covtracer
Tools for contextualizing tests, built using covr test traces. This
package provides utilities for linking an assortment of test and package
information to paint a more complete picture of how a test was
performed.
flowchart LR
tests[Tests] <--> traces[Traced Exprs] <--> code[Package Code] <--> docs[Package Documentation]Installation
To install, use remotes to install directly from
GitHub
Functionality hinges heavily on coverage objects prepared using covr
(≥ 3.5.1.9003). To ensure suggested dependency requirements are met,
install with dependencies = TRUE (satisfy all dependencies).
# will install covr >= v3.5.1.9003 for examples
remotes::install_github("Genentech/covtracer", dependencies = TRUE)Motivation
Tests are not black boxes. When it comes to verifying behaviors of code, we can use observations about the code that is executed by a test to build a more complete picture of exactly what the test does. This is a core part of software validation. By combining information about each test, the tested package code and linking that code to package documentation, we can link documented behaviors to their respective tests.
Getting Started
Test traces are connected to evaluated code using covr (≥ 3.5.1.9003).
Likewise, a new option flag (covr.record_tests) must be set in order
to record tests alongside the coverage traces. Finally, the package to
evaluate must be installed with source references in order to map all
the components together.
That’s a lot to configure, but if you’re in a position where this test data is valuable hopefully it’s worth the setup.
library(covtracer)
# additional demo packages
library(dplyr)
library(withr)
library(covr)
withr::with_temp_libpaths({
pkg <- system.file("examplepkg", package = "covtracer")
install.packages(
pkg,
type = "source",
repos = NULL,
quiet = TRUE,
INSTALL_opts = c("--with-keep.source", "--install-tests")
)
options(covr.record_tests = TRUE)
cov <- covr::package_coverage(pkg)
ttdf <- test_trace_df(cov)
})There’s a lot of info in the resulting data.frame, but we’ll focus on
just the critical piece, showing which tests evaluate code related to
which documented behaviors. Below we show how one might map unit tests
to evaluated, documented objects.
Note: Below we ignore documentation for datasets and S4 class constructors. Although these are defined in the package, they don’t map to testable lines of code because they are constructed when the package is built.
traceability_matrix <- ttdf %>%
filter(!doctype %in% c("data", "class")) %>% # ignore objects without testable code
select(test_name, file) %>%
filter(!duplicated(.)) %>%
arrange(file)
traceability_matrix
#> test_name file
#> 1 Example R6 Person class public methods are traced Accumulator.Rd
#> 2 S4Example increment generic method works Person.Rd
#> 3 Example R6 Person class public methods are traced Person.Rd
#> 4 S4Example increment generic method works Rando.Rd
#> 5 <NA> Rando.Rd
#> 6 <NA> adder.Rd
#> 7 S4Example increment generic method works adder.Rd
#> 8 S4Example increment generic method works complex_call_stack.Rd
#> 9 S4Example increment generic method works deeper_nested_function.Rd
#> 10 S4Example increment generic method works hypotenuse.Rd
#> 11 S4Example increment generic method works increment.Rd
#> 12 S4Example names method works names-S4Example-method.Rd
#> 13 <NA> names-S4Example2-method.Rd
#> 14 S4Example increment generic method works nested_function.Rd
#> 15 <NA> rd_sampler.Rd
#> 16 S4Example increment generic method works recursive_function.Rd
#> 17 <NA> reexport_example.Rd
#> 18 <NA> reexports.Rd
#> 19 S4Example increment generic method works s3_example_func.Rd
#> 20 S4 Generic Call: show(<myS4Example>) show-S4Example-method.Rd
#> 21 <NA> <NA>We can quickly see which functions or methods are entirely untested.
Use Cases
The data.frame returned by test_trace_df contains a ton of
information, and we can measure a few dimensions of the quality of tests
with some relatively straightforward analysis.
Traceability Matrix
Perhaps the most immediate use case is to map unit tests to documented behaviors.
ttdf %>%
filter(!doctype %in% c("data", "class")) %>% # ignore objects without testable code
select(test_name, file) %>%
filter(!duplicated(.)) %>%
arrange(file)
#> test_name file
#> 1 Example R6 Person class public methods are traced Accumulator.Rd
#> 2 S4Example increment generic method works Person.Rd
#> 3 Example R6 Person class public methods are traced Person.Rd
#> 4 S4Example increment generic method works Rando.Rd
#> 5 <NA> Rando.Rd
#> 6 <NA> adder.Rd
#> 7 S4Example increment generic method works adder.Rd
#> 8 S4Example increment generic method works complex_call_stack.Rd
#> 9 S4Example increment generic method works deeper_nested_function.Rd
#> 10 S4Example increment generic method works hypotenuse.Rd
#> 11 S4Example increment generic method works increment.Rd
#> 12 S4Example names method works names-S4Example-method.Rd
#> 13 <NA> names-S4Example2-method.Rd
#> 14 S4Example increment generic method works nested_function.Rd
#> 15 <NA> rd_sampler.Rd
#> 16 S4Example increment generic method works recursive_function.Rd
#> 17 <NA> reexport_example.Rd
#> 18 <NA> reexports.Rd
#> 19 S4Example increment generic method works s3_example_func.Rd
#> 20 S4 Generic Call: show(<myS4Example>) show-S4Example-method.Rd
#> 21 <NA> <NA>Finding Untested Behaviors
Once we can map unit testing to documentation, we can filter down to only documentation that is not covered by any test.
ttdf %>%
filter(!doctype %in% c("data", "class")) %>% # ignore objects without testable code
select(test_name, count, alias, file) %>%
filter(is.na(count)) %>%
arrange(alias)
#> test_name count alias file
#> 1 <NA> NA Rando Rando.Rd
#> 2 <NA> NA adder adder.Rd
#> 3 <NA> NA help reexports.Rd
#> 4 <NA> NA names,S4Example2-method names-S4Example2-method.Rd
#> 5 <NA> NA person <NA>
#> 6 <NA> NA rd_sampler rd_sampler.Rd
#> 7 <NA> NA reexport_example reexport_example.Rd
#> 8 <NA> NA reexports reexports.RdFilter For Only Directly Tested Behaviors
Some tests evaluate a broad set of functionality by calling functions that themselves call out to internal package functions. This is often perfectly fine, since the mechanisms of calling those internal functions are limited by the surfaced user-facing functions. Nevertheless, whether a function is called directly is a good indication of the “unit”-ness of a unit test. You may consider only the coverage of directly tested functions.
ttdf %>%
filter(!doctype %in% c("data", "class")) %>% # ignore objects without testable code
select(direct, alias) %>%
group_by(alias) %>%
summarize(any_direct_tests = any(direct, na.rm = TRUE)) %>%
arrange(alias)
#> # A tibble: 21 × 2
#> alias any_direct_tests
#> <chr> <lgl>
#> 1 Accumulator TRUE
#> 2 Person TRUE
#> 3 Rando TRUE
#> 4 adder TRUE
#> 5 complex_call_stack TRUE
#> # ℹ 16 more rows