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Found 13 papers, 6 papers with code
Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers
We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning.
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting
This classifier is then used to detect if a trained model is overfit.
Studying the Practices of Testing Machine Learning Software in the Wild
Aims: To fill this gap, we perform the first fine-grained empirical study on ML testing practices in the wild, to identify the ML properties being tested, the followed testing strategies, and their implementation throughout the ML workflow.
Bug Characterization in Machine Learning-based Systems
Based on our results, fixing ML bugs are more costly and ML components are more error-prone, compared to non-ML bugs and non-ML components respectively.
GitHub Copilot AI pair programmer: Asset or Liability?
In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks.
An Empirical Study of Challenges in Converting Deep Learning Models
Usually DL models are developed and trained using DL frameworks that have their own internal mechanisms/formats to represent and train DL models, and usually those formats cannot be recognized by other frameworks.
Bugs in Machine Learning-based Systems: A Faultload Benchmark
Although most of these tools use bugs' lifecycle, there is no standard benchmark of bugs to assess their performance, compare them and discuss their advantages and weaknesses.
Towards a Change Taxonomy for Machine Learning Systems
However, thus far little is known about the degree of collaboration activity happening on such ML research repositories, in particular regarding (1) the degree to which such repositories receive contributions from forks, (2) the nature of such contributions (i. e., the types of changes), and (3) the nature of changes that are not contributed back to forks, which might represent missed opportunities.
Towards Training Reproducible Deep Learning Models
However, DL models are challenging to be reproduced due to issues like randomness in the software (e. g., DL algorithms) and non-determinism in the hardware (e. g., GPU).
Towards a consistent interpretation of AIOps models
Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures.
Can I use this publicly available dataset to build commercial AI software? -- A Case Study on Publicly Available Image Datasets
Publicly available datasets are one of the key drivers for commercial AI software.
Empirical Study on the Software Engineering Practices in Open Source ML Package Repositories
Recent advances in Artificial Intelligence (AI), especially in Machine Learning (ML), have introduced various practical applications (e. g., virtual personal assistants and autonomous cars) that enhance the experience of everyday users.
An Exploratory Study on Machine Learning Model Stores
Recent advances in Artificial Intelligence, especially in Machine Learning (ML), have brought applications previously considered as science fiction (e. g., virtual personal assistants and autonomous cars) into the reach of millions of everyday users.
Software Engineering
