Add reviewing exercise text

Author: MakisH

04_documentation/reviewing_exercise.md

@@ -0,0 +1,86 @@
+# Reviewing code
+
+Let us get familiar with code review tools and practices.
+
+Deadline: **December 17, 2025, 9:00**
+
+**Note:** This is the first time that we have this exercise. Feedback and suggestions is very welcome - [open an issue](https://github.com/Simulation-Software-Engineering/Lecture-Material/issues).
+
+## Overview
+
+In this exercise, you will work responsibly with AI tools to write and review code.
+More specifically, you will:
+
+1. use [Responsible AI](https://rai.uni-stuttgart.de) to write a simulation program,
+2. create a repository and publish the first implementation as a pull request to your main branch,
+3. review the code as if it was written by another human,
+4. use [GitHub copilot](https://docs.github.com/en/copilot) to conduct an automated review,
+5. compare the two reviews to each other and to examples from your challenge project.
+
+In the end, you will need to open an issue in the [exercise repository](https://github.com/Simulation-Software-Engineering/reviewing-exercise) with name `[username] Reviewing exercise submission`.
+
+## Generate code
+
+Generating code can be very useful to kick-start a project or to fill rather clear code parts (such as basic tests or documentation).
+With the right prompts and some iterations, one can create functional programs.
+
+**Important note:** Always check for mistakes and check everything you do not understand with the respective documentation.
+You are entering dangerous territory: AI tools have particular strengths, but also weaknesses, and they will very often confidently give wrong answers (and code with bugs), which might not be immediately visibly wrong.
+You remain in control and responsible for your projects.
+
+Now that we have set some guidelines, let's see the strengths in action:
+
+1. Login to the [Responsible AI](https://rai.uni-stuttgart.de) provided by the University of Stuttgart.
+2. In your own words, ask the tool to generate code for solving a heat equation, in a programming language you understand (e.g., Python).
+   Ask it to take parameters from a configuration file and write results to an output file.
+   Ask for some documentation, tests, or anything else you would like to see in such a code.
+3. Create a repository on GitHub (under your namespace) and initialize it with a `README.md` file.
+4. Add the files that RAI created to the repository, in a branch called `feature`. In your commit message, clearly disclose the tool. For example:
+
+   ```text
+    I acknowledge the use of RAI (https://rai.uni-stuttgart.de/) to implement the entirety of this code.
+
+    Promts:
+    1. I would like a Python script that solves the heat equation.
+    2. ...
+   ```
+
+   The more details you add, the better.
+
+5. Open a merge request from the branch `feature` to the `main` branch.
+
+## Review the code
+
+Assume that this code was written by another human being. Be kind and thorough, and leave some comments in the pull request. Make sure to:
+
+- Describe what you understand this PR does.
+- Does the code run? Does it do the right thing?
+- Mention what you like about it
+- Add [inline comments and at least one inline code suggestion](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/commenting-on-a-pull-request)
+- Question parts that do not make sense to you - ask for clarifications. Are the "why" and "how" clear?
+- Could parts of the code be made simpler?
+- Comment on individual aspects such as documentation, testing, design, corner cases, and potential bugs. See the [Google Reviewer Guidelines](https://google.github.io/eng-practices/review/reviewer/) for some ideas.
+
+Your goal with any code review is to help the author improve the contribution to a state that can be accepted into the project.
+
+Start an issue in the [exercise repository](https://github.com/Simulation-Software-Engineering/reviewing-exercise) and add a link to your repository and this PR. Give a summary: what did the AI tool do nicely, where did it fail?
+
+## Get an automated review
+
+GitHub Copilot can automatically review a pull request. Add it as a reviewer and wait for it to submit a review.
+
+Carefully go through the review. Apply comments you agree with, comment on the points you don't agree with.
+
+Optional: Give [custom instructions to Copilot](https://docs.github.com/en/copilot/tutorials/customization-library/custom-instructions) (such as [code reviewer](https://docs.github.com/en/copilot/tutorials/customization-library/custom-instructions/code-reviewer)).
+
+In the issue you just opened, edit the description and comment:
+
+- Comparing the two reviews, what did Copilot focus on, what did you focus on?
+- What did you learn from the suggestions?
+
+## Examples from your Challenge project
+
+In the issue you just opened, edit the description and:
+
+- Add 1-3 examples of pull/merge requests with extensive review.
+- Comment: What do reviewers seem to typically focus on?

timetable.md

@@ -89,4 +89,4 @@
 
 ## 9.2 - Wed, December 10, 2025
 
-- **90** min.: [Exercise: Reviewing with AI tools](https://github.com/Simulation-Software-Engineering/reviewing-exercise)
+- **90** min.: [Exercise: Reviewing with AI tools](https://github.com/Simulation-Software-Engineering/Lecture-Material/blob/main/04_documentation/reviewing_exercise.md)