https://github.com/se2p/sbse2024

https://github.com/se2p/sbse2024

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• Host: GitHub
• URL: https://github.com/se2p/sbse2024
• Owner: se2p
• Created: 2024-11-05T12:46:52.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-18T14:50:18.000Z (19 days ago)
• Last Synced: 2024-12-18T15:42:20.267Z (19 days ago)
• Language: Jupyter Notebook
• Size: 15.9 MB
• Stars: 4
• Watchers: 0
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Search-Based Software Engineering Course WS24/25

This repository contains the code examples and content of the lectures. I

will be uploading rendered versions as PDF files to StudIP, and include

rendered Markdown versions in this repository. If you want to run the

notebooks yourself, you will need to install [Jupyter](https://jupyter.org/install).

If you need help with setting up Jupyter, here's a tutorial on [how to install jupyter notebook on your machine](https://www.dataquest.io/blog/jupyter-notebook-tutorial/).

## Chapter 1: Random and Local Search

The first chapter covers the coding examples from the first two weeks, on basic random search and local search algorithms.

[Markdown Export](rendered/Random%20and%20Local%20Search.md)

## Chapter 2: Evolutionary Search (Part 1)

This chapter covers basic evolutionary strategies and genetic algorithms.

[Markdown Export](rendered/Evolutionary%20Search%20-%20Part%201.md)

## Chapter 3: Evolutionary Search (Part 2)

This chapter looks into the various search operators of a genetic algorithm:

Survivor selection, parent selection, crossover, mutation, and the

population itself. We also look at memetic algorithms, which combine

global and local search.

[Markdown Export](rendered/Evolutionary%20Search%20-%20Part%202.md)

## Chapter 4: Multi-Objective Optimisation (Part 1)

This chapter covers the basics of Pareto optimality, NSGA-II, and comparison

of multi-objective search algorithms.

[Markdown Export](rendered/Multi-Objective%20Optimisation%20-%20Part%201.md)

## Chapter 5: Multi-Objective Optimisation (Part 2)

This chapter covers several alternative multi-objective search algorithms:

A random baseline, PAES, SPEA2, TwoArchives, and SMS-EMOA.

[Markdown Export](rendered/Multi-Objective%20Optimisation%20-%20Part%202.md)

## Chapter 6: Search-based Test Generation (Part 1)

This chapter looks at how the problem of test input generation can be cast

as a search problem, and how to automatically instrument programs for

fitness generation.

[Markdown Export](rendered/Search-Based%20Test%20Generation%20-%20Part%201.md)

## Chapter 7: Search-based Test Generation (Part 2)

This chapter continues whole test suite generation, and then moves on to

many objective optimisation for test generation.

[Markdown Export](rendered/Search-Based%20Test%20Generation%20-%20Part%202.md)

## Chapter 8: Parameter Tuning and Parameter Control

This chapter considers how to choose values for the many parameters that we

have introduced in our evolutionary algorithms, how to optimise these

values, and how to adapt them to new problems.

[Markdown Export](rendered/Parameter%20Control%20and%20Adaptation.md)