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452 lines
26 KiB
Plaintext
452 lines
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Unit test writing using a multi-step prompt (with the older API)\n",
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"\n",
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"Complex tasks, such as writing unit tests, can benefit from multi-step prompts. In contrast to a single prompt, a multi-step prompt generates text from GPT-3 and then feeds that text back into subsequent prompts. This can help in cases where you want GPT-3 to explain its reasoning before answering, or brainstorm a plan before executing it.\n",
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"\n",
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"In this notebook, we use a 3-step prompt to write unit tests in Python using the following steps:\n",
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"\n",
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"1. Given a Python function, we first prompt GPT-3 to explain what the function is doing.\n",
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"2. Second, we prompt GPT-3 to plan a set of unit tests for the function.\n",
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" - If the plan is too short, we ask GPT-3 to elaborate with more ideas for unit tests.\n",
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"3. Finally, we prompt GPT-3 to write the unit tests.\n",
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"\n",
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"The code example illustrates a few optional embellishments on the chained, multi-step prompt:\n",
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"\n",
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"- Conditional branching (e.g., only asking for elaboration if the first plan is too short)\n",
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"- Different models for different steps (e.g., `gpt-3.5-turbo-instruct` for the text planning steps and `gpt-4` for the code writing step)\n",
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"- A check that re-runs the function if the output is unsatisfactory (e.g., if the output code cannot be parsed by Python's `ast` module)\n",
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"- Streaming output so that you can start reading the output before it's fully generated (useful for long, multi-step outputs)\n",
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"\n",
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"The full 3-step prompt looks like this (using as an example `pytest` for the unit test framework and `is_palindrome` as the function):\n",
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"\n",
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" # How to write great unit tests with pytest\n",
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"\n",
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" In this advanced tutorial for experts, we'll use Python 3.9 and `pytest` to write a suite of unit tests to verify the behavior of the following function.\n",
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" ```python\n",
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" def is_palindrome(s):\n",
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" return s == s[::-1]\n",
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" ```\n",
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"\n",
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" Before writing any unit tests, let's review what each element of the function is doing exactly and what the author's intentions may have been.\n",
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" - First,{GENERATED IN STEP 1}\n",
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" \n",
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" A good unit test suite should aim to:\n",
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" - Test the function's behavior for a wide range of possible inputs\n",
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" - Test edge cases that the author may not have foreseen\n",
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" - Take advantage of the features of `pytest` to make the tests easy to write and maintain\n",
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" - Be easy to read and understand, with clean code and descriptive names\n",
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" - Be deterministic, so that the tests always pass or fail in the same way\n",
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"\n",
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" `pytest` has many convenient features that make it easy to write and maintain unit tests. We'll use them to write unit tests for the function above.\n",
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"\n",
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" For this particular function, we'll want our unit tests to handle the following diverse scenarios (and under each scenario, we include a few examples as sub-bullets):\n",
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" -{GENERATED IN STEP 2}\n",
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"\n",
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" [OPTIONALLY APPENDED]In addition to the scenarios above, we'll also want to make sure we don't forget to test rare or unexpected edge cases (and under each edge case, we include a few examples as sub-bullets):\n",
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" -{GENERATED IN STEP 2B}\n",
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"\n",
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" Before going into the individual tests, let's first look at the complete suite of unit tests as a cohesive whole. We've added helpful comments to explain what each line does.\n",
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" ```python\n",
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" import pytest # used for our unit tests\n",
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"\n",
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" def is_palindrome(s):\n",
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" return s == s[::-1]\n",
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"\n",
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" #Below, each test case is represented by a tuple passed to the @pytest.mark.parametrize decorator\n",
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" {GENERATED IN STEP 3}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import ast # used for detecting whether generated Python code is valid\n",
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"import openai\n",
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"\n",
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"# example of a function that uses a multi-step prompt to write unit tests\n",
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"def unit_test_from_function(\n",
