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Develop #4

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87abdd5
- Add unit tests for core game logic (board, game flow, reset, win/draw)
ZePyLaw Dec 12, 2025
22e3d1b
Fixed symbols path (now assets before it was in game)
ZePyLaw Dec 12, 2025
d57a9cc
Fixed end of file precommit
ZePyLaw Jan 9, 2026
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48 changes: 48 additions & 0 deletions .github/workflows/ci.yml
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name: CI

on:
push:
branches: ["**"]
pull_request:

permissions:
contents: read

jobs:
test-and-lint:
runs-on: ubuntu-latest

steps:
- name: Checkout repository
uses: actions/checkout@v4

- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: "1.25.x"
cache: true

- name: Show Go environment
run: go env

- name: Download dependencies
run: go mod download

- name: Check formatting (gofmt)
run: |
test -z "$(gofmt -l .)"

- name: Static analysis (golangci-lint)
uses: golangci/golangci-lint-action@v6
with:
version: latest

- name: Run tests with coverage
run: TESTING=1 go test ./... -count=1 -cover

- name: Run race detector (develop only)
if: github.ref == 'refs/heads/develop'
run: TESTING=1 go test ./... -race -count=1

- name: CI summary
run: echo "CI completed successfully"
19 changes: 19 additions & 0 deletions .golangci.yml
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run:
timeout: 3m
tests: true

linters:
enable:
- govet
- staticcheck
- errcheck
- ineffassign
- unused

linters-settings:
errcheck:
check-type-assertions: true
check-blank: true

issues:
exclude-use-default: false
27 changes: 27 additions & 0 deletions .pre-commit-config.yaml
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repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.6.0
hooks:
- id: end-of-file-fixer
- id: trailing-whitespace
- id: check-yaml

- repo: local
hooks:
- id: gofmt
name: gofmt
entry: gofmt -w
language: system
files: \.go$

- id: govet
name: go vet
entry: go vet ./...
language: system
pass_filenames: false

- id: gotest
name: go test
entry: python -c "import os, subprocess; os.environ['TESTING']='1'; subprocess.check_call(['go','test','./...','-count=1'])"
language: system
pass_filenames: false
14 changes: 7 additions & 7 deletions ai_models/ai.go
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package ai_models
import "GoTicTacToe/game"
type AIModel interface {
NextMove(board *game.Board, me *game.Player, players [2]*game.Player) (x, y int)
}
package ai_models

import "GoTicTacToe/game"

type AIModel interface {
NextMove(board *game.Board, me *game.Player, players [2]*game.Player) (x, y int)
}
130 changes: 65 additions & 65 deletions ai_models/minimax.go
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package ai_models
import "GoTicTacToe/game"
type MinimaxAI struct{}
func (MinimaxAI) NextMove(board *game.Board, me *game.Player, players [2]*game.Player) (int, int) {
bestScore := -9999
bestMove := game.Move{X: -1, Y: -1}
for _, mv := range board.AvailableMoves() {
clone := board.Clone()
clone.Play(me, mv.X, mv.Y)
score := minimax(clone, me, players, false)
if score > bestScore {
bestScore = score
bestMove = mv
}
}
return bestMove.X, bestMove.Y
}
func minimax(board *game.Board, me *game.Player, players [2]*game.Player, maximizing bool) int {
winner := board.CheckWin()
if winner == me {
return +1
}
if winner != nil && winner != me {
return -1
}
if board.CheckDraw() {
return 0
}
if maximizing {
best := -9999
for _, mv := range board.AvailableMoves() {
clone := board.Clone()
clone.Play(me, mv.X, mv.Y)
score := minimax(clone, me, players, false)
if score > best {
best = score
}
}
return best
}
// minimizing (opponent turn)
opp := me.Opponent(players)
best := 9999
for _, mv := range board.AvailableMoves() {
clone := board.Clone()
clone.Play(opp, mv.X, mv.Y)
score := minimax(clone, me, players, true)
if score < best {
best = score
}
}
return best
}
package ai_models

import "GoTicTacToe/game"

type MinimaxAI struct{}

func (MinimaxAI) NextMove(board *game.Board, me *game.Player, players [2]*game.Player) (int, int) {
bestScore := -9999
bestMove := game.Move{X: -1, Y: -1}

for _, mv := range board.AvailableMoves() {
clone := board.Clone()
clone.Play(me, mv.X, mv.Y)

score := minimax(clone, me, players, false)

if score > bestScore {
bestScore = score
bestMove = mv
}
}

return bestMove.X, bestMove.Y
}

func minimax(board *game.Board, me *game.Player, players [2]*game.Player, maximizing bool) int {
winner := board.CheckWin()
if winner == me {
return +1
}
if winner != nil && winner != me {
return -1
}
if board.CheckDraw() {
return 0
}

if maximizing {
best := -9999
for _, mv := range board.AvailableMoves() {
clone := board.Clone()
clone.Play(me, mv.X, mv.Y)
score := minimax(clone, me, players, false)
if score > best {
best = score
}
}
return best
}

