Merge branch 'main' of https://github.com/s-ccs/summerschool_simtech_2023

material/2_tue/testing/slides.md

@@ -34,11 +34,11 @@ slideOptions:
 
 # Learning Goals
 
-- Justify the effort of developing tests to some extent
 - Get to know a few common terms of testing
 - Work with the Julia unit testing package `Test.jl`
 
-Material is taken and modified, on the one hand, from the [SSE lecture](https://github.com/Simulation-Software-Engineering/Lecture-Material), which builds partly on the [py-rse book](https://merely-useful.tech/py-rse), and, on the other hand, from the [Test.jl docs](https://docs.julialang.org/en/v1/stdlib/Test/).
+Material is taken and modified from the [SSE lecture](https://github.com/Simulation-Software-Engineering/Lecture-Material), which builds partly on the [py-rse book](https://merely-useful.tech/py-rse), and from the [Test.jl docs](https://docs.julialang.org/en/v1/stdlib/Test/).
+
 
 ---
 
@@ -48,30 +48,21 @@ Material is taken and modified, on the one hand, from the [SSE lecture](https://
 
 ## What is Testing?
 
-- Smelling old milk before using it!
+- Smelling old milk before using it
-- A way to determine if a software is not producing reliable results and if so, what is the reason.
+- A way to determine if a software is not producing reliable results and if so, what is the reason
-- Manual testing vs. automated testing.
+- Manual testing vs. automated testing
 
 ---
 
 ## Why Should you Test your Software?
 
-- Improve software reliability and reproducibility.
+- Improve software reliability and reproducibility
-- Make sure that changes (bugfixes, new features) do not affect other parts of software.
+- Make sure that changes (bugfixes, new features) do not affect other parts of software
 - Generally all software is better off being tested regularly. Possible exceptions are very small codes with single users.
 - Ensure that a released version of a software actually works.
 
 ---
 
-## Nomenclature in Software Testing
-
-- **Fixture**: preparatory set for testing.
-- **Actual result**: what the code produces when given the fixture.
-- **Expected result**: what the actual result is compared to.
-- **Test coverage**: how much of the code do tests touch in one run.
-
----
-
 ## Some Ways to Test Software
 
 - Assertions
@@ -83,29 +74,27 @@ Material is taken and modified, on the one hand, from the [SSE lecture](https://
 
 ## Assertions
 
-- Principle of *defensive programming*.
+```julia
+@assert condition "message"
+```
+
+- Principle of *defensive programming*
 - Nothing happens when an assertion is true; throws error when false.
 - Types of assertion statements:
     - Precondition
     - Postcondition
     - Invariant
-- A basic but powerful tool to test a software on-the-go.
+- A basic but powerful tool to test a software on-the-go
-- Assertion statement syntax in Python
-
-```julia
-@assert condition "message"
-```
 
 ---
 
 ## Unit Testing
 
-- Catching errors with assertions is good but preventing them is better!
+- Catching errors with assertions is good but preventing them is better.
 - A *unit* is a single function in one situation.
     - A situation is one amongst many possible variations of input parameters.
-- User creates the expected result manually.
+- User creates the **expected result** manually.
-- Fixture is the set of inputs used to generate an actual result.
+- **Actual result** is compared to the expected result by `@test`.
-- Actual result is compared to the expected result by `@test`.
 
 ---
 
@@ -113,82 +102,26 @@ Material is taken and modified, on the one hand, from the [SSE lecture](https://
 
 - Test whether several units work in conjunction.
 - *Integrate* units and test them together in an *integration* test.
-- Often more complicated than a unit test and has more test coverage.
+- Often more complicated than a unit test and gives higher test coverage.
-- A fixture is used to generate an actual result.
-- Actual result is compared to the expected result by `@test`.
 
 ---
 
 ## Regression Testing
 
 - Generating an expected result is not possible in some situations.
-- Compare the current actual result with a previous actual result.
+- Compare the *current* actual result with a *previous* actual result.
 - No guarantee that the current actual result is correct.
 - Risk of a bug being carried over indefinitely.
 - Main purpose is to identify changes in the current state of the code with respect to a past state.
 