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" function_to_test: str, # Python function to test, as a string\n",
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" unit_test_package: str = \"pytest\", # unit testing package; use the name as it appears in the import statement\n",
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" approx_min_cases_to_cover: int = 7, # minimum number of test case categories to cover (approximate)\n",
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" print_text: bool = False, # optionally prints text; helpful for understanding the function & debugging\n",
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" text_model: str = \"gpt-3.5-turbo-instruct\", # model used to generate text plans in steps 1, 2, and 2b\n",
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" code_model: str = \"gpt-3.5-turbo-instruct\", # if you don't have access to code models, you can use text models here instead\n",
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" max_tokens: int = 1000, # can set this high, as generations should be stopped earlier by stop sequences\n",
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" temperature: float = 0.4, # temperature = 0 can sometimes get stuck in repetitive loops, so we use 0.4\n",
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" reruns_if_fail: int = 1, # if the output code cannot be parsed, this will re-run the function up to N times\n",
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") -> str:\n",
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" \"\"\"Outputs a unit test for a given Python function, using a 3-step GPT-3 prompt.\"\"\"\n",
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"\n",
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" # Step 1: Generate an explanation of the function\n",
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"\n",
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" # create a markdown-formatted prompt that asks GPT-3 to complete an explanation of the function, formatted as a bullet list\n",
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" prompt_to_explain_the_function = f\"\"\"# How to write great unit tests with {unit_test_package}\n",
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"\n",
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"In this advanced tutorial for experts, we'll use Python 3.9 and `{unit_test_package}` to write a suite of unit tests to verify the behavior of the following function.\n",
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"```python\n",
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"{function_to_test}\n",
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"```\n",
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"\n",
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"Before writing any unit tests, let's review what each element of the function is doing exactly and what the author's intentions may have been.\n",
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"- First,\"\"\"\n",
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" if print_text:\n",
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" text_color_prefix = \"\\033[30m\" # black; if you read against a dark background \\033[97m is white\n",
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" print(text_color_prefix + prompt_to_explain_the_function, end=\"\") # end='' prevents a newline from being printed\n",
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"\n",
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" # send the prompt to the API, using \\n\\n as a stop sequence to stop at the end of the bullet list\n",
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" explanation_response = openai.Completion.create(\n",
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" model=text_model,\n",
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" prompt=prompt_to_explain_the_function,\n",
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" stop=[\"\\n\\n\", \"\\n\\t\\n\", \"\\n \\n\"],\n",
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" max_tokens=max_tokens,\n",
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" temperature=temperature,\n",
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" stream=True,\n",
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" )\n",
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" explanation_completion = \"\"\n",
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" if print_text:\n",
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" completion_color_prefix = \"\\033[92m\" # green\n",
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" print(completion_color_prefix, end=\"\")\n",
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" for event in explanation_response:\n",
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" event_text = event[\"choices\"][0][\"text\"]\n",
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" explanation_completion += event_text\n",
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" if print_text:\n",
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" print(event_text, end=\"\")\n",
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"\n",
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" # Step 2: Generate a plan to write a unit test\n",
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"\n",
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" # create a markdown-formatted prompt that asks GPT-3 to complete a plan for writing unit tests, formatted as a bullet list\n",
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" prompt_to_explain_a_plan = f\"\"\"\n",
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" \n",
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"A good unit test suite should aim to:\n",
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"- Test the function's behavior for a wide range of possible inputs\n",
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"- Test edge cases that the author may not have foreseen\n",
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"- Take advantage of the features of `{unit_test_package}` to make the tests easy to write and maintain\n",
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"- Be easy to read and understand, with clean code and descriptive names\n",
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"- Be deterministic, so that the tests always pass or fail in the same way\n",
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"\n",
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"`{unit_test_package}` has many convenient features that make it easy to write and maintain unit tests. We'll use them to write unit tests for the function above.\n",
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"\n",
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"For this particular function, we'll want our unit tests to handle the following diverse scenarios (and under each scenario, we include a few examples as sub-bullets):\n",
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"-\"\"\"\n",
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" if print_text:\n",
