ChatGPT in Game Testing: Practical Experiences, Use Cases, and Efficiency Gains

Introduction – The author explores whether ChatGPT, a widely discussed AI chatbot, can truly boost game‑testing efficiency and shares personal trial experiences.

ChatGPT Overview – ChatGPT is described as a Transformer‑based natural‑language model capable of text generation, Q&A, summarisation, and dialogue, with potential applications in game testing such as intelligent customer service, task generation, sentiment analysis, and more.

Use Case 1: Code Generation – The author asks ChatGPT to create a Flask image‑upload tool. ChatGPT replies with installation commands and sample code, which the author copies verbatim:

pip install Flask Pillow

from flask import Flask, request, render_template
app = Flask(__name__)
# ... (rest of Flask upload code) ...

Use Case 2: Script Writing – ChatGPT converts a Windows batch script that deletes .svn folders into a Unix/Linux shell script, providing the full conversion:

cd dailybuild/pkgs
for dir in $(find . -type d -name ".svn"); do rm -rf "$dir"; done

Use Case 3: Language Conversion – The author requests a Lua version of a Python script that removes .svn files. ChatGPT returns a complete Lua implementation (code omitted for brevity).

Use Case 4: Code Analysis, Debugging, and Optimisation – Various snippets show ChatGPT analysing C code, adding Chinese comments, identifying bugs, and suggesting optimisations for a LeetCode path‑finding solution.

Use Case 5: Test‑Case Generation – For a game’s Christmas‑tree activity, ChatGPT proposes test points, though the author notes some missing cases and iterates to improve coverage.

Use Case 6: Text Expansion – ChatGPT expands a simple sentence about good weather into a richer paragraph.

Use Case 7: Daily Problem Solving – The author asks for Linux folder‑size commands and Python timestamp conversion; ChatGPT supplies the appropriate du commands and datetime code.

Use Case 8: Database Design – ChatGPT designs a student‑class‑score schema with three tables (students, classes, scores) and generates sample data and full SQL statements.

Comparison: ChatGPT vs. New Bing – The article contrasts ChatGPT’s creativity and conversational abilities with New Bing’s real‑time search integration, noting strengths and weaknesses of each.

Safety and Privacy – Advice is given to avoid feeding confidential or politically sensitive information to ChatGPT.

Language Differences – The author observes that ChatGPT’s English responses are generally faster and more accurate than Chinese ones due to training data composition.

Conclusion – AI tools like ChatGPT are becoming integral to work processes; embracing them can prepare teams for the upcoming AI‑driven era.