Testing Search and Filter Functionality in JSON Tools

Core Concepts: Search vs. Filter

While often used together, search and filter have distinct purposes when dealing with JSON:

• Search: Typically involves finding occurrences of a specific string or pattern within the values or keys of the JSON data. It might highlight matches or list parent objects/arrays containing matches.
• Filter: Involves selecting entire objects or array elements based on criteria applied to their properties. This reduces the dataset to only include items that match the conditions.

A robust tool might combine these, allowing users to first filter a large dataset down to relevant items and then search within the filtered subset. Testing must consider these combined scenarios.

Understanding the JSON Structure

JSON's hierarchical structure (objects, arrays, nested values) significantly impacts how search and filter are implemented and, consequently, how they should be tested. Consider this example:

{
  "name": "Example Product",
  "id": "prod-123",
  "price": 49.99,
  "tags": ["electronics", "gadget", "sale"],
  "details": {
    "weight_kg": 0.5,
    "dimensions_cm": {
      "width": 10,
      "height": 5,
      "depth": 2
    },
    "manufacturer": "Acme Corp"
  },
  "reviews": [
    {
      "user": "Alice",
      "rating": 5,
      "comment": "Great product!"
    },
    {
      "user": "Bob",
      "rating": 3,
      "comment": "It's okay, a bit pricey."
    }
  ],
  "inStock": true
}

A search for "Acme" should find the `manufacturer` value. A filter for items where `price < 50` should include this object. A filter for items where any `rating` is 5 should also include this object. Testing must verify that search and filter logic correctly traverse and interpret these nested and array structures.

Types of Search to Test

JSON tools might offer different search capabilities. Each requires specific test cases:

• Full Text Search: Search across all string values. Test with substrings, whole words, case sensitivity/insensitivity.
• Key Search: Search for specific key names (e.g., find all objects with a key named "id"). Test with exact key names, partial matches, nested keys.
• Value Search: Search specifically within values of a certain type (e.g., find the number `49.99`, or the boolean `true`).
• Path-Based Search (e.g., JSONPath): Search for values at a specific path (e.g., $.details.manufacturer). Test various path expressions, including array indices ($.reviews[0].user), wildcards ($.reviews[*].rating), deep scans ($..comment).
• Regex Search: Allow searching using regular expressions. Test with complex patterns, escaping special characters, and performance on large data.

Types of Filter to Test

Filters apply criteria to objects or array elements. Common filter conditions include:

• Equality: key = value. Test with strings, numbers, booleans, `null`. Test case sensitivity for strings.
• Comparison: key > value, key < value, key >= value, key <= value. Test with numbers and potentially dates/times if supported.
• Existence/Presence: key exists, key is null/not null. Test whether a key is present or its value is specifically `null`.
• Array/Object Property Match: arrayKey contains value, objectKey has property nestedKey. Test filtering based on properties within nested arrays or objects. For arrays, test matching *any* element or *all* elements.
• Logical Operators: Combining conditions with AND, OR, NOT. Test complex filter expressions involving multiple conditions and parentheses.
• Path-Based Filtering: Similar to search, filtering based on conditions applied to values found via a specific path.

Testing Strategies

A multi-pronged approach ensures comprehensive test coverage:

• Unit Tests: Test the underlying search and filter logic functions in isolation. Provide various JSON snippets and test inputs, verifying the correct output (e.g., list of matched paths, filtered array of objects). This is fastest and easiest for catching logic bugs.
• Integration Tests: Test the integration of the search/filter logic with other parts of the tool, such as the JSON parser, the data visualization component, and the input parsing for search/filter queries. Does entering "price < 50" into the UI correctly trigger the filter function with the parsed JSON data?
• End-to-End Tests: Simulate user interaction via the UI. Enter search terms or filter criteria into the input fields, click buttons, and verify the visual output (e.g., correct elements are shown/hidden, search matches are highlighted). Use tools like Playwright or Cypress if applicable (though this specific page is static, the principle applies to testing the actual tool).
• Manual Testing: Crucial for exploratory testing and catching usability issues. Manually test various complex scenarios, edge cases, and combinations that might be hard to automate.
• Performance Testing: Test search and filter responsiveness on large JSON files (MBs or even GBs if applicable). Measure the time taken and memory usage. This is especially important for tools designed to handle big data.

Example Test Cases

Here are examples of specific test cases, categorized by the type of input or scenario:

Positive Cases (Expected Match/Filter)

• Search for an exact string value: `gadget` in the tags array.
• Filter objects where `inStock` is exactly `true`.
• Filter objects where `price < 50`.
• Search for a key name: `user`.
• Filter objects where nested `details.manufacturer` equals `Acme Corp`.
• Filter objects where any review rating >= 4.
• Combined: Filter where `price > 40` AND `inStock = true`. Then search for `Product` in the filtered results.
• Path Search: $.details.dimensions_cm.width
• Path Filter: Objects where $.reviews[1].user equals Bob.

Negative & Edge Cases (Expected No Match/Filter or Specific Behavior)

• Search for a string that does not exist.
• Filter using a key that does not exist: nonExistentKey > 10. Expect no results.
• Filter using a key with wrong type comparison: inStock > 10 (comparing boolean and number). Expect error or no results.
• Search/Filter in an empty JSON object {} or array []. Expect no results.
• Search/Filter in JSON with `null` values: e.g., { "key": null }. Search for "null" (string) vs. Filter where key is null.
• Search/Filter in JSON with empty strings or zero values: e.g., { "str": "", "num": 0 }.
• Search/Filter with special characters in search term or filter value (commas, quotes, brackets, regex metacharacters).
• Filter with logical operators that result in no matches: price > 100 AND inStock = false.
• Filter targeting a nested path that does not exist for some objects.
• Path Search/Filter using invalid path syntax. Expect error or no results.

Complex & Combination Cases

• Filter based on properties within deeply nested objects.
• Filter based on properties within objects inside arrays inside objects (e.g., $.details.reviews[0].rating).
• Search for a term that appears multiple times within a single value or object.
• Filter using a complex boolean expression: (price < 20 OR price > 80) AND NOT inStock.
• Test search/filter on JSON containing different data types: strings, numbers, booleans, nulls, nested objects, nested arrays.
• Test search/filter on JSON with Unicode characters or emojis.

Implementation Considerations & Testing Reflection

The specific test cases you write will depend heavily on the syntax your tool uses for search/filter queries (e.g., simple string input, a custom query language, JSONPath, etc.).

Consider how the tool handles errors or invalid input for search/filter. Does it fail gracefully? Does it provide helpful error messages? These aspects also need testing.

Finally, remember that performance characteristics change with data size and query complexity. Basic unit tests are fast, but integration and end-to-end tests with realistic, large datasets are essential to catch performance bottlenecks before they impact users.

Conclusion

Thorough testing of search and filter functionality in JSON tools is vital for providing a reliable and efficient user experience. By understanding the different types of search and filter operations, the impact of JSON structure, and employing a mix of unit, integration, end-to-end, and performance testing strategies with a variety of positive, negative, and complex test cases, developers can build confidence in their tool's ability to handle the complexities of querying JSON data.