1. 自动生成测试用例
目标
训练一个大语言模型，能够根据需求自动生成测试用例。这通常包括生成不同功能的测试场景、输入数据、预期结果等。
训练过程
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数据准备：
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模型选择：
- 可以选择GPT、T5、BERT等预训练大模型，进行微调。
- 我们将选择GPT系列模型（如GPT-2或GPT-3）进行微调。GPT非常擅长生成文本内容。
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模型微调：
- 微调是指在一个预训练模型的基础上，使用你的特定数据进行进一步的训练。
- 这里，我们使用Hugging Face的
transformers库和datasets库来微调GPT模型。
代码示范
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安装依赖：
pip install transformers datasets torch -
数据准备：
假设我们已经收集了一个简单的测试用例数据集，数据格式如下：{"test_cases": [{"description": "Login functionality","steps": ["Go to the login page","Enter a valid username and password","Click the login button"],"expected_result": "User is redirected to the homepage."},{"description": "Sign-up functionality","steps": ["Go to the sign-up page","Enter username, password, and email","Click the sign-up button"],"expected_result": "User is taken to the confirmation page."}] } -
加载和微调模型：
from transformers import GPT2LMHeadModel, GPT2Tokenizer, Trainer, TrainingArguments from datasets import load_dataset# 加载预训练模型和Tokenizer model_name = "gpt2" model = GPT2LMHeadModel.from_pretrained(model_name) tokenizer = GPT2Tokenizer.from_pretrained(model_name)# 数据加载，假设我们将数据保存在 `test_cases.json` 中 dataset = load_dataset("json", data_files="test_cases.json")def preprocess_function(examples):return tokenizer(examples['description'], truncation=True, padding="max_length")dataset = dataset.map(preprocess_function, batched=True)# 微调模型 training_args = TrainingArguments(output_dir="./results",evaluation_strategy="epoch",learning_rate=5e-5,per_device_train_batch_size=2,num_train_epochs=3, )trainer = Trainer(model=model,args=training_args,train_dataset=dataset['train'], )trainer.train() -
input_text = "Generate a test case for a password reset functionality." inputs = tokenizer(input_text, return_tensors="pt")outputs = model.generate(inputs['input_ids'], max_length=200) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
2. 自动写自动化代码
目标
训练一个模型根据用户描述生成自动化代码（如Selenium自动化测试脚本）。
训练过程
-
数据准备：
-
模型选择与微调：
- 可以使用GPT-3或T5进行微调。T5特别适合任务导向的文本生成任务，GPT系列适合生成自然语言。
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代码生成：
- 让模型根据自然语言描述自动生成测试代码。下面是一个简单的例子，描述如何生成一个使用Selenium的自动化代码。
代码示范
from transformers import T5ForConditionalGeneration, T5Tokenizer# 加载预训练模型和Tokenizer
model = T5ForConditionalGeneration.from_pretrained("t5-small")
tokenizer = T5Tokenizer.from_pretrained("t5-small")# 输入的自然语言描述
input_text = "Generate Selenium code to open Google and search for 'AI technology'"# 编码输入
input_ids = tokenizer.encode(input_text, return_tensors="pt")# 生成代码
outputs = model.generate(input_ids, max_length=200)# 解码输出
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_code)
可能的输出
from selenium import webdriver
from selenium.webdriver.common.keys import Keys# Set up the driver
driver = webdriver.Chrome()# Navigate to Google
driver.get("https://www.google.com")# Find the search bar and input search text
search_box = driver.find_element_by_name("q")
search_box.send_keys("AI technology")
search_box.send_keys(Keys.RETURN)# Wait for the results to load
driver.implicitly_wait(10)# Close the browser
driver.quit()
3. 自动生成文本小说
目标
训练一个大模型（如GPT-3或T5）生成连贯且富有创意的文本小说。
训练过程
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数据准备：
- 收集大量小说文本数据。可以使用公开的小说数据集（如Project Gutenberg上的书籍）。
- 数据集的格式应该是按章节或段落分割的小说文本。
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模型选择与微调：
- 使用GPT模型，因为它在文本生成方面表现出色，特别适合创作类任务。
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文本生成：
- 使用微调后的GPT模型，输入一个简短的情节或设定，让模型自动生成小说内容。
代码示范
from transformers import GPT2LMHeadModel, GPT2Tokenizer# 加载预训练模型和Tokenizer
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)# 输入小说情节或设置
input_text = "Write the first chapter of a fantasy novel about a young wizard named Arlo who discovers a hidden magical power."# 编码输入
input_ids = tokenizer.encode(input_text, return_tensors="pt")# 生成小说内容
outputs = model.generate(input_ids, max_length=500, num_return_sequences=1)# 解码输出
generated_novel = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_novel)
输出示例
Chapter 1: The Hidden PowerArlo had always known that he was different. Ever since he was a child, he had felt a strange pull towards the old library at the edge of the village. His friends thought it was just an obsession, but Arlo knew there was something more. Something magical.One evening, while exploring the library’s hidden corners, he stumbled upon an ancient book. Its pages were yellowed with age, and its cover was decorated with strange runes. As soon as he opened the first page, a sudden surge of energy coursed through his body. Arlo stumbled back, shocked by the power that seemed to awaken within him.“This is it,” he whispered, “I have found the power I've been searching for.”But what did it mean? What was this magic, and how would he control it?He closed the book carefully, his heart racing. There was no turning back now. His journey into the unknown had just begun.
