VCE Software Development U3O1

VCE Software Development Unit 3 Outcome 1 — Programming — is where students learn to write, test and document working programs: choosing data types and data structures, reading data from files, validating input, building a graphical user interface, applying object-oriented design, and implementing searching and sorting algorithms.

This page lists the 103 free modules written for this outcome: data dictionaries, class diagrams and object descriptions; naming conventions (camel case, snake case, Hungarian notation) and internal documentation with comments, docstrings and stubs; one- and two-dimensional arrays and records; plain text, CSV and XML files; existence, type and range checks; tkinter windows, labels, entries, buttons and messageboxes; abstraction, encapsulation, generalisation and inheritance; linear and binary search, selection sort, recursion and quick sort; logic and runtime errors, tracebacks, breakpoints and trace tables; test data, boundary values and test cases; and using AI code generation tools responsibly. Each module includes real practice questions and a full answer key.

The full journey below also uses Cloodit's Python and VCE Pseudocode modules where the topic is the same, so it is a complete course for the outcome. Teachers can assign the whole outcome, or one key knowledge area at a time, with progress tracking, class sprints and badges.

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Requirements and design tools (5 modules)

Data dictionary A table listing each data element with its name, data type, size or format, description and validation rule, so everyone uses the data the same way when coding and testing.

Writing a data dictionary entry One data dictionary row for a data item: name, data type, format or size, description, example value and validation rule. Written from a requirement or IPO chart.

Designing classes with a class diagram A box listing a class's name, attributes and methods. An arrow joins a child class to the parent it inherits from. You draw it as a plan before writing the code.

Object descriptions A design table for a class that lists its name, its attributes with data types, and its methods with their parameters and purpose.

Writing an object description from a requirement Names an object, its attributes with data types and its methods, using only what the requirement needs. Example: Student has name: string, age: integer, getAge().

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Data types and Python basics (2 modules)

Constants A constant: a named value set once and never changed while the program runs. Written in UPPER_CASE by convention, it replaces magic numbers and is easy to update in one place.

Sequence: instructions run in order A control structure where statements run one after another, top to bottom, so changing their order can change the result.

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Selection and iteration in Python (2 modules)

Nested if statements in Python An if statement indented inside another if block. The inner condition is only checked when the outer condition is True.

Counters and totals in a Python loop Set count = 0 before a loop, then use count += 1 inside it to add 1 each repeat. Use total += value to build a running total.

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Naming conventions and internal documentation (10 modules)

Purposes of naming conventions An agreed pattern for naming variables, functions and controls so code is readable, consistent across a team and easier to maintain and debug.

Camel casing Camel casing: join words with no spaces, start with a lower case letter, then capitalise the first letter of each later word, e.g. studentFirstName or calculateTotal.

Snake casing A naming convention that writes every letter in lower case and joins words with underscores, e.g. student_first_name. Python uses it for variables and functions.

Hungarian notation for variables A naming style where a variable name starts with a short lower-case prefix showing its data type, e.g. strName, intAge, fltPrice, blnPassed.

Hungarian notation for interface controls Hungarian notation for a control: a type prefix (btn button, txt textbox, lbl label, lst list, cbo combobox) plus a purpose, so btnSubmit is clearly a button.

Applying a naming convention to code structures A consistent rule for naming code structures: snake_case verbs for functions and methods (get_total), CamelCase nouns for classes (StudentRecord). Pick one style and stick to it.

Purposes of internal documentation: code maintenance Comments inside the code, like a header with purpose, author and date, and notes on each function, that help others (or you later) understand and safely change the program.

Comments that explain and justify data and code structures A comment that explains what a variable, array, record or algorithm is for and why it was chosen, rather than just repeating what the code does.

Docstrings for functions and classes A triple-quoted string on the first line of a function or class body. It documents what the code does, its parameters and its return value. help() displays it.

Stubs: placeholder code and comments for future development A Python statement that does nothing. It fills a stub's empty body, often beside a # TODO comment, so the program still runs while the real code is written later.

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Pseudocode (2 modules)

Counters and accumulators in pseudocode A counter. It adds 1 to a variable each time a loop repeats. Set it to 0 before the loop. An accumulator works the same way: total <- total + x adds a value each time.

