions. However, it also restricts access to advanced fields and operators, which means some issues may be unintentionally omitted if the required condition cannot be expressed through the UI.

Steps:

1. Open Jira and select Issues from the top navigation.
2. Ensure Basic search mode is active.
3. Apply filters such as Project, Issue type, Status, Assignee, or Priority.
4. Review the issue list to confirm that all expected issues are present.
5. Configure visible columns if exporting Current fields or Excel CSV.
6. Click Export in the top-right corner.
7. Select the required export format and download the file.

2. Using Advanced Search (JQL)

Advanced search exposes Jira Query Language directly, allowing full control over how issues are selected. Unlike basic search, JQL supports complex boolean logic, date functions, field history, and multi-project queries. The export output is therefore only as accurate as the query itself, which makes validation essential before export.

JQL-based searches are particularly useful when exporting data for audits, trend analysis, or workflow analysis, where conditions such as status changes, time ranges, or exclusion rules must be explicitly defined. However, because the logic is not visual, small errors in the query can significantly alter the exported dataset.

Steps:

1. Open Issues and switch to Advanced issue search.
2. Enter the required JQL query.
3. Run the query and validate the result count and issue list.
4. Configure columns if exporting view-based formats.
5. Click Export and select the required format.
6. Download the generated file.

3. Exporting Saved Filters

Saved filters persist a specific search definition, including its query logic and default column configuration. When exporting from a saved filter, Jira does not recalculate or reinterpret the search, it simply executes the stored definition.

This makes saved filters ideal for recurring exports, shared reports, and team-level data pulls where consistency matters more than ad-hoc flexibility.

Because filters can be edited over time, exports based on saved filters should always be reviewed before use, especially in regulated or reporting-heavy contexts.

Steps:

1. Navigate to Filters > View all filters.
2. Open the required saved filter.
3. Review the issue list and column configuration.
4. Click Export in the top-right corner.
5. Select the export format and download the file.

Most Teams Spend 5+ Hours Exporting Data

Ad-hoc reporting and scattered test data slow exports. Auto-generate Jira reports to ensure full traceability.

Try Jira Test Management

How to Export Large Datasets (Over 1,000 Issues)

Jira’s native export functionality is optimized for smaller datasets and often enforces limits when exporting large result sets. When the issue count exceeds these limits, exports may fail, truncate data, or return incomplete files without explicit errors. Handling large exports requires deliberate scoping and sequencing.

• Break exports into smaller ranges: Split the dataset using JQL conditions such as date ranges, sprints, versions, or issue keys to keep each export within limits.
• Use incremental exports: Export issues in batches based on creation or update timestamps, then merge files externally for a complete dataset.
• Prefer JQL over basic search: JQL allows precise slicing of large datasets, which reduces accidental overlaps or gaps between exports.
• Validate result counts before exporting: Always confirm the issue count returned by the search to ensure each batch stays within allowable limits.
• Consolidate externally: Combine multiple CSV files in spreadsheet tools or data processing systems after export to rebuild the full dataset.

How to Export Report Data from Jira Service Management

Exporting report data from Jira Service Management differs from standard issue exports because reports are generated from service-specific metrics such as request types, SLAs, queues, and customer interactions. These reports reflect aggregated or calculated data rather than raw issue fields.

• Access reports from the service project: Open the relevant Jira Service Management project and navigate to the Reports section.
• Select the required report type: Choose reports such as SLA performance, request volume, or resolution time, depending on the data needed.
• Apply filters and time ranges: Configure date ranges, request types, or queues before exporting to ensure the report reflects the correct scope.
• Use built-in export options: Where available, use the report’s export or download option to generate CSV or spreadsheet-compatible output.
• Cross-check with issue-level data: For audits or deep analysis, validate report exports against issue exports to account for calculated fields and aggregation logic.

Exporting Configuration and Backup Data from Jira

Exporting configuration and backup data in Jira is a system-level operation and is fundamentally different from exporting issues or reports. These exports are designed for migration, disaster recovery, or environment replication rather than analysis or reporting.

