AI for Software Development: your first working example

Why this example matters

The example examines a short Python source string and records whether particular patterns occur. It illustrates a basic check, not a parser and not a security review.

What you will be able to do

• Recognize the input values and describe what they represent in AI for Software Development.
• Run the AI for Software Development program and explain why its displayed output follows from the recorded operations.
• Identify a limitation of the AI for Software Development activity before applying similar reasoning to a real system.

Original worked program

The following AI for Software Development demonstration uses small invented values so you can trace every operation. The AI for Software Development activity uses only Python standard-library features; it downloads no model and calls no paid service.

source = "def add(a, b):" + chr(10) + "    return a + b" + chr(10)
checks = {"has_function": "def add" in source,
          "returns_value": "return" in source,
          "has_tests": "assert" in source}
for name, passed in checks.items():
    print(name, passed)

Actual output from Python

has_function True
returns_value True
has_tests False

Read each instruction

1. source = "def add(a, b):" + chr(10) + " return a + b" + chr(10)The assignment gives a name to a value or a calculated result in AI for Software Development. In the AI for Software Development activity, check the named values on that line rather than memorizing symbols.
2. checks = {"has_function": "def add" in source,The assignment gives a name to a value or a calculated result in AI for Software Development. In the AI for Software Development activity, check the named values on that line rather than memorizing symbols.
3. for name, passed in checks.items():The loop repeats an operation over the available values in AI for Software Development. In the AI for Software Development activity, check the named values on that line rather than memorizing symbols.

Observe the results

To verify the AI for Software Development result, copy the program into a fresh file and run it once without modification. Then change only one input. For the AI for Software Development trial, record what you predict before pressing Run again. Compare the AI for Software Development prediction with the observed result.

Second original practice: Specifying a programming task

The AI for Software Development extension changes the task, not just the numbers in the first example. Study its new data structure or operation, then decide how the result supports the milestone “Specifying a programming task”.

def discount(price, percent):
    return price*(1-percent/100)
assert discount(100, 15)==85
print("Unit test passed")

Actual second output

Unit test passed

For the AI for Software Development extension, read the code from its first assignment or import to its final print statement. Explain the additional AI for Software Development idea introduced by this extension, then design a check that could reveal a mistake. The AI for Software Development extension output above was recorded by running that independent program.

What the example does not prove

Finding the word “return” does not prove a function works. Actual tests should execute behavior, cover edge cases and check for unsafe dependencies.

A reliable AI for Software Development project also needs an explicit objective, test cases that were not used to tune the approach, documentation of the assumptions, and review of situations where the result might mislead the user. Do not mistake the small AI for Software Development program for evidence of accuracy or safety in a deployed application.

How to validate your own result

For AI for Software Development, a convincing result starts with a written claim about the actual task, not the appearance of a successful print statement. For AI for Software Development, list the initial values and the one value you will vary; record a prediction that could be disproved. Save both AI for Software Development outputs as separate records to avoid mixing up the results of the two investigations. A correct Python run only establishes that the particular instructions executed; it does not establish that the assumptions match every situation in which AI for Software Development might be used.

Connect this trial to “Writing testable requirements” without claiming that the milestone is already complete. Name a measurement you would collect for AI for Software Development, a plausible error source and a person able to review the decision. The later topic “Autocomplete versus reasoning tools” deserves its own investigation with different evidence. Keeping these questions separate prevents your AI for Software Development learning journal from becoming a collection of exercises that all make the same claim.

Finally, keep the AI for Software Development experiment reproducible: include the Python version, exact script, input values, observed results and any unexpected behavior. If a AI for Software Development investigation uses external data, document the source and your permission to use those records. A new learner should be able to explain every output line in ordinary words before moving into the next milestone of AI for Software Development.

Second verified lab: Data in practice

To deepen Chapter 1 of AI for Software Development, compare the first calculation with a different, executable experiment. The small data lab below illustrates a related technique, not every possible implementation of where ai helps developers.

# Course: AI for Software Development | Chapter 1 | Subject: Where AI helps developers
# Validate a toy input table before using it downstream
records=[{"id":1,"value":7},{"id":2,"value":None},{"id":3,"value":4}]
valid=[row for row in records if isinstance(row["value"],int)]
print("Rows received:",len(records))
print("Valid rows:",len(valid))
print("Missing rows:",len(records)-len(valid))
print("Valid total:",sum(row["value"] for row in valid))

Actual output of the displayed code

Rows received: 3
Valid rows: 2
Missing rows: 1
Valid total: 11

While examining your first working example in the ai for software development course (chapter 1), apply this test: Explicit schema and missing-value checks make the transformation reproducible. The example does not silently treat missing observations as zeros. For the campus shuttle passenger counts exercise, distinguish what the code observes from any stronger predictive or causal claim.

While examining your first working example in the ai for software development course (chapter 1), apply this test: Interpretation limit. Filtering records changes the represented population; document what was excluded and why. Record the changed inputs and compare both outputs before extending the result to a project.Interpretation limit. Filtering records changes the represented population; document what was excluded and why. Record the changed inputs and compare both outputs before extending the result to a project.

20 practice questions and worked answers

Test your understanding of your first working example in the ai for software development course (chapter 1). Compare the claims in both runnable labs, and use the outputs to defend each numerical response.

Build one original mini-project

Chapter 1 checkpoint: Explain how where ai helps developers affects the campus shuttle passenger counts example; demonstrate one failing input; then propose a revision with an acceptance test and a recorded output. This checkpoint belongs to the AI for Software Development course.

Write a specification for a temperature converter, then list tests a human reviewer should demand before merging generated code.

A six-step practice plan

1. Where AI helps developers. For the “Where AI helps developers” exercise, identify one observable result and record what would demonstrate progress in AI for Software Development.
2. Specifying a programming task. For the “Specifying a programming task” exercise, identify one observable result and record what would demonstrate progress in AI for Software Development.
3. Writing testable requirements. For the “Writing testable requirements” exercise, identify one observable result and record what would demonstrate progress in AI for Software Development.
4. Autocomplete versus reasoning tools. For the “Autocomplete versus reasoning tools” exercise, identify one observable result and record what would demonstrate progress in AI for Software Development.
5. Reviewing suggested code. For the “Reviewing suggested code” exercise, identify one observable result and record what would demonstrate progress in AI for Software Development.
6. Prompting for unit tests. For the “Prompting for unit tests” exercise, identify one observable result and record what would demonstrate progress in AI for Software Development.

Save your original inputs, your changed inputs, the two actual outputs and a short explanation of what you learned about AI for Software Development. The AI for Software Development record forms evidence for the first milestone of your project portfolio.

Where you go next

Your AI for Software Development roadmap has 18 subject-specific milestones progressing toward evaluation and a capstone. Future AI for Software Development lessons will require new demonstrations and evidence, beyond the skills taught in this first chapter.