# Testing Graph Properties

Graph formats abound. As well as documented and widely-supported formats like GEXF, GML and GraphML there are as many ad-hoc or sparsely-documented formats. The latter of these usually arising as the input or output language for a standalone program implementing specific graph algorithms.

Suppose that we are trying to accomplish some task involving a graph and our task involves several steps and several different packages or programs. For an example we might be trying to colour a graph using one program, compute the graph’s automorphism group with another program and then visualise the graph using a third layout program.

In this setting we are likely to export and import our graph in multiple formats to facilitate using those different packages and we may even be forced to make ad-hoc manual or scripted changes to the file representation of the graph, for example to augment the data with vertex properties like colours or weights. Under these circumstances there is some risk that the graph file could become corrupted or invalid in some way and no longer represent the graph it claims to.

How can we be certain that a data file that purports to contain a certain graph in a specific format really does?

## Testing graph properties

As an example, consider the Petersen graph. This graph is the smallest bridgeless cubic graph with no three-edge-colouring. We know some other properties of the Petersen graph, such as it has 10 vertices, 15 edges, radius 2, diameter 2 and girth 5. So a list of testable properties for the Petersen graph begins something like this:

• vertices 10,
• edges 15,
• diameter 2,
• girth 5,
• maxdegree 3,

A property testing program should be able to load a file purporting to contain a representation of the Petersen graph and test whether or not the it has these properties.

A graph failing to have all of these properties can certainly be rejected as not being the Petersen graph. A graph having enough properties in common with such a list can be said with increasing confidence, up to and beyond absolute certainty, to really be the Petersen graph.

A more extensive list of properties that the Petersen graph possesses can be found on here on Mathworld.

When it comes to storing property data for testing purposes we have at least three options. One is to store the properties with the graph in the same file. Another option is to not store the properties but to keep them in the source code of the testing program itself. A third option is to store properties externally and separately from the graph data.

In a later post we will discuss in more detail why the third of these options is preferable. For the purposes of this tutorial our properties will be kept in JSON format files. Such a file representing the above list of properties looks like this.

{
"name": "Petersen Graph",
"n_vertices": 10,
"n_edges": 15,
"diameter": 2,
"girth": 5,
"maxdegree": 3,
}

In the rest of this tutorial we demonstrate how to test a file in the GML format against such a JSON list of properties.

## Testing Graph Properties with CUnit

We are going to write a program in C that takes as input a property file in the above JSON format and a graph in GML format and tests whether the graph possesses the properties in the property file.

We could write a program from scratch but it benefits us to make us of a pre-existing test framework. There are many advantages to using a test framework, the most significant probably being that it greatly enhances code reuse. Another important feature of a test system built on a test framework is that all tests are run, regardless whether they succeed or fail. The testing isn’t halted by a failed test.

There are many test systems for many different languages. We restrict our attention to test systems for C of which one of the most used is CUnit.

There are several steps to creating a test program based on CUnit. In the following subsections we go through this list explaining each step in detail.

1. Get parameter data from the JSON property file.
2. Compute parameters using igraph on the graph data.
3. Express property tests as test functions.
4. Create test suites.
5. Add test suites to a registry
6. Add test functions to suites.
7. Run the tests.

## Get parameter data

There are numerous choices for reading data from a JSON format file. For our purposes the library Jansson suffices.

With Jansson the properties file can be loaded with the json_load_file command

#include <jansson.h>

json_t *json = NULL;
json_error_t error;

json = json_load_file("petersen_properties.json", 0, &error)

After the property file has been loaded we use the json_object_get command to access graph properties and store them in variables to be checked later against computed parameters during the test run.

json_t *vcount_e, *ecount_e, *girth_e, *diameter_e, *maxdegree_e, *radius_e;

void get_parameter_data() {
vcount_e = json_object_get(json, "n_vertices");
ecount_e = json_object_get(json, "n_edges");
diameter_e = json_object_get(json, "diameter");
girth_e = json_object_get(json, "girth");
maxdegree_e = json_object_get(json, "maxdegree");
}

## Compute parameters

The values extracted from the properties file can be thought of as expected parameter values for in the testing scenario (hence the _e suffix). Computed parameters from graph data ought to be in agreement with the expected values.

