- product manager. ex software engineering and research intern. CS grad
- used to work with pharo a lot, you can find my projects here
- linkedin: https://www.linkedin.com/in/myroslavarm/
- medium blog: https://medium.com/@myroslavarm
Bachelor thesis on the topic of Improving Code Completion in Pharo Using N-gram Language Models
To shortly wrap up everything that was explained.
Find 10 cases and test them on all three sorting strategies and compare the accuracy of results.
n-gram
/N-gram
state-of-the-art
you
with one
token
type to tokens
so there are several ideas but the main suspicions are:
when training, some problems involving punctuation and delimiters, such as ; ] ) etc.
when training, essentially tokenisation problems in the ngram model, because in a lot of cases something like parantheses might be missed and so the bigram dependency will be trained differently (think something = 1 ifTrue:
the completion itself is wrong because it's for an integer, but also when we train the ngram sequence is also number-based, and then during the actual evaluation when we are comparing the actual code that was typed in and calculating the accuracy it will be way off because our typing/completion is different from the end result)
another thing is with error nodes, because we might be getting the wrong ast, and hence getting wrong placeholder stuff for the actual code at the end, so again training/actual code discrepancy
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