• Full site backups: Jira Cloud allows admins to generate site backups from the administration settings, which include projects, issues, attachments, and most configuration data in a single archive.
• Project-level exports: Individual projects can be exported to preserve project structure, workflows, and issues, typically used during project migration or archival.
• Configuration exports: Certain configuration elements such as workflows, screens, and schemes can be exported separately to replicate setup across environments.
• Automation and rule exports: Automation rules can be exported to JSON files for reuse or version control in other Jira instances.

• Restore and migration considerations: Backup files are intended for restoration or migration and are not suitable for selective data analysis or partial imports.

How to Export Jira Product Discovery Data

Jira Product Discovery focuses on ideas, insights, and prioritization fields, which means its export options are more limited and structured differently compared to standard Jira issues. Exports are primarily designed for analysis and sharing rather than system backup or migration.

• Export ideas from the Ideas view: Open a Jira Product Discovery project, navigate to the Ideas view, apply filters or views, and use the export option to download idea data.
• Field-based exports: Only fields configured and visible in the current view, including custom prioritization fields, are included in the export.
• CSV as the primary format: Jira Product Discovery exports are typically limited to CSV, optimized for analysis in spreadsheets or product tools.
• No native configuration backups: JPD does not support exporting project configuration, formulas, or prioritization logic as reusable artifacts.
• Permission-dependent data: Exported data respects visibility and access controls, so restricted ideas or fields may be excluded.

Common Issues When Exporting Results from Jira

Exporting results from Jira can fail or produce incomplete data when search setup, format selection, or system limits are overlooked. These issues are usually not errors in the export process itself, but side effects of how Jira scopes and prepares data for download.

• Incomplete issue sets: Filters or JQL conditions may unintentionally exclude issues, especially when relying on default search settings or unvalidated queries.
• Missing fields in exports: Using view-based formats such as CSV (Current fields) without configuring columns results in partial data.
• Export size limitations: Large result sets may be truncated due to Jira’s native export limits, leading to missing issues without explicit warnings.
• Flattened or unreadable data: Multi-value fields such as labels, sprints, or components are flattened into single cells, requiring manual cleanup.
• Permission-based data gaps: Fields or issues restricted by permissions are silently omitted from the export output.

Most Teams Spend 5+ Hours Exporting Data

Ad-hoc reporting and scattered test data slow exports. Auto-generate Jira reports to ensure full traceability.

Try Jira Test Management

How Can BrowserStack Test Management Help With Jira Exports?

BrowserStack Test Management for Jira extends its native export capabilities by adding structured test data that Jira does not manage by default. Instead of exporting only issues, teams can export test cases, test executions, and traceability data that are directly linked to Jira work items, making exports more meaningful for quality reporting and audits.

Because test management happens inside Jira, exported data stays consistent with Jira workflows and permissions. This removes the need to manually reconstruct test coverage, execution status, or requirement linkage after exporting, especially when sharing reports with stakeholders or moving data into external analysis tools.

Features that help with Jira exports and test data workflows

• Test case exports: Export test cases with structured steps, expected results, and metadata for analysis or documentation.
• Test execution data: Export execution results, statuses, and run history tied to Jira issues and releases.
• Requirements traceability: Export traceability data showing links between requirements, test cases, and defects.
• Filtered exports: Apply filters on test cases and executions to generate targeted export datasets.
• Jira-native integration: Ensure exported test data remains aligned with Jira issues, workflows, and permissions.

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Conclusion

Jira’s native export options often limit the number of issues, flatten multi-value fields, and exclude custom test data, making it difficult to generate complete reports or analyze complex test coverage. Teams frequently need to perform multiple exports and manually combine data to get a usable dataset.

BrowserStack Test Management solves this by providing structured test data, traceability, and execution results within Jira, eliminating the need for repeated exports or manual consolidation. This makes reports more comprehensive, actionable, and easier to share with stakeholders while keeping data aligned with existing workflows.

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