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" print(text_color_prefix + prompt_to_explain_a_plan, end=\"\")\n",
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"\n",
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" # append this planning prompt to the results from step 1\n",
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" prior_text = prompt_to_explain_the_function + explanation_completion\n",
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" full_plan_prompt = prior_text + prompt_to_explain_a_plan\n",
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"\n",
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" # send the prompt to the API, using \\n\\n as a stop sequence to stop at the end of the bullet list\n",
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" plan_response = openai.Completion.create(\n",
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" model=text_model,\n",
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" prompt=full_plan_prompt,\n",
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" stop=[\"\\n\\n\", \"\\n\\t\\n\", \"\\n \\n\"],\n",
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" max_tokens=max_tokens,\n",
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" temperature=temperature,\n",
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" stream=True,\n",
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" )\n",
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" plan_completion = \"\"\n",
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" if print_text:\n",
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" print(completion_color_prefix, end=\"\")\n",
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" for event in plan_response:\n",
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" event_text = event[\"choices\"][0][\"text\"]\n",
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" plan_completion += event_text\n",
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" if print_text:\n",
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" print(event_text, end=\"\")\n",
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"\n",
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" # Step 2b: If the plan is short, ask GPT-3 to elaborate further\n",
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" # this counts top-level bullets (e.g., categories), but not sub-bullets (e.g., test cases)\n",
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" elaboration_needed = plan_completion.count(\"\\n-\") +1 < approx_min_cases_to_cover # adds 1 because the first bullet is not counted\n",
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" if elaboration_needed:\n",
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" prompt_to_elaborate_on_the_plan = f\"\"\"\n",
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"\n",
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"In addition to the scenarios above, we'll also want to make sure we don't forget to test rare or unexpected edge cases (and under each edge case, we include a few examples as sub-bullets):\n",
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"-\"\"\"\n",
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" if print_text:\n",
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" print(text_color_prefix + prompt_to_elaborate_on_the_plan, end=\"\")\n",
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"\n",
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" # append this elaboration prompt to the results from step 2\n",
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" prior_text = full_plan_prompt + plan_completion\n",
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" full_elaboration_prompt = prior_text + prompt_to_elaborate_on_the_plan\n",
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"\n",
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" # send the prompt to the API, using \\n\\n as a stop sequence to stop at the end of the bullet list\n",
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" elaboration_response = openai.Completion.create(\n",
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" model=text_model,\n",
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" prompt=full_elaboration_prompt,\n",
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" stop=[\"\\n\\n\", \"\\n\\t\\n\", \"\\n \\n\"],\n",
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" max_tokens=max_tokens,\n",
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" temperature=temperature,\n",
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" stream=True,\n",
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" )\n",
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" elaboration_completion = \"\"\n",
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" if print_text:\n",
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" print(completion_color_prefix, end=\"\")\n",
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" for event in elaboration_response:\n",
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" event_text = event[\"choices\"][0][\"text\"]\n",
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" elaboration_completion += event_text\n",
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" if print_text:\n",
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" print(event_text, end=\"\")\n",
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"\n",
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" # Step 3: Generate the unit test\n",
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"\n",
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" # create a markdown-formatted prompt that asks GPT-3 to complete a unit test\n",
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" starter_comment = \"\"\n",
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" if unit_test_package == \"pytest\":\n",
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" starter_comment = \"Below, each test case is represented by a tuple passed to the @pytest.mark.parametrize decorator\"\n",
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" prompt_to_generate_the_unit_test = f\"\"\"\n",
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"\n",
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"Before going into the individual tests, let's first look at the complete suite of unit tests as a cohesive whole. We've added helpful comments to explain what each line does.\n",
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"```python\n",
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"import {unit_test_package} # used for our unit tests\n",
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"\n",
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"{function_to_test}\n",
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"\n",
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"#{starter_comment}\"\"\"\n",
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" if print_text:\n",