To compute parameter values from graph data there are several possibilities, including writing our own routines. In this post, however, we will make use of the extensive igraph library.

igraph provides an internal graph data structure, functions for reading the data from a file into memory and functions for computing graph parameters like those we are testing.

To load a graph from a file in GML format use the igraph_read_graph_gml.

#include <igraph.h>

igraph_t g;

i_file = fopen("petersen.gml", "r")
igraph_read_graph_gml(&g, i_file);

Once the graph itself is loaded we can then compute degree and distance parameters using the extensive functionality of igraph.

igraph_real_t radius;
igraph_integer_t diameter, girth, maxdegree;

void compute_all_parameters() {
igraph_maxdegree(&g, &maxdegree, igraph_vss_all(), IGRAPH_ALL, IGRAPH_NO_LOOPS);
igraph_diameter(&g, &diameter, 0, 0, 0, IGRAPH_UNDIRECTED, 1);
igraph_girth(&g, &girth, 0);
}

## Express property tests

The tests themselves are expressed using CUnit’s wide range of assert macros. For our purposes we only need one of those macros, CU_ASSERT_EQUAL which tests for equality of its two arguments.

void test_basic_parameters(void)
{
CU_ASSERT_EQUAL(igraph_vcount(&g), json_integer_value(vcount_e));
CU_ASSERT_EQUAL(igraph_ecount(&g), json_integer_value(ecount_e));
}

void test_degree_parameters(void)
{
CU_ASSERT_EQUAL(maxdegree, json_integer_value(maxdegree_e));
}

void test_distance_parameters(void)
{
CU_ASSERT_EQUAL(diameter, json_integer_value(diameter_e));
CU_ASSERT_EQUAL(girth, json_integer_value(girth_e));
}

## Create test suites

The price we pay for the reusability of testing code is that there is some work to be done to set up the test suites, register them with a test runner and then run them.

The setup or init function is responsible for loading the two files. If the file-handling is successful then graph data can be read and both expected property values and actual parameters can be computed.

int init_suite(void)
{
json_error_t error;
if (NULL == (i_file = fopen("petersen.gml", "r")) ||
(NULL == (json = json_load_file("petersen_properties.json", 0, &error)))) {
return -1;
}

else {
get_parameter_data();
compute_all_parameters();
return 0;
}
}

Every suite also has a teardown or clean function which is called after all of the tests in the suite have finished. This function is used to close files that need to be closed as well to initialise or destroy data structures that have been used in the tests.

int clean_suite(void)
{
if (0 != fclose(i_file)) {
return -1;
}
else {
igraph_destroy(&g);
i_file = NULL;
return 0;
}
}

## Register suites

Once we have created setup and teardown functions for our test suite we must create and add the suite to the registry and give it a name. As suites correspond to graphs in this testing setting we name the suites after the graphs whose properties are under test.

CU_pSuite pSuite = CU_add_suite("Petersen Graph", init_suite, clean_suite);

With a suite created, test functions can now be added. The CU_add_test function requires the suite, a name for the test and the test itself as arguments. The test name is used in reporting the success or failure of tests.

CU_add_test(pSuite, "Basic parameters.", test_basic_parameters)
CU_add_test(pSuite, "Distance parameters.", test_distance_parameters)

## Run tests

CUnit provides several test runners. There are basic, non-interactive runners as well as console and GUI-based interactive ones. On a system with Curses we can make use of an especially nice Curses-based console interface for running and monitoring the tests.

CU_basic_set_mode(CU_BRM_VERBOSE);
CU_curses_run_tests();
CU_cleanup_registry();
return CU_get_error();

When the completed test program is compiled and run the output of a successful run should look something like this: