Property Testing Py3plex: Opportunities & Implementation

Hey everyone! Today, we're diving deep into the world of property-based testing within the py3plex library. Property-based testing, using tools like Hypothesis, is a fantastic way to ensure the robustness and reliability of our code. Instead of writing specific test cases, we define properties that should always hold true for our functions and methods. Hypothesis then generates a wide range of inputs to try and break these properties, helping us uncover edge cases and potential bugs we might have missed with traditional unit testing. Let's embark on this journey together, exploring how we can leverage property-based testing to strengthen py3plex!

1. Mapping the Targets: Identifying Property-Testable Units

First things first, we need a clear roadmap. We need to identify the most promising candidates for property-based testing within the py3plex codebase. Remember, we're aiming for deterministic, side-effect-free code paths – the pure-ish helpers, if you will – rather than getting bogged down in CLI/IO operations. Our primary focus will be on modules within py3plex/core/*, py3plex/algorithms/*, and py3plex/visualization/*. Let's check out some prime candidates:

Here’s a list of potential targets, along with a brief explanation of why they're well-suited for property-based testing:

• py3plex/core/multilayernetwork.py:MultilayerNetwork.add_node() (✅)
  • Rationale: Adding the same node multiple times should be idempotent. The network structure should remain consistent.
• py3plex/core/multilayernetwork.py:MultilayerNetwork.add_edge() (✅)
  • Rationale: Adding the same edge multiple times should also be idempotent. Edge counts and connections should remain the same.
• py3plex/core/multilayernetwork.py:MultilayerNetwork.remove_node() (🟡)
  • Rationale: Removing a node should consistently remove all its associated edges. We can verify the absence of the node and its connections.
• py3plex/core/multilayernetwork.py:MultilayerNetwork.remove_edge() (✅)
  • Rationale: Removing a specific edge should ensure it's no longer present in the network. Edge counts should decrease accordingly.
• py3plex/core/multilayernetwork.py:MultilayerNetwork.get_neighbors() (🟡)
  • Rationale: The neighbors of a node should be consistent regardless of the order in which edges were added. We can check for consistent neighbor sets after shuffling edge order.
• py3plex/algorithms/clustering.py:louvain() (🔴)
  • Rationale: While Louvain is inherently heuristic, we can test metamorphic properties. For instance, adding a duplicate layer shouldn't drastically change the clustering result. (Complex: Heuristic algorithm, requires careful property definition and tolerance)
• py3plex/algorithms/community_detection.py:label_propagation() (🔴)
  • Rationale: Similar to Louvain, metamorphic testing is key here. Permuting node labels should lead to an isomorphic community structure. (Complex: Stochastic algorithm, needs specific testing strategies)
• py3plex/visualization/colors.py:generate_distinct_colors() (✅)
  • Rationale: This function should consistently generate a specified number of distinct colors. We can check for color uniqueness and proper length of the output. (Quick Win: Relatively simple function, easy to define properties)
• py3plex/core/base.py:BaseGraph.get_edges() (✅)
  • Rationale: This method should return all edges present in the graph. We can verify this by comparing the returned edges with the edges added.
• py3plex/core/base.py:BaseGraph.get_nodes() (✅)
  • Rationale: Similarly, this should return all nodes. We can check for consistency with added nodes.
• py3plex/algorithms/centrality.py:degree_centrality() (🟡)
  • Rationale: Scaling edge weights should monotonically affect degree centrality. (Medium: Need to handle potential floating-point issues and edge cases)
• py3plex/core/multilayernetwork.py:MultilayerNetwork.is_multigraph() (✅)
  • Rationale: Should correctly identify if the graph is a multigraph based on edge multiplicity. This is a straightforward structural property.
• py3plex/core/multilayernetwork.py:MultilayerNetwork.to_undirected() (🟡)
  • Rationale: Converting to an undirected graph should preserve node count and merge bidirectional edges correctly. (Medium: Needs to consider edge directionality and potential merging)

This list gives us a solid starting point for our property-based testing efforts. Remember, the (✅), (🟡), and (🔴) markers indicate the perceived complexity of implementing tests for each target. Quick wins (✅) are the low-hanging fruit we'll tackle first!

2. Defining Properties and Invariants: What Should Always Hold True?

The heart of property-based testing lies in defining the properties that our code should always satisfy. These properties act as constraints, guiding Hypothesis in its input generation. Let's delve into some specific properties and invariants for a few of our target functions.

For py3plex/core/multilayernetwork.py:MultilayerNetwork.add_node(node):

• Idempotent Property: Adding the same node multiple times should not change the network's node set. The node list should not contain duplicates. Think of it like a set: adding the same element multiple times doesn't change the set.
• Node Count Invariant: After adding N unique nodes, the network should contain exactly N nodes. This is a basic but crucial structural invariant.
• Data Type Invariant: The added nodes should maintain their data type. If you add an integer as a node, it should remain an integer within the network.

For py3plex/core/multilayernetwork.py:MultilayerNetwork.add_edge(node1, node2, layer):

• Idempotent Property: Similar to adding nodes, adding the same edge multiple times should not alter the network's edge structure. Edge multiplicity should be handled correctly.
• Node Existence Invariant: Adding an edge between nodes that don't exist should either create those nodes or raise an exception (depending on the desired behavior). We need to be explicit about how we want this case handled.
• Edge Count Invariant: After adding M unique edges, the network should contain exactly M edges (or 2M if it's an undirected graph). This invariant ensures we're tracking edges accurately.
• Self-Loop Constraint: If self-loops are disallowed, adding an edge from a node to itself should either be prevented or raise an exception. This enforces a key constraint on the network's structure.

For py3plex/visualization/colors.py:generate_distinct_colors(n):

• Uniqueness Property: The generated colors should be distinct from each other, ideally with a quantifiable minimum color distance (e.g., in a color space like CIELAB).
• Output Length Invariant: The function should always return a list containing n colors.
• Valid Color Range Invariant: The generated colors should fall within the valid range for the chosen color representation (e.g., RGB values between 0 and 1). This prevents invalid color outputs.

For py3plex/core/base.py:BaseGraph.get_edges():

• Completeness Property: The returned list of edges should contain all edges that have been added to the graph. Nothing should be missing.
• Consistency Property: The edges returned should have the correct source, target, and layer information (if applicable). This ensures data integrity.

These examples showcase how we can translate desired behaviors into concrete properties and invariants. Remember, clear and precise property definitions are crucial for effective property-based testing.

3. Crafting Strategies: Generating Smart Inputs with Hypothesis

With our properties defined, we need a way to generate inputs that can effectively test them. This is where Hypothesis strategies come into play. Strategies are the blueprints for creating the data that will be fed into our functions. We want to generate inputs that are both diverse and likely to uncover potential issues.

Here are some strategy ideas tailored to py3plex's data structures:

• Small Integers (0..200): For node and edge IDs, small integers are a good starting point. This allows us to test basic graph manipulations without overwhelming the system. We can use st.integers(min_value=0, max_value=200).
• Bounded Floats (No Inf/NaN Unless Tested on Purpose): For edge weights or centrality measures, we'll need floating-point numbers. It's crucial to avoid infinities and NaNs unless we're specifically testing how the code handles them. st.floats(min_value=-1.0, max_value=1.0, allow_nan=False, allow_infinity=False) provides a good range.
• Text Labels: Nodes and layers can often be labeled with text. We can use st.text(min_size=1, max_size=10) to generate strings of varying lengths. Consider using a restricted alphabet (e.g., ASCII letters and numbers) to avoid potential encoding issues.
• Graph-like Data (Constrained Tuples): Edges are often represented as tuples of (source, target, layer). We can generate these tuples using st.tuples(), combining strategies for node IDs and layer labels. The key is to constrain these tuples to create valid graph structures. For instance, we might want to ensure that node IDs are within a certain range or that layers exist within a predefined set.
  • Example: st.tuples(st.integers(0, 50), st.integers(0, 50), st.text(min_size=1, max_size=5)).filter(lambda x: x[0] != x[1]) (generates edge tuples with distinct source and target nodes)
• Adjacency Lists/Multilayer Edge Sets: These are more complex graph representations. We can build strategies to generate these structures by combining lists and sets of edges. We'll need to pay close attention to the relationships between nodes and edges to ensure we're creating valid graphs.
• Assumptions are Key: Strategies often need preconditions. For example, we might want to assume that a graph is connected before testing a shortest-path algorithm. Hypothesis's assume() function allows us to filter out inputs that don't meet these preconditions. Document these assumptions clearly in your tests.

