vignettes/how_it_works.Rmd
how_it_works.RmdThe covr package provides a framework for measuring unit test coverage. Unit testing is one of the cornerstones of software development. Any piece of R code can be thought of as a software application with a certain set of behaviors. Unit testing means creating examples of how the code should behave with a definition of the expected output. This could include normal use, edge cases, and expected error cases. Unit testing is commonly facilitated by frameworks such as testthat and RUnit. Test coverage is the proportion of the source code that is executed when running these tests. Code coverage consists of:
Measuring code coverage allows developers to asses their progress in quality checking their own (or their collaborators) code. Measuring code coverage allows code consumers to have confidence in the measures taken by the package authors to verify high code quality. covr provides three functions to calculate test coverage.
package_coverage() performs coverage calculation on an
R package. (Unit tests must be contained in the "tests"
directory.)file_coverage() performs coverage calculation on one or
more R scripts by executing one or more R scripts.function_coverage() performs coverage calculation on a
single named function, using an expression provided.In addition to providing an objective metric of test suite
extensiveness, it is often advantageous for developers to have a code
level view of their unit tests. An interface for visually marking code
with test coverage results allows a clear box view of the unit test
suite. The clear box view can be accessed using online tools or a local
report can be generated using report().
The core function in covr is
trace_calls(). This function was adapted from ideas in Advanced R -
Walking the Abstract Syntax Tree with recursive functions. This
recursive function modifies each of the leaves (atomic or name objects)
of an R expression by applying a given function to them. If the
expression is not a leaf the walker function calls itself recursively on
elements of the expression instead.
We can use this same framework to instead insert a trace statement
before each call by replacing each call with a call to a counting
function followed by the previous call. Braces ({) in R may
seem like language syntax, but they are actually a Primitive function
and you can call them like any other function.
identical(x = { 1 + 2; 3 + 4 },
y = `{`(1 + 2, 3 + 4))## [1] TRUERemembering that braces always return the value of the last evaluated
expression, we can call a counting function followed by the previous
function substituting as.call(recurse(x)) in our function
above with.
Now that we have a way to add a counting function to any call in the
Abstract Syntax Tree without changing the output we need a way to
determine where in the code source that function came from. Luckily R
has a built-in method to provide this information in the form of source
references. When option(keep.source = TRUE) (the default
for interactive sessions), a reference to the source code for functions
is stored along with the function definition. This reference is used to
provide the original formatting and comments for the given function
source. In particular each call in a function contains a
srcref attribute, which can then be used as a key to count
just that call.
The actual source for trace_calls is slightly more
complicated because we want to initialize the counter for each call
while we are walking the Abstract Syntax Tree and there are a few
non-calls we also want to count.
Each statement comes with a source reference. Unfortunately, the following is counted as one statement:
if (x)
y()To work around this, detailed parse data (obtained from a refined
version of getParseData) is analyzed to impute source
references at sub-statement level for if, for,
while and switch constructs.
After we have our modified function definition, how do we re-define the function to use the updated definition, and ensure that all other functions which call the old function also use the new definition? You might try redefining the function directly.
f1 <- function() 1
f1 <- function() 2
f1() == 2## [1] TRUEWhile this does work for the simple case of calling the new function in the same environment, it fails if another function calls a function in a different environment.
env <- new.env()
f1 <- function() 1
env$f2 <- function() f1() + 1
env$f1 <- function() 2
env$f2() == 3## [1] FALSEAs modifying external environments and correctly restoring them can
be tricky to get correct, we use the C function reassign_function,
which is also used in testthat::with_mock. This function
takes a function name, environment, old definition, new definition and
copies the formals, body, attributes and environment from the old
function to the new function. This allows you to do an in-place
replacement of a given function with a new function and ensure that all
references to the old function will use the new definition.
R’s S3 object oriented classes simply define functions directly in the packages namespace, so they can be treated the same as any other function.
