An empirical study of refactorings and technical debt in machine learning systems
Machine Learning (ML), including Deep Learning (DL), systems, ie, those with ML
capabilities, are pervasive in today's data-driven society. Such systems are complex; they …
capabilities, are pervasive in today's data-driven society. Such systems are complex; they …
Toward efficient interactions between Python and native libraries
Python has become a popular programming language because of its excellent
programmability. Many modern software packages utilize Python for high-level algorithm …
programmability. Many modern software packages utilize Python for high-level algorithm …
Pysstubs: Characterizing single-statement bugs in popular open-source python projects
AV Kamienski, L Palechor… - 2021 IEEE/ACM 18th …, 2021 - ieeexplore.ieee.org
Single-statement bugs (SStuBs) can have a severe impact on developer productivity.
Despite usually being simple and not offering much of a challenge to fix, these bugs may still …
Despite usually being simple and not offering much of a challenge to fix, these bugs may still …
Challenges in migrating imperative deep learning programs to graph execution: an empirical study
Efficiency is essential to support responsiveness wrt ever-growing datasets, especially for
Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred …
Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred …
Complex Python features in the wild
While Python is increasingly popular, program analysis tooling for Python is lagging. This is
due, in part, to complex features of the Python language---features with difficult to …
due, in part, to complex features of the Python language---features with difficult to …
Cross-language call graph construction supporting different host languages
Modern software systems are increasingly multi-lingual, which consist of components
developed in different programming languages to reuse existing libraries and com-bine …
developed in different programming languages to reuse existing libraries and com-bine …
DrPy: Pinpointing Inefficient Memory Usage in Multi-Layer Python Applications
Python has become an increasingly popular programming language, especially in the areas
of data analytics and machine learning. Many modern Python packages employ a multi …
of data analytics and machine learning. Many modern Python packages employ a multi …
Towards Safe Automated Refactoring of Imperative Deep Learning Programs to Graph Execution
R Khatchadourian, TC Vélez… - 2023 38th IEEE/ACM …, 2023 - ieeexplore.ieee.org
Efficiency is essential to support responsiveness wrt ever-growing datasets, especially for
Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred …
Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred …
On the Usefulness of Python Structural Pattern Matching: An Empirical Study
N Vánder, G Antal, P Hegedüs… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
As the important role of software in our modern world becomes more and more evident, the
need for more complex data structures is increasing. Structural pattern matching has …
need for more complex data structures is increasing. Structural pattern matching has …
Improving tese case generation for Python native libraries through constraints on input data structures
Modern Python projects execute computational functions using native libraries and give
Python interfaces to boost execution speed; hence, testing these libraries becomes critical to …
Python interfaces to boost execution speed; hence, testing these libraries becomes critical to …