qbee is a QBASIC compiler written in Python. The compilation target is a virtual machine called QVM. Most of the core language is complete, but graphics routines are mostly missing. qbee can compile and run the classic nibbles example:

$ python -m qbee.main -o nibbles.mod -O3 -v nibbles.bas
$ python -m qvm.run nibbles.mod

qbee has a source-level debugger capable of tracing code, setting breakpoints and evaluating variables and expressions at run-time. In order to be able to use it, you need to compile the source using the -g compiler option.

$ python -m qbee.main -o nibbles.mod -O3 -v -g nibbles.bas

Then you can run the compiled module in the debugger:

python -m qvm.dbg nibbles.mod

You can step through the program using next and step commands, set breakpoints using break, and evaluate expressions using print. Run help to get a full list of all available debugger commands.

You can find a description of the QVM virtual machine, and its instruction set at the project wiki on github.

Here's a short example so that you can see what the QVM assembly looks like:

$ cat >foo.bas <<EOF
> defint a-z
> input i
> if i < 10 then
>    print "too small"
> else
>    print "too big"
> end if
> EOF

$ python -m qbee.main -O3 --asm -o- foo.bas
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
.literals
    0 string ""
    1 string "too small"
    2 string "too big"

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
.routines

_main:
    integer i

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
.code

    call        _sub__main
    halt
_sub__main:
    frame       0, 1
    push0%
    push$       ""
    pushm1%
    push1%
    push1%
    io          terminal, input
    storel      i
    readl%      i
    push%       10
    cmp
    lt
    jz          _else_2
    push0%
    push$       "too small"
    push2%
    io          terminal, print
    jmp         _endif_1
_else_2:
    push0%
    push$       "too big"
    push2%
    io          terminal, print
_endif_1:
    ret

qbee has a fairly extensive test suite. You can run the tests using pytest: python -m pytest

Python 3.9 or higher is required. This is needed because:

• Python 3.9 allows having classmethod and property decorators on the same method.
• It allows using indexed built-in types for type hinting (like list[int], instead of List[int])