S4 methods have a more complicated implementation than S3 classes. The function definitions are placed in an enclosing environment based on the generic method they implement. This makes getting the function definition more complicated.
replacements_S4 first gets all the generic functions for
the package environment. Then for each generic function if finds the
mangled meta package name and gets the corresponding environment from
the base environment. All of the functions within this environment are
then traced.
Test coverage of compiled code uses a completely different mechanism than that of R code. Fortunately we can take advantage of Gcov, the built-in coverage tool for gcc and compatible reports from clang versions 3.5 and greater.
Both of these compilers track execution coverage when given the
--coverage flag. In addition it is necessary to turn off
compiler optimization -O0, otherwise the coverage output is
difficult or impossible to interpret as multiple lines can be optimized
into one, functions can be inlined, etc.
R passes flags defined in PKG_CFLAGS to the compiler,
however it also has default flags including -02 (defined in
$R_HOME/etc/Makeconf), which need to be overridden.
Unfortunately it is not possible to override the default flags with
environment variables (as the new flags are added to the left of the
defaults rather than the right). However if Make variables are defined
in ~/.R/Makevars they are used in place of the
defaults.
Therefore, we need to temporarily add -O0 --coverage to
the Makevars file, then restore the previous state after the coverage is
run.
The last hurdle to getting compiled code coverage working properly is that the coverage output is only produced when the running process ends. Therefore you cannot run the tests and get the results in the same R process. covr runs a separate R process when running tests. However we need to modify the package code first before running the tests.
covr installs the package to be tested in a temporary directory. Next, calls are made to the lazy loading code which installs a user hook to modify the code when it is loaded. We also register a finalizer which prints the coverage counts when the namespace is unloaded or the R process exits. These output files are then aggregated together to determine the coverage.
This procedure works regardless of the number of child R processes used, so therefore also works with parallel code.
The output format returned by covr is an R object of class “coverage” containing the information gathered when executing the test suite. It consists of a named list, where the names are colon-delimited information from the source references (the file, line and columns the traced call is from). The value is the number of times that given expression was called and the source ref of the original call.
# an object to analyze
f1 <- function(x) { x + 1 }
# get results with no unit tests
c1 <- function_coverage(fun = f1, code = NULL)
c1## Coverage: 0.00%## <text>: 0.00%
# get results with unit tests
c2 <- function_coverage(fun = f1, code = f1(x = 1) == 2)
c2## Coverage: 100.00%## <text>: 100.00%An as.data.frame method is available to make subsetting
by various features easy to do.
While covr tracks coverage by expression, typically users expect coverage to be reported by line, so there are functions to convert to line oriented coverage.
Codecov and Coveralls are a web services to help you track your code coverage over time, and ensure that all new code is appropriately covered.
They both have JSON-based APIs to submit and report on coverage. The
functions codecov and coveralls create outputs
that can be consumed by these services.
Prior to writing covr, there were a handful of coverage tools for R code. R-coverage by Karl Forner and testCoverage by Tom Taverner, Chris Campbell & Suchen Jin.
R-coverage provides a very robust solution by modifying the R source code to instrument the code for each call. Unfortunately this requires you to patch the source of the R application itself. Getting the changes incorporated into the core R distribution would likely be challenging.
testCoverage uses getParseData, R’s
alternate parser (from 3.0) to analyse the R source code. The package
replaces symbols in the code to be tested with a unique identifier. This
is then injected into a tracing function that will report each time the
symbol is called. The first symbol at each level of the expression tree
is traced, allowing the coverage of code branches to be checked. This is
a complicated implementation I do not fully understand, which is one of
the reasons I decided to write covr.
covr takes an approach in-between the two previous tools. Function definitions are modified by parsing the abstract syntax tree and inserting trace statements. These modified definitions are then transparently replaced in-place using C. This allows us to correctly instrument every call and function in a package without having to resort to alternate parsing or changes to the R source.
covr provides an accessible framework which will ease the communication of R unit test suites. covr can be integrated with continuous integration services where R developers are working on larger projects, or as part of multi-disciplinary teams. covr aims to be simple to use to make writing high quality code part of every R user’s routine.