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Lab Handout Conway’s Game of Life

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SWEN221: Software Development
Lab Handout
Outline
In this lab you will revisit the implementation of Conway’s Game of Life seen in an earlier lab. The purpose
of this lab is consider the implementation of that program using Java 8 lambda expressions, rather than using
inheritance. You will find that the implementation using lambda’s is more concise and simpler to write. Before
the end of the lab, you should submit your solutions via the online submission system which will automatically
mark it.
Recall that Conway’s Game of Life is a simple cellular simulation devised by John Horton Conway (a British
mathematician) in 1970. The Game of Life has been studied extensively since then for both scientific interest,
and also just for fun. For example, it has been shown that the problem of deciding whether a given state of the
game will ever reach the empty board is undecidable.

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SWEN221: Software Development
Lab Handout
Outline
In this lab you will revisit the implementation of Conway’s Game of Life seen in an earlier lab. The purpose
of this lab is consider the implementation of that program using Java 8 lambda expressions, rather than using
inheritance. You will find that the implementation using lambda’s is more concise and simpler to write. Before
the end of the lab, you should submit your solutions via the online submission system which will automatically
mark it.
Recall that Conway’s Game of Life is a simple cellular simulation devised by John Horton Conway (a British
mathematician) in 1970. The Game of Life has been studied extensively since then for both scientific interest,
and also just for fun. For example, it has been shown that the problem of deciding whether a given state of the
game will ever reach the empty board is undecidable. A simple example is:
Here, cell’s marked in black are “alive” whilst those in white are “dead”. Conway identified four rules for
governing the transition between life and death in the Game of Life:
1. Any live cell with fewer than two live neighbours dies, as if caused by under-population.
2. Any live cell with exactly two live neighbours continues on to the next generation.
3. Any live cell with more than three live neighbours dies, as if by over-population.
4. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.
Here, the neighbours of a cell are the eight surrounding cells (i.e. the four cells at North, South, East, and
West and the four diagonals at NorthWest, NorthEast, etc). See en.wikipedia.org/wiki/Conway’s_
Game_of_Life for more.
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In this lab, we are going to play with an extensible implementation of the Game of Life. The implementation
is extensible because new rules can easily be written to control the game in different ways. The implementation
also supports more complex forms of the Game, as it allows for more than just cells which are “alive” or “dead”.
Instead, each cell is in a state between 0 and 9, where 9 corresponds roughly to “dead” and the other states to
varying forms of “alive”. The meaning of the intermediate states is left up to the rule implementor, but can be
used to simulate age, sickness, happiness, etc.
Getting Started (approx 20 mins)
To get started, download the lambda-conway.jar file from the course website and import this into an
Eclipse project. The Game of Life is provided with a Graphical User Interface, which you can run by rightclicking on GameOfLife and selecting “Run As−→Java Application” (ignore CellDecayGameOfLife
for now). You should see a window pop up like this:
You can play the game by clicking on the window to place cells and the starting the simulation by pressing
“run”. Take a few minutes to play around with the game. You should notice that not all rules are implemented
and we will begin by fixing them in Activity 1.
NOTE: it is useful to take the time and revisit the implementation of Conway using inheritance from an
earlier lab. You will notice that the rules package is no longer required in the implementation for this lab. In
fact, the number of classes required has reduced by four in total.
Activity 1: Conway’s Missing Rules (approx 30mins, worth 50%)
The implementation of the Game of Life provided does not properly implement rules (3) and (4) from above.
The goal of this activity is to complete these rules. The rules themselves are implemented using lambda expressions. For example, Conway’s underpopulation rule is implemented as follows:
1 public static final Rule[] ConwaysOriginalRules = {
2 / / The u n d e r p r o d u c t i o n r u l e
3 (Pair<Point,Board> p) -> neighbours(p) < 2 ? DEAD : null,
4 …
5 };
This implementation of the rule is very easy to compare with the original English description. You will
find this rule in the class GameOfLife and it should provide a useful guide which you can follow. Having
completed rules (3) and (4), you should find that all tests in GameOfLifeTests now pass.
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Activity 2: Cell Decay (approx 30mins, worth 50%)
The goal now is to implement a variation on the basic rules for the Game Of Life which models the age of a
cell. Our updated rules for the game are:
1. Any live cell with fewer than two live neighbours gets older, as if caused by under-population.
2. Any live cell with two live neighbours continues in stasis to the next generation.
3. Any live cell with more than three live neighbours gets older, as if by over-population.
4. Any cell with exactly three live neighbours gets younger, as if by happiness.
The age of a cell is determined by its current state which, if you recall, is a value between 0 . . . 9. Here,
0 is youngest whilst 9 is oldest. We consider that cells of age 9 are dead, whilst all others are varying
forms of alive. At this point, we will now concentrate on using the variation CellDecayGameOfLife
and the accompanying test cases, CellDecayTests. You can run the simulation as a Java Application with
CellDecayGameOfLife, though at this stage you should find that it does nothing.
What to do. Your aim is to implement the above rules for cell decay. You should add any rules you create
to the CellDecayRules array in CellDecayGameOfLife, as was done for the original GameOfLife.
Having done this, all tests in CellDecayTests should now pass.
Having completed this part, you should find that the balance has tipped and cell growth expands quickly.
This is because cells stay “alive” for much longer than before. For example, the system will often produce
something like this:
Submission
Your lab solution should be submitted electronically via the online submission system, linked from the course
homepage. The minimum set of required files is:
swen221/lambdaconway/CellDecayGameOfLife.java
swen221/lambdaconway/GameOfLife.java
swen221/lambdaconway/model/Board.java
swen221/lambdaconway/model/BoardView.java
swen221/lambdaconway/model/Rule.java
swen221/lambdaconway/model/Simulation.java
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swen221/lambdaconway/testing/CellDecayTests.java
swen221/lambdaconway/testing/GameOfLifeTests.java
swen221/lambdaconway/util/Pair.java
swen221/lambdaconway/util/Point.java
You must ensure your submission meets the following requirements (which are needed for the automatic
marking script):
1. Your submission is packaged into a jar file, including the source code. Note, the jar file does not need
to be executable. See the following Eclipse tutorials for more on this:
http://ecs.victoria.ac.nz/Support/TechNoteEclipseTutorials
2. The names of all classes, methods and packages remain unchanged. That is, you may add new classes
and/or new methods and you may modify the body of existing methods. However, you may not change
the name of any existing class, method or package. This is to ensure the automatic marking script can
test your code.
3. All JUnit test files supplied for the assignment remain unchanged. Specifically, you cannot alter the
way in which your code is tested as the marking script relies on this. This does not prohibit you from
adding new tests, as you can still create additional JUnit test files. This is to ensure the automatic marking
script can test your code.
4. You have removed any debugging code that produces output, or otherwise affects the computation.
This ensures the output seen by the automatic marking script does not include spurious information.
Note: Failure to meet these requirements could result in your submission being reject by the submission system
and/or zero marks being awarded.
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