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" print(text_color_prefix + prompt_to_generate_the_unit_test, end=\"\")\n",
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"\n",
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" # append this unit test prompt to the results from step 3\n",
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" if elaboration_needed:\n",
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" prior_text = full_elaboration_prompt + elaboration_completion\n",
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" else:\n",
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" prior_text = full_plan_prompt + plan_completion\n",
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" full_unit_test_prompt = prior_text + prompt_to_generate_the_unit_test\n",
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"\n",
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" # send the prompt to the API, using ``` as a stop sequence to stop at the end of the code block\n",
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" unit_test_response = openai.Completion.create(\n",
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" model=code_model,\n",
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" prompt=full_unit_test_prompt,\n",
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" stop=\"```\",\n",
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" max_tokens=max_tokens,\n",
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" temperature=temperature,\n",
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" stream=True\n",
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" )\n",
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" unit_test_completion = \"\"\n",
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" if print_text:\n",
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" print(completion_color_prefix, end=\"\")\n",
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" for event in unit_test_response:\n",
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" event_text = event[\"choices\"][0][\"text\"]\n",
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" unit_test_completion += event_text\n",
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" if print_text:\n",
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" print(event_text, end=\"\")\n",
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"\n",
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" # check the output for errors\n",
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" code_start_index = prompt_to_generate_the_unit_test.find(\"```python\\n\") + len(\"```python\\n\")\n",
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" code_output = prompt_to_generate_the_unit_test[code_start_index:] + unit_test_completion\n",
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" try:\n",
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" ast.parse(code_output)\n",
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" except SyntaxError as e:\n",
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" print(f\"Syntax error in generated code: {e}\")\n",
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" if reruns_if_fail > 0:\n",
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" print(\"Rerunning...\")\n",
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" return unit_test_from_function(\n",
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" function_to_test=function_to_test,\n",
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" unit_test_package=unit_test_package,\n",
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" approx_min_cases_to_cover=approx_min_cases_to_cover,\n",
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" print_text=print_text,\n",
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" text_model=text_model,\n",
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" code_model=code_model,\n",
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" max_tokens=max_tokens,\n",
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" temperature=temperature,\n",
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" reruns_if_fail=reruns_if_fail-1, # decrement rerun counter when calling again\n",
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" )\n",
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"\n",
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" # return the unit test as a string\n",
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" return unit_test_completion\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[30m# How to write great unit tests with pytest\n",
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"\n",
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"In this advanced tutorial for experts, we'll use Python 3.9 and `pytest` to write a suite of unit tests to verify the behavior of the following function.\n",
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"```python\n",
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"def is_palindrome(s):\n",
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" return s == s[::-1]\n",
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"```\n",
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"\n",
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"Before writing any unit tests, let's review what each element of the function is doing exactly and what the author's intentions may have been.\n",
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"- First,\u001b[92m we have a function definition. This is where we give the function a name, `is_palindrome`, and specify the arguments that the function accepts. In this case, the function accepts a single string argument, `s`.\n",
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"- Next, we have a return statement. This is where we specify the value that the function returns. In this case, the function returns `s == s[::-1]`.\n",
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"- Finally, we have a function call. This is where we actually call the function with a specific set of arguments. In this case, we're calling the function with the string `\"racecar\"`.\u001b[30m\n",
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" \n",
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"A good unit test suite should aim to:\n",
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"- Test the function's behavior for a wide range of possible inputs\n",
|
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"- Test edge cases that the author may not have foreseen\n",
|
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"- Take advantage of the features of `pytest` to make the tests easy to write and maintain\n",
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"- Be easy to read and understand, with clean code and descriptive names\n",
|
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"- Be deterministic, so that the tests always pass or fail in the same way\n",
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"\n",