// minimizing (opponent turn)
opp := me.Opponent(players)
best := 9999

for _, mv := range board.AvailableMoves() {
clone := board.Clone()
clone.Play(opp, mv.X, mv.Y)
score := minimax(clone, me, players, true)
if score < best {
best = score
}
}

return best
}
56 changes: 56 additions & 0 deletions ai_models/minimax_fuzz_test.go
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package ai_models

import (
"GoTicTacToe/game"
"testing"
)

/*
FuzzMinimaxDoesNotCrash performs fuzz testing on the MinimaxAI algorithm.

The goal of this test is NOT to verify the quality of the moves,
but to ensure that the Minimax implementation:
- never panics
- always terminates
- behaves safely for a range of board sizes

The fuzzing engine generates random board sizes within a constrained range
to explore unexpected execution paths.
*/
func FuzzMinimaxDoesNotCrash(f *testing.F) {

// Seed corpus: start fuzzing with a valid board size
f.Add(3)

// Fuzz function executed with randomly generated inputs
f.Fuzz(func(t *testing.T, size int) {

// Ignore sizes that would produce invalid or unsupported boards
// This keeps the fuzzing focused on realistic game scenarios
if size < 3 || size > 5 {
return
}

// Create a board with the fuzz-generated size
b := game.NewBoard(size, size)

// Create AI player and opponent
p1 := &game.Player{Name: "AI"}
p2 := &game.Player{Name: "Human"}

// Players array required by the AI interface
players := [2]*game.Player{p1, p2}

// Instantiate the Minimax AI model
ai := MinimaxAI{}

// Ask the AI to compute a move
x, y := ai.NextMove(b, p1, players)

// The AI should never return absurd values or crash
// Even in edge cases, coordinates must remain within safe bounds
if x < -1 || y < -1 {
t.Fatalf("invalid move")
}
})
}
40 changes: 40 additions & 0 deletions ai_models/minimax_test.go
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package ai_models

import (
"GoTicTacToe/game"
"testing"
)

/*
TestMinimaxReturnsValidMove verifies that the MinimaxAI model
always returns a valid board coordinate on an empty board.

This test does NOT attempt to validate the optimality of the move
(minimax correctness), which would require complex scenario-based tests.
Instead, it ensures that:
- the algorithm terminates
- no panic occurs
- the returned move is within board bounds
*/
func TestMinimaxReturnsValidMove(t *testing.T) {
// Create a standard empty 3x3 board
b := game.NewBoard(3, 3)

// Create players: one controlled by the AI, one opponent
p1 := &game.Player{Name: "AI"}
p2 := &game.Player{Name: "Human"}

// Players array required by the AI interface
players := [2]*game.Player{p1, p2}

// Instantiate the Minimax AI model
ai := MinimaxAI{}

// Ask the AI to compute the next move
x, y := ai.NextMove(b, p1, players)

// The returned move must be inside the board boundaries
if x < 0 || x >= 3 || y < 0 || y >= 3 {
t.Errorf("invalid move returned: %d,%d", x, y)
}
}
36 changes: 18 additions & 18 deletions ai_models/random.go
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package ai_models
import (
"GoTicTacToe/game"
"math/rand"
)
type RandomAI struct{}
func (RandomAI) NextMove(board *game.Board, me *game.Player, players [2]*game.Player) (int, int) {
moves := board.AvailableMoves()
if len(moves) == 0 {
return -1, -1
}
m := moves[rand.Intn(len(moves))]
return m.X, m.Y
}
package ai_models

import (
"GoTicTacToe/game"
"math/rand"
)

type RandomAI struct{}

func (RandomAI) NextMove(board *game.Board, me *game.Player, players [2]*game.Player) (int, int) {
moves := board.AvailableMoves()
if len(moves) == 0 {
return -1, -1
}

m := moves[rand.Intn(len(moves))]
return m.X, m.Y
}
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