 ---
 
-## Test Coverage
-
-- Coverage is the amount of code a test touches in one run.
-- Aim for high test coverage.
-- There is a trade-off: high test coverage vs. effort in test development
-
----
-
-## Comparing Floating-point Variables
-
-- Very often quantities in math software are `float` / `double`.
-- Such quantities cannot be compared to exact values, an approximation is necessary.
-- Comparison of floating point variables needs to be done to a certain tolerance.
-
-```julia
-@test 1 ≈ 0.999999  rtol=1e-5
-```
-
-- Get `≈` by Latex `\approx` + TAB
-
----
-
-## Test-driven Development (TDD)
-
-- Principle is to write a test and then write a code to fulfill the test.
-- Advantages:
-    - In the end user ends up with a test alongside the code.
-    - Eliminates confirmation bias of the user.
-    - Writing tests gives clarity on what the code is supposed to do.
-- Disadvantage: known to not improve productivity.
-
----
-
-## Checking-driven Development (CDD)
-
-- Developer performs spot checks; sanity checks at intermediate stages
-- Math software often has heuristics which are easy to determine.
-- Keep performing same checks at different stages of development to ensure the code works.
-
----
-
-## Verifying a Test
-
-- Test written as part of a bug-fix:
-    - Reproduce the bug in the test by ensuring that the test fails.
-    - Fix the bug.
-    - Rerun the test to ensure that it passes.
-- Test written to increase code coverage:
-    - Make sure that the first iteration of the test passes.
-    - Try introducing a small fixable bug in the code to verify if the test fails.
-
----
-
 # 2. Unit Testing in Julia with Test.jl
 
 ---
 
-## Setup of Tests.jl
+## Setup of Test.jl
 
-- Standard library to write and manage tests, `using Test`
 - Standardized folder structure:
 
   ```
@@ -204,18 +137,9 @@ Material is taken and modified, on the one hand, from the [SSE lecture](https://
 
 - Singular `test` vs plural `runtests.jl`
 - `setup.jl` for all `using XYZ` statements, included in `runtests.jl`
-- Additional packages either in `[extra] section` of `./Project.toml` or in a new `./test/Project.toml` environment
+- Additional packages in `[extra] section` of `./Project.toml` or in new `./test/Project.toml`
   - In case of the latter: Do not add the package itself to the `./test/Project.toml`
-
+- Run: `]test` when root project is activated
-
----
-
-## Run Tests
-
-Various options:
-
-- Directly call `runtests.jl` TODO?
-- From Pkg-Manager `]test` when root project is activated
 
 ---
 
@@ -234,7 +158,7 @@ Various options:
 - `@testset`: Structure tests
 
    ```julia
-   julia> @testset "trigonometric identities" begin
+   @testset "trigonometric identities" begin
        θ = 2/3*π
        @test sin(-θ) ≈ -sin(θ)
        @test cos(-θ) ≈ cos(θ)
@@ -251,6 +175,8 @@ Various options:
 - [HiRSE-Summer of Testing Part 2b: "Testing with Julia" by Nils Niggemann](https://www.youtube.com/watch?v=gSMKNbZOpZU)
 - [Official documentation of Test.jl](https://docs.julialang.org/en/v1/stdlib/Test/)
 
+---
+
 # 3. Test.jl Demo
 
 We use [`MyTestPackage`](https://github.com/s-ccs/summerschool_simtech_2023/tree/main/material/2_tue/testing/MyTestPackage), which looks as follows:
@@ -274,7 +200,7 @@ We use [`MyTestPackage`](https://github.com/s-ccs/summerschool_simtech_2023/tree
 - Look at `MyTestPackage.jl` and `find.jl`: We have two functions `find_max` and `find_mean`, which calculate the maximum and mean of all elements of a `::AbstractVector`.
   - Assertions were added to check for `NaN` values
 - Look at `runtests.jl`:
-  - TODO: Why do we need `using MyTestPackage`?
+  - Why do we need `using MyTestPackage`?
   - We include dependencies via `setup.jl`: `Test` and `StableRNG`.
   - Testset "find"
 - Look at `find.jl`

material/3_wed/regression/Code_Snippets.jl

@@ -1,34 +1,30 @@
-############################################################################
-#### Execute code chunks separately in VSCODE by pressing 'Alt + Enter' ####
-############################################################################
-
 using Statistics
 using Plots
 using RDatasets
 using GLM
 
-##
+#---
 
 trees = dataset("datasets", "trees")
 
 scatter(trees.Girth, trees.Volume,
         legend=false, xlabel="Girth", ylabel="Volume")
 
-##
+#---
 
 scatter(trees.Girth, trees.Volume,
         legend=false, xlabel="Girth", ylabel="Volume")
 plot!(x -> -37 + 5*x)
 
-##
+#---
 
 linmod1 = lm(@formula(Volume ~ Girth), trees)
 
-##
+#---
 
 linmod2 = lm(@formula(Volume ~ Girth + Height), trees)
 
-##
+#---
 
 r2(linmod1)
 r2(linmod2)
@@ -37,7 +33,7 @@ linmod3 = lm(@formula(Volume ~ Girth + Height + Girth*Height), trees)
 
 r2(linmod3)
 
-##
+#---
 
 using CSV
 using HTTP
@@ -47,6 +43,6 @@ SwissLabor = DataFrame(CSV.File(http_response.body))
 
 SwissLabor[!,"participation"] .= (SwissLabor.participation .== "yes")
 
-##
+#---
 
 model = glm(@formula(participation ~ age), SwissLabor, Binomial(), ProbitLink())

material/3_wed/regression/MultipleRegressionBasics.qmd

@@ -10,7 +10,7 @@ editor:
 
 ### Introductory Example: tree dataset from R
 
-```{julia}
+``` julia
 using Statistics
 using Plots
 using RDatasets
@@ -25,7 +25,7 @@ scatter(trees.Volume, trees.Girth,
 the *explanatory variable/covariate* `girth`? Can we predict the volume
 of a tree given its girth?
 
-```{julia}
+``` julia
 scatter(trees.Girth, trees.Volume,
         legend=false, xlabel="Girth", ylabel="Volume")
 plot!(x -> -37 + 5*x)
@@ -68,7 +68,7 @@ rather use Julia to solve the problem.
 \[use Julia code (existing package) to perform linear regression for
 `volume ~ girth`\]
 
-```{julia}
+``` julia
 lm(@formula(Volume ~ Girth), trees)
 ```
 