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"`pytest` has many convenient features that make it easy to write and maintain unit tests. We'll use them to write unit tests for the function above.\n",
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"\n",
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"For this particular function, we'll want our unit tests to handle the following diverse scenarios (and under each scenario, we include a few examples as sub-bullets):\n",
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"-\u001b[92m The input is a palindrome\n",
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" - `\"racecar\"`\n",
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" - `\"madam\"`\n",
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" - `\"anna\"`\n",
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"- The input is not a palindrome\n",
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" - `\"python\"`\n",
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" - `\"test\"`\n",
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" - `\"1234\"`\n",
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"- The input is an empty string\n",
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" - `\"\"`\n",
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"- The input is `None`\n",
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"- The input is not a string\n",
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" - `1`\n",
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" - `1.0`\n",
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" - `True`\n",
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" - `False`\n",
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" - `[]`\n",
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" - `{}`\u001b[30m\n",
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"\n",
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"In addition to the scenarios above, we'll also want to make sure we don't forget to test rare or unexpected edge cases (and under each edge case, we include a few examples as sub-bullets):\n",
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"-\u001b[92m The input is a palindrome with spaces\n",
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" - `\"race car\"`\n",
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" - `\" madam \"`\n",
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" - `\" anna \"`\n",
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"- The input is not a palindrome with spaces\n",
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" - `\" python \"`\n",
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" - `\" test \"`\n",
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" - `\" 1234 \"`\n",
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"- The input is a palindrome with punctuation\n",
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" - `\"racecar!\"`\n",
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" - `\"Madam, I'm Adam.\"`\n",
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" - `\"Anna's\"`\n",
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"- The input is not a palindrome with punctuation\n",
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" - `\"python!\"`\n",
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" - `\"test.\"`\n",
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" - `\"1234!\"`\n",
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"- The input is a palindrome with mixed case\n",
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" - `\"Racecar\"`\n",
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" - `\"Madam\"`\n",
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" - `\"Anna\"`\n",
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"- The input is not a palindrome with mixed case\n",
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" - `\"Python\"`\n",
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" - `\"Test\"`\n",
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" - `\"1234\"`\u001b[30m\n",
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"\n",
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"Before going into the individual tests, let's first look at the complete suite of unit tests as a cohesive whole. We've added helpful comments to explain what each line does.\n",
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"```python\n",
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"import pytest # used for our unit tests\n",
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"\n",
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"def is_palindrome(s):\n",
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" return s == s[::-1]\n",
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"\n",
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"#Below, each test case is represented by a tuple passed to the @pytest.mark.parametrize decorator\u001b[92m.\n",
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"#The first element of the tuple is a name for the test case, and the second element is a list of arguments for the test case.\n",
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"#The @pytest.mark.parametrize decorator will generate a separate test function for each test case.\n",
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"#The generated test function will be named test_is_palindrome_<name> where <name> is the name of the test case.\n",
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"#The generated test function will be given the arguments specified in the list of arguments for the test case.\n",
|
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"#The generated test function will be given the fixture specified in the decorator, in this case the function itself.\n",
|
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"#The generated test function will call the function with the arguments and assert that the result is equal to the expected value.\n",
|
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"@pytest.mark.parametrize(\n",
|
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" \"name,args,expected\",\n",
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" [\n",
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" # Test the function's behavior for a wide range of possible inputs\n",
|
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" (\"palindrome\", [\"racecar\"], True),\n",
|
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" (\"palindrome\", [\"madam\"], True),\n",
|
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" (\"palindrome\", [\"anna\"], True),\n",
|
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" (\"non-palindrome\", [\"python\"], False),\n",
|
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" (\"non-palindrome\", [\"test\"], False),\n",
|
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" (\"non-palindrome\", [\"1234\"], False),\n",
|
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" (\"empty string\", [\"\"], True),\n",
|
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" (\"None\", [None], False),\n",
|
