ErikSchierboom
commented
1 year ago Exercise: two-fer
Code
section .rodata
ONE_FOR: db "One for ", 0
YOU: db "you", 0
ONE_FOR_ME: db ", one for me.", 0
section .text
global two_fer
two_fer:
; rdi contains optional word
; rsi contains destination buffer
mov r10, rdi
mov rdi, rsi
; r10 contains optional word
; rdi contains destination buffer
; ignoring rsi contents.
; using rsi to as input param to _CopyToBuffer
_TransferOneFor:
mov rsi, ONE_FOR
call _CopyToBuffer
_TestIncoming:
cmp r10, 0h ;NULL
jz _TransferYou
cmp byte [r10], 0h
jz _TransferYou
_TransferName:
mov rsi, r10
call _CopyToBuffer
jmp _TransferAndOneForMe
_TransferYou:
mov rsi, YOU
call _CopyToBuffer
_TransferAndOneForMe:
mov rsi, ONE_FOR_ME
call _CopyToBuffer
mov byte [rdi], 0h
ret
_CopyToBuffer:
cmp byte [rsi], 0h
jz _CopyToBufferEnd
_CopyToBufferStart:
mov al, byte [rsi]
mov byte [rdi], al
inc rdi
inc rsi
cmp byte [rsi], 0h
jnz _CopyToBufferStart
_CopyToBufferEnd:
retTags:
construct:byte
construct:comment
construct:db
construct:global-label
construct:hexadecimal-number
construct:instruction
construct:label
construct:local-label
construct:mov
construct:parameter
construct:section-directive
construct:string
construct:undefined-value
construct:word
paradigm:imperative
paradigm:structured
Hello lovely maintainers :wave:
We've recently added "tags" to student's solutions. These express the constructs, paradigms and techniques that a solution uses. We are going to be using these tags for lots of things including filtering, pointing a student to alternative approaches, and much more.
In order to do this, we've built out a full AST-based tagger in C#, which has allowed us to do things like detect recursion or bit shifting. We've set things up so other tracks can do the same for their languages, but its a lot of work, and we've determined that actually it may be unnecessary. Instead we think that we can use machine learning to achieve tagging with good enough results. We've fine-tuned a model that can determine the correct tags for C# from the examples with a high success rate. It's also doing reasonably well in an untrained state for other languages. We think that with only a few examples per language, we can potentially get some quite good results, and that we can then refine things further as we go.
I released a new video on the Insiders page that talks through this in more detail.
We're going to be adding a fully-fledged UI in the coming weeks that allow maintainers and mentors to tag solutions and create training sets for the neural networks, but to start with, we're hoping you would be willing to manually tag 20 solutions for this track. In this post we'll add 20 comments, each with a student's solution, and the tags our model has generated. Your mission (should you choose to accept it) is to edit the tags on each issue, removing any incorrect ones, and add any that are missing. In order to build one model that performs well across languages, it's best if you stick as closely as possible to the C# tags as you can. Those are listed here. If you want to add extra tags, that's totally fine, but please don't arbitrarily reword existing tags, even if you don't like what Erik's chosen, as it'll just make it less likely that your language gets the correct tags assigned by the neural network.
To summarise - there are two paths forward for this issue:
If you tell us you're not able/wanting to help or there's no comment added, we'll automatically crowd-source this in a week or so.
Finally, if you have questions or want to discuss things, it would be best done on the forum, so the knowledge can be shared across all maintainers in all tracks.
Thanks for your help! :blue_heart:
Note: Meta discussion on the forum