Data Structures and Algorithms, 2024-5: Labs

Docs

JavaDoc for the labs is hosted here.

Notes

1. Getting Started

Setup

What to do at the start of a lab session (if using lab computers):

• Open the Software Hub and launch:
  • Git for Windows
  • IntelliJ Community Edition
• Open PowerShell and run:
  • If lab 1:
    • N:
    • git clone https://github.com/bertie-wheen/dsa-2024-5 dsa
  • Else (labs 2 onwards):
    • cd N:/dsa
    • git pull
    • If you get an error "fatal: detected dubious ownership in repository ..." (which you likely will):
      • git config --global --add safe.directory '%(prefix)///smbhome.uscs.susx.ac.uk/<username>/dsa' (where <username> is your username)
      • git pull
• Open IntelliJ IDEA and:
  • Open N:/dsa as a project
  • In the menu, select File -> Project Structure (or press Ctrl+Alt+Shift+S)
  • Select Project in the left sidebar (if it isn't already selected)
  • If there is no SDK:
    • Click on the dropdown, and select Download JDK
    • Choose Amazon Corretto as the vendor, and click Download
    • Click Apply, then OK to close the project structure dialog

Structure

These labs are structured into packages, one package per lab (e.g. dsa.lab01 for lab 1). All are subpackages of the top-level dsa package, and each is further structured into subpackages.

Each lab contains an exercises subpackage (e.g. dsa.lab01.exercises) that contains one or more exercises. Each exercise is a partially-implemented class with one or more TODOs that you are expected to fill in in order to complete the implementation.

Model solutions for each of the exercises are provided in the solutions subpackage (e.g. dsa.lab01.solutions).

Some labs also include a base subpackage that contains given code that is needed for the exercises, and thus - though these files aren't exercises and you don't need to edit them - you should have a look through them.

You often don't need to worry too much about their actual code - the most important thing is to look at their type signatures: For example, if it's a class, what you should look for are what methods it has, what parameters they take, what they're supposed to do and what they return.

The most commonly-provided base code are interfaces defining abstract data types (such as in dsa.lab01.base.StringContainer), as exercises will often be to implement various concrete data types as subclasses of these ADTs (e.g. as in dsa.lab01.exercises.StringArray).