Turning pseudocode into Python Pseudocode steps map to Python lines: <- becomes =, input becomes input(), if becomes if with a colon, for becomes for in range(). END IF becomes un-indenting.

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Functions and program structure (3 modules)

Changing a global variable with the global keyword A keyword used inside a function so it can assign to a variable defined outside all functions, instead of creating a new local variable with the same name.

Functions calling other functions A function can call another function inside its body. Big problems get split into small helper functions that each do one job, like boxes in a decomposition.

Program structure with a main() function Defines a function called main that holds the main steps of your program. You call main() once at the end of the file, after the other functions are defined, to start the program.

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Data structures (9 modules)

One-dimensional arrays A named, ordered collection of elements of the same data type. Each element is reached by its index, its position number (0-based in Python, often 1-based in pseudocode).

Creating an array of a set size Creates a one-dimensional array (list) of 10 elements that all start with the value 0. Change the number to set the size, or use '' for strings.

Looping through a list with for...in Runs the indented block once for each value in the list. The variable item holds the current value each time round, starting with the first and finishing with the last.

Looping through an array by index Loops i through every index of arr, so arr[i] can be read or changed and you know each element's position. Used for totals, finding the largest value and editing in place.

Two-dimensional arrays A 2D array stores data in rows and columns like a table. Use the row index first, then the column index, to reach one element, such as a seat in a seating plan.

Traversing a 2D array with nested loops Reads one element of a 2D array. An outer loop steps through each row and an inner loop steps through each column, so every element in the grid gets visited.

Records: fields with different data types A data structure that groups related fields of different data types into one item describing one thing, e.g. a student's name (String), age (Integer) and enrolled (Boolean).

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Accessing a record's fields by name and by field index Gets a field from a record by its field name. A list or tuple record uses the field index (its position) instead, such as student[0]. Indexes start at 0.

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Choosing a data structure Pick the structure by how you use the data: a list for an ordered collection, a dict for lookup by key, and a tuple for a fixed group of values that should not change.

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Data sources and files (8 modules)

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Plain text (TXT) files: structure A plain text file holds unformatted characters, usually one line of text per line ended by a newline. It has no field headings or data types, so everything is read as a string.

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Delimited (CSV) files: structure A text file with one record per line, fields split by a delimiter such as a comma, and an optional header line of field names. Text containing commas goes in quotes.

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XML files: structure XML stores data in nested, named elements. Each element has an opening and closing tag, and child elements sit inside a root element. The tags describe the data.

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Reasons for using TXT, CSV and XML data sources Choose a data source by structure: TXT for simple lines, CSV for tables of records, XML for nested self-describing data shared between systems. All three store data between runs.

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Splitting a line into fields with .split() A string method that breaks a line of text into a list of field strings at each comma. Use strip() first to remove the newline, and int() or float() to convert fields.

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Reading a CSV file into an array of records Splits one CSV line into fields and appends them as one record to a list, so each row of the file becomes a record and the whole file becomes a table in memory.

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Writing records to a CSV file Joins a record's fields, which must all be strings, into one comma-separated line. Use str() on numbers first, then write the line plus a newline to the CSV file.

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Reading an XML file with ElementTree Python module that parses an XML file into a tree of elements. Use find() and findall() to reach elements and .text to read their values.

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Validation (7 modules)

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Validation: why data is checked on entry An automatic check that data is reasonable and in the right form when entered. It blocks bad data early, but it cannot prove the data is true (a valid age of 15 could still be wrong).

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Types of validation check A test that an entry is sensible. Types include presence, data type, range, length, format and list. Each one tests a different property of the entry.

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Existence checking in code An existence check tests that a value was actually entered. strip() removes spaces, so this is True for empty or space-only input, and an error message can be shown.

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Type checking in code A check that input is the expected data type, using .isdigit(), try/except around int() or float(), or isinstance(). input() is always text, so check before converting.

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Range checking in code A range check: tests that a number lies between a minimum and a maximum (inclusive here). Values outside the limits, such as 0 or 121, are rejected as invalid.