@@ -183,7 +183,7 @@ the corresponding standard errors and the $t$-statistics. Test your
 functions with the \`\`\`tree''' data set and try to reproduce the
 output above.
 
-```{julia}
+``` julia
 r2(linmod1)
 r2(linmod2)
 
@@ -232,21 +232,22 @@ $$
 
 For the models above, these are:
 
-+--------------+---------------------+--------------------+
++---------------+-------------------+------------------+
-| Type of Data | Distribution Family | Link Function      |
+| Type of Data  | Distribution      | Link Function    |
-+==============+=====================+====================+
+|               | Family            |                  |
++===============+===================+==================+
 | continuous    | Normal            | identity:        |
 |               |                   |                  |
 |               |                   | $$               |
 |               |                   | g(x)=x           |
 |               |                   | $$               |
-+--------------+---------------------+--------------------+
++---------------+-------------------+------------------+
 | count         | Poisson           | log:             |
 |               |                   |                  |
 |               |                   | $$               |
 |               |                   |  g(x) = \log(x)  |
 |               |                   | $$               |
-+--------------+---------------------+--------------------+
++---------------+-------------------+------------------+
 | binary        | Bernoulli         | logit:           |
 |               |                   |                  |
 |               |                   | $$               |
@@ -254,9 +255,10 @@ For the models above, these are:
 |               |                   | (                |
 |               |                   | \                |
 |               |                   | f                |
-|              |                     | rac{x}{1-x}\right) |
+|               |                   | ra               |
+|               |                   | c{x}{1-x}\right) |
 |               |                   | $$               |
-+--------------+---------------------+--------------------+
++---------------+-------------------+------------------+
 
 In general, the parameter vector $\beta$ is estimated via maximizing the
 likelihood, i.e.,
@@ -274,7 +276,7 @@ $$
 In the Gaussian case, the maximum likelihood estimator is identical to
 the least squares estimator considered above.
 
-```{julia}
+``` julia
 using CSV
 using HTTP