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" (\"non-string\", [1], False),\n",
|
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" (\"non-string\", [1.0], False),\n",
|
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" (\"non-string\", [True], False),\n",
|
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" (\"non-string\", [False], False),\n",
|
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" (\"non-string\", [[]], False),\n",
|
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" (\"non-string\", [{}], False),\n",
|
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" # Test edge cases that the author may not have foreseen\n",
|
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" (\"palindrome with spaces\", [\"race car\"], True),\n",
|
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" (\"palindrome with spaces\", [\" madam \"], True),\n",
|
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" (\"palindrome with spaces\", [\" anna \"], True),\n",
|
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" (\"non-palindrome with spaces\", [\" python \"], False),\n",
|
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" (\"non-palindrome with spaces\", [\" test \"], False),\n",
|
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" (\"non-palindrome with spaces\", [\" 1234 \"], False),\n",
|
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" (\"palindrome with punctuation\", [\"racecar!\"], True),\n",
|
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" (\"palindrome with punctuation\", [\"Madam, I'm Adam.\"], True),\n",
|
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" (\"palindrome with punctuation\", [\"Anna's\"], True),\n",
|
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" (\"non-palindrome with punctuation\", [\"python!\"], False),\n",
|
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" (\"non-palindrome with punctuation\", [\"test.\"], False),\n",
|
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" (\"non-palindrome with punctuation\", [\"1234!\"], False),\n",
|
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" (\"palindrome with mixed case\", [\"Racecar\"], True),\n",
|
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" (\"palindrome with mixed case\", [\"Madam\"], True),\n",
|
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" (\"palindrome with mixed case\", [\"Anna\"], True),\n",
|
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" (\"non-palindrome with mixed case\", [\"Python\"], False),\n",
|
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" (\"non-palindrome with mixed case\", [\"Test\"], False),\n",
|
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" (\"non-palindrome with mixed case\", [\"1234\"], False),\n",
|
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" ],\n",
|
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")\n",
|
|
"def test_is_palindrome(is_palindrome, args, expected):\n",
|
|
" assert is_palindrome(*args) == expected\n"
|
|
]
|
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},
|
|
{
|
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"data": {
|
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"text/plain": [
|
|
"'.\\n#The first element of the tuple is a name for the test case, and the second element is a list of arguments for the test case.\\n#The @pytest.mark.parametrize decorator will generate a separate test function for each test case.\\n#The generated test function will be named test_is_palindrome_<name> where <name> is the name of the test case.\\n#The generated test function will be given the arguments specified in the list of arguments for the test case.\\n#The generated test function will be given the fixture specified in the decorator, in this case the function itself.\\n#The generated test function will call the function with the arguments and assert that the result is equal to the expected value.\\n@pytest.mark.parametrize(\\n \"name,args,expected\",\\n [\\n # Test the function\\'s behavior for a wide range of possible inputs\\n (\"palindrome\", [\"racecar\"], True),\\n (\"palindrome\", [\"madam\"], True),\\n (\"palindrome\", [\"anna\"], True),\\n (\"non-palindrome\", [\"python\"], False),\\n (\"non-palindrome\", [\"test\"], False),\\n (\"non-palindrome\", [\"1234\"], False),\\n (\"empty string\", [\"\"], True),\\n (\"None\", [None], False),\\n (\"non-string\", [1], False),\\n (\"non-string\", [1.0], False),\\n (\"non-string\", [True], False),\\n (\"non-string\", [False], False),\\n (\"non-string\", [[]], False),\\n (\"non-string\", [{}], False),\\n # Test edge cases that the author may not have foreseen\\n (\"palindrome with spaces\", [\"race car\"], True),\\n (\"palindrome with spaces\", [\" madam \"], True),\\n (\"palindrome with spaces\", [\" anna \"], True),\\n (\"non-palindrome with spaces\", [\" python \"], False),\\n (\"non-palindrome with spaces\", [\" test \"], False),\\n (\"non-palindrome with spaces\", [\" 1234 \"], False),\\n (\"palindrome with punctuation\", [\"racecar!\"], True),\\n (\"palindrome with punctuation\", [\"Madam, I\\'m Adam.\"], True),\\n (\"palindrome with punctuation\", [\"Anna\\'s\"], True),\\n (\"non-palindrome with punctuation\", [\"python!\"], False),\\n (\"non-palindrome with punctuation\", [\"test.\"], False),\\n (\"non-palindrome with punctuation\", [\"1234!\"], False),\\n (\"palindrome with mixed case\", [\"Racecar\"], True),\\n (\"palindrome with mixed case\", [\"Madam\"], True),\\n (\"palindrome with mixed case\", [\"Anna\"], True),\\n (\"non-palindrome with mixed case\", [\"Python\"], False),\\n (\"non-palindrome with mixed case\", [\"Test\"], False),\\n (\"non-palindrome with mixed case\", [\"1234\"], False),\\n ],\\n)\\ndef test_is_palindrome(is_palindrome, args, expected):\\n assert is_palindrome(*args) == expected\\n'"
|
|
]
|
|
},
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"example_function = \"\"\"def is_palindrome(s):\n",
|
|
" return s == s[::-1]\"\"\"\n",
|
|
"\n",
|
|
"unit_test_from_function(example_function, print_text=True)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3.9.9 ('openai')",
|
|
"language": "python",
|
|
"name": "python3"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
|
"name": "ipython",
|
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"version": 3
|
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},
|
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"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
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"name": "python",
|
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
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"version": "3.9.9"
|
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},
|
|
"orig_nbformat": 4,
|
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"vscode": {
|
|
"interpreter": {
|
|
"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
|
|
}
|
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}
|
|
},
|
|
"nbformat": 4,
|
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"nbformat_minor": 2
|
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}
|