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Input validation with a while loop A loop that keeps asking for input again until the answer is valid, so bad data (like an age of -5) can't get into your program. Show an error message each time.

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Combining validation checks in a validation function A validation function that runs the existence, type and range checks in order and returns True or False, so the same checks can be reused for many inputs and tested.

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Graphical user interfaces (8 modules)

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What a graphical user interface (GUI) is A graphical user interface: windows and controls like buttons and text boxes the user clicks or types in. The program waits for events, such as a click, and responds to each one.

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Creating a tkinter window Creates the main tkinter window after import tkinter as tk. Call root.mainloop() at the end to show the window and wait for events like clicks.

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GUI Label control Creates a tkinter label that shows text in the window. Use .config(text=...) to change what it shows, such as displaying a result. Options like font and fg set style.

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GUI Entry control and .get() Creates a text box in the window. Call entry.get() to read what the user typed. It is always a string, so convert it with int() or float() to use it as a number.

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GUI Button control and event handlers Makes a button that runs the function show when clicked. Write the function name with no brackets, or it runs straight away instead of on click.

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Placing controls with grid() and pack() Places a control in a table-like layout at the given row and column. pack() instead stacks controls in order. Never mix grid and pack in one window.

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Choice controls: Checkbutton, Radiobutton and Combobox A tkinter variable that stores the user's pick from a choice control (Checkbutton, Radiobutton or Combobox). Link it with variable= and read the choice with .get().

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Showing messages with messagebox Pops up a message window to tell the user a result. Its partner messagebox.showerror() pops up an error, such as when a handler finds invalid input. Both need a title and a message.

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Object-oriented programming (6 modules)

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OOP principle: abstraction Modelling only the attributes and methods that matter for a purpose and hiding the rest of the detail, such as a Student class that keeps just the data a school app needs.

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OOP principle: encapsulation Bundling an object's data and methods together and restricting access, so private data is only changed through public methods that can check values are valid.

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Setter methods that validate data A setter method that checks a new value (such as a valid age range) before storing it in a private attribute, rejecting or reporting bad values so the object never holds invalid data.

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OOP principle: generalisation Finding the attributes and methods that several classes share and moving them into one more general parent class, so the code is not repeated in each specialised class.

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OOP principle: inheritance Inheritance: the child (sub) class reuses the parent (super) class's attributes and methods and can add or change its own, showing an is-a relationship.

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Calling the parent __init__ with super() Runs the parent class's __init__ first so it sets up inherited attributes, then the child's __init__ adds its own extra attributes.

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Searching and sorting algorithms (10 modules)

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Linear search in Python An algorithm that uses a loop to check each item in a list in turn. It stops when it finds the target or reaches the end of the list, often returning the index or -1.

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Binary search in Python The binary search loop: while low <= high, check the middle item of a sorted list, then move low or high to halve the search range each time.

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Selection sort in Python A sorting algorithm that uses nested loops to find the smallest remaining item, then swaps it into place with a, b = b, a. It repeats for each position until the list is sorted.

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Recursion: a function calling itself A function that calls itself with a smaller problem each time, and stops at a base case that returns without calling itself. With no base case, Python raises a RecursionError.

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Quick sort: pivot and partitioning Picks a pivot, splits the list into items smaller and larger than the pivot, then sorts each part the same way (recursion) until the parts are sorted.

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Quick sort in Python Recursive step of quick sort: sorts the smaller and larger lists by calling itself, then joins them around the equal items. Base case len(items) <= 1 stops it.

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Quick sort in pseudocode A recursive function that picks a pivot, partitions the array into smaller and larger parts around it, then calls itself on each part until the array is sorted.

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Lists – Sorting with sorted() and .sort() sorted(x) returns a new sorted list and leaves x unchanged. x.sort() sorts the list x itself (in place) and returns None. Both accept reverse=True and key= to change the order.

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Comparing searching and sorting algorithms How many steps an algorithm takes as data grows, and what it needs (e.g. sorted data for binary search). Compare it to choose between linear or binary search, selection or quick sort.

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Applying an algorithm to a task A step-by-step method for solving a problem, like search or sort. Choose one by matching its input and output to your data, then wrap it in a function to reuse it.

