PYEVOLVE: Automating Frequent Code Changes in Python ML Systems

Because of the naturalness of software and the rapid evolution of Machine Learning (ML) techniques, frequently repeated code change patterns (CPATs) occur often. They range from simple API migrations to changes involving several complex control structures such as for loops. While manually performing...

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Vydáno v:Proceedings / International Conference on Software Engineering s. 995 - 1007
Hlavní autoři: Dilhara, Malinda, Dig, Danny, Ketkar, Ameya
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2023
Témata:
Codes
Engines
Machine learning
Organ transplantation
Program synthesis
Program transformation
Programming by example
Python
Repetitive code changes
Software
Software engineering
Transformation by Example
Transforms
ISSN:1558-1225
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Shrnutí:Because of the naturalness of software and the rapid evolution of Machine Learning (ML) techniques, frequently repeated code change patterns (CPATs) occur often. They range from simple API migrations to changes involving several complex control structures such as for loops. While manually performing CPATs is tedious, the current state-of-the-art techniques for inferring transformation rules are not advanced enough to handle unseen variants of complex CPATs, resulting in a low recall rate. In this paper we present a novel, automated workflow that mines CPATs, infers the transformation rules, and then transplants them automatically to new target sites. We designed, implemented, evaluated and released this in a tool, PYEVOLVE. At its core is a novel data-flow, control-flow aware transformation rule inference engine. Our technique allows us to advance the state-of-the-art for transformation-by-example tools; without it, 70% of the code changes that PYEVOLVE transforms would not be possible to automate. Our thorough empirical evaluation of over 40,000 transformations shows 97% precision and 94% recall. By accepting 90% of CPATs generated by PYEVOLVE in famous open-source projects, developers confirmed its changes are useful.
ISSN:1558-1225
DOI:10.1109/ICSE48619.2023.00091