Remember to avoid pathological generation that violates API contracts. We don't want to crash our code with invalid inputs unless we're specifically testing error handling. State your assumptions clearly to avoid confusion.

4. Implementing the Tests: Bringing Properties to Life with Code

Now for the fun part: writing the actual tests! We'll create new pytest files under the tests/property/ directory, following the naming convention test_<module>_props.py. Each file will contain Hypothesis-powered tests for a specific module or set of functions.

Let's look at a basic example for testing MultilayerNetwork.add_node():

from hypothesis import given, strategies as st, settings, example
import pytest
from py3plex.core.multilayernetwork import MultilayerNetwork

@settings(deadline=None, max_examples=200, derandomize=True)
@given(node=st.integers())
def test_add_node__idempotent(node):
    net = MultilayerNetwork()
    net.add_node(node)
    net.add_node(node)  # Adding the same node again
    assert node in net.nodes
    assert len(net.nodes) == 1, "Node should only be added once"

@settings(deadline=None, max_examples=200, derandomize=True)
@given(nodes=st.lists(st.integers(), min_size=1, max_size=10))
def test_add_node__count_invariant(nodes):
    net = MultilayerNetwork()
    for node in nodes:
        net.add_node(node)
    assert len(net.nodes) == len(set(nodes)), "Node count should match unique node count"

@example(node=10)  # Pinning a known edge case
def test_add_node__idempotent_example(node):
    test_add_node__idempotent(node)

Key things to note in this example:

• Reproducible Settings: We use @settings(deadline=None, max_examples=200, derandomize=True) to ensure our tests are reproducible and have sufficient coverage. deadline=None removes time limits, max_examples=200 sets the number of generated examples, and derandomize=True reduces flakiness.
• @given Decorator: This is Hypothesis's magic. It tells Hypothesis to generate inputs based on the provided strategy (st.integers() in this case).
• Clear Test Names: We use the test_<function>__<property>() naming convention for clarity.
• @example Decorator: This is crucial for pinning known edge cases. It ensures that specific inputs always trigger the test, even if Hypothesis's random generation might miss them. Include at least one @example per test.
• No Network/FS/Randomness: We avoid network access, file system operations, and heavy randomness in our property-based tests. If randomness is unavoidable, seed internal RNGs for reproducibility.
• Floating-Point Comparisons: If your results involve floats, use pytest.approx for comparisons with explicit tolerances to account for potential floating-point inaccuracies.

5. Generating Patch-Style Output: Sharing Your Tests

Once you've written your tests, you'll want to share them with the py3plex community. The best way to do this is by providing unified diffs (patch-style output). This makes it easy for others to review and integrate your changes.

To generate a patch, you can use git diff:

git diff > property_tests.patch

This will create a property_tests.patch file containing the diffs for your new test files.

Also, remember to include a short snippet in the tests/property/README.md file describing how to run the property-based tests. Something like this:

To run the property-based tests:

```bash
pytest -q tests/property -k props

The -k props option allows you to run only tests that have "props" in their name, which is a convenient way to target your property-based tests.

Constraints and Style: Keeping Things Clean and Efficient

Let's quickly recap some important constraints and style guidelines:

• Public APIs (Mostly): Stick to public APIs whenever possible. If you must use internal APIs, document them clearly and be aware that they might change in the future.
• Test Speed: Keep your tests fast! Aim for each test to take less than 1 second on average. Skip very heavy inputs to avoid slowing down the test suite.
• Clear Names: Use descriptive test names following the test_<function>__<property>() convention.
• Explain Invariants: If you can't guarantee a strict invariant, explain why and propose a weaker property that can be tested.

Conclusion: A Stronger py3plex Through Property-Based Testing

Property-based testing is a powerful tool for building more robust and reliable software. By defining properties and letting Hypothesis generate inputs, we can uncover edge cases and potential bugs that traditional unit tests might miss. We've covered a lot of ground here, from identifying target functions to crafting strategies and implementing tests. Now it's time to put these principles into practice and make py3plex even stronger!

I'm excited to see what properties you guys come up with and the tests you implement. Let's collaborate to make py3plex a shining example of well-tested code! Remember to start with the quick wins and gradually tackle the more complex algorithms. Happy testing!