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Errors and debugging (12 modules)

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Logic errors in Python A mistake that lets a program run with no error message but give the wrong result, caused by a wrong condition, operator (> or >=), range() off by one, or steps in the wrong order.

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Runtime errors in Python An error that stops a program while it runs, shown as a named error like NameError, ValueError or ZeroDivisionError. Read the message to find the problem.

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Runtime error: overflow An error raised when a calculation gives a number too big for its data type to hold, such as a Python float going past its maximum, e.g. 2.0 ** 10000.

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Runtime error: index out of range A runtime error raised when code uses an index that is not in the list, such as items[5] in a 5-item list. The last valid index is len(items) - 1.

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Runtime error: type mismatch A runtime error when an operation gets the wrong data types, like '5' + 3 (string plus integer). Fix it by converting with int() or str(), e.g. int(input()).

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Runtime error: divide by zero A Python runtime error raised when a number is divided by 0, or a remainder is taken with % 0. Guard against it with an if check or try/except.

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Reading an error message and traceback The start of Python's error report. It names the error type (like NameError) and the line number where it happened, so you know where to start looking for the bug.

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Debugging with print() A debug print. Add a line like this to show a variable's value while the program runs, so you can spot where it goes wrong. Put one inside a loop to watch it change, then remove it once fixed.

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Debugging a program with print() and trace tables Debugging tool: print a variable at key points, then compare each value with your trace table of what it should be. The first mismatch shows where the bug is.

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Setting a breakpoint A marker set on a line of code (click beside the line number) that pauses the program there in debug mode, so you can inspect variables and step through the code.

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Stepping through code and watching variables Run paused code one line at a time using step over, step into or step out, while the watch panel shows each variable change, to find where a value goes wrong.

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Tracing nested loops A loop inside another loop. When tracing, the inner loop completes all its repeats for each single repeat of the outer loop, so total passes = outer repeats x inner repeats.

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Testing modules (8 modules)

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Choosing a range of test data A good test set uses normal data (typical values), boundary data (values at the edges of a range) and invalid data (values that should be rejected), so every path in an algorithm is checked.

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Boundary test values Test inputs at the edge of a condition. For age >= 18, test 17 (just below), 18 (exactly on) and 19 (just above) to check the algorithm picks the right path at the edge.

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Constructing test data for validation checks Values chosen to test each validation rule: empty for existence, text for type, and values just inside and just outside range limits, each with an expected result.

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Test cases: input, expected output, actual output A chosen input paired with the output you expect. Run the algorithm, record the actual output, then compare: if actual matches expected it passes, otherwise it fails.

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Testing a Python program with test cases A chosen input paired with the output you expect. Run the program with it, compare the real output to the expected result in your test table, then record pass or fail.

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Testing a function with assert Checks a condition is True, like assert add(2, 3) == 5. If the result is not what you expected, it raises an AssertionError, so test cases can run automatically.

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Testing one module in isolation Testing one function or class on its own, with its own test table, before joining it to the rest of the program, so a failing test points to that module.

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Modifying an existing program Read and trace the code first to see what it does, then change one small part at a time and re-run your test cases to check the effect of that change.

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Artificial intelligence in programming (11 modules)

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Emerging trend: AI code generation tools Using AI tools, like editor assistants and chatbots, to turn plain-English prompts into program code. It saves time, but the code must still be checked and tested by the programmer.

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Writing a prompt to generate code A clear AI request stating the language, the task, the inputs and outputs, and the constraints. Keep to one task per prompt, e.g. Write a Python function that returns an average.

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Refining a prompt after reviewing the output A new prompt sent after reviewing AI code that names the exact problem, e.g. 'Use snake_case names and add range checking', so the next version fixes it.

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Checking and testing AI-generated code Read and trace AI code, then test it with your own boundary and invalid data, and check it meets the requirements. It can look right but be wrong or use made-up functions.

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Automated debugging with AI tools Giving an AI tool your code, the error message and the expected behaviour so it suggests a cause and a fix, which you must still understand and test yourself.

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Automated test generation with AI tools AI can draft assert tests for a module, but you must check each expected output by hand to be sure it is correct. Also add any normal, boundary or invalid cases it missed.

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Code optimisation Changing code so it runs faster, uses less memory or is easier to read, without changing what it does. For example, removing repeated code or using a built-in function.

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Optimising code with AI tools Re-running the same test cases on AI-optimised code to prove it still gives identical results as before. If any result changes, reject the change.

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Protecting privacy and confidential code when using AI tools Private information like personal data, passwords or secret code. Never paste it into an AI tool, as it may be stored or used to train the model. Use fake sample data and check the data policy.

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Acknowledging AI use and licensing of generated code A comment that acknowledges AI helped write the code. Name the tool and prompt, and check the output is not copied licensed work. You stay responsible for the final code.

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Bias and over-reliance when using AI to program Trusting AI-generated code without checking it. The code may be biased, outdated or insecure, and you lose your own skills and judgement. Always review it and stay in control.

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Printable VCE Software Development U3O1 Crosswords & Word Searches

Built from the key terms of each block of modules, every one with a full answer key. All free printables →

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Crossword: Requirements and design tools to Naming conventions and internal documentation (Modules 10–47) 10 clues · Data dictionary, Writing a data dictionary entry, Designing classes with a class diagram, Object descriptions and more

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Crossword: Naming conventions and internal documentation and Pseudocode (Modules 48–71) 10 clues · Camel casing, Snake casing, Hungarian notation for variables, Hungarian notation for interface controls and more

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Crossword: Pseudocode to Data structures (Modules 72–96) 10 clues · Turning pseudocode into Python, Changing a global variable with the global keyword, Functions calling other functions, Program structure with a main() function and more

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Crossword: Data structures and Data sources and files (Modules 98–113) 10 clues · Records: fields with different data types, Accessing a record's fields by name and by field index, Choosing a data structure, Plain text (TXT) files: structure and more

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Crossword: Data sources and files to Graphical user interfaces (Modules 114–124) 10 clues · Reading an XML file with ElementTree, Validation: why data is checked on entry, Types of validation check, Existence checking in code and more

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Crossword: Graphical user interfaces and Object-oriented programming (Modules 125–141) 10 clues · GUI Label control, GUI Entry control and .get(), GUI Button control and event handlers, Placing controls with grid() and pack() and more

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Crossword: Object-oriented programming and Searching and sorting algorithms (Modules 142–155) 10 clues · OOP principle: inheritance, Calling the parent __init__ with super(), Linear search in Python, Binary search in Python and more

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Crossword: Searching and sorting algorithms and Errors and debugging (Modules 156–166) 10 clues · Comparing searching and sorting algorithms, Applying an algorithm to a task, Logic errors in Python, Runtime errors in Python and more

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Crossword: Errors and debugging and Testing modules (Modules 168–177) 10 clues · Debugging a program with print() and trace tables, Setting a breakpoint, Stepping through code and watching variables, Tracing nested loops and more

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Crossword: Testing modules and Artificial intelligence in programming (Modules 178–193) 10 clues · Testing one module in isolation, Modifying an existing program, Emerging trend: AI code generation tools, Writing a prompt to generate code and more

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Word Search: Requirements and design tools to Pseudocode (Modules 10–71) 21 words to find · Data dictionary, Writing a data dictionary entry, Designing classes with a class diagram, Object descriptions and more

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Word Search: Pseudocode to Data sources and files (Modules 72–113) 21 words to find · Turning pseudocode into Python, Changing a global variable with the global keyword, Functions calling other functions, Program structure with a main() function and more

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Word Search: Data sources and files to Object-oriented programming (Modules 114–141) 20 words to find · Reading an XML file with ElementTree, Validation: why data is checked on entry, Types of validation check, Existence checking in code and more

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Word Search: Object-oriented programming to Errors and debugging (Modules 142–166) 20 words to find · OOP principle: inheritance, Calling the parent __init__ with super(), Linear search in Python, Binary search in Python and more

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Word Search: Errors and debugging to Artificial intelligence in programming (Modules 168–193) 20 words to find · Debugging a program with print() and trace tables, Setting a breakpoint, Stepping through code and watching variables, Tracing nested loops and more

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