no code implementations • 16 Apr 2024 • Ahmed E. Hassan, Gustavo A. Oliva, Dayi Lin, Boyuan Chen, Zhen Ming, Jiang
We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning.
1 code implementation • 18 Jan 2024 • Hao Li, Gopi Krishnan Rajbahadur, Dayi Lin, Cor-Paul Bezemer, Zhen Ming, Jiang
This classifier is then used to detect if a trained model is overfit.
1 code implementation • 19 Dec 2023 • Moses Openja, Foutse khomh, Armstrong Foundjem, Zhen Ming, Jiang, Mouna Abidi, Ahmed E. Hassan
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.
1 code implementation • 26 Jul 2023 • Mohammad Mehdi Morovati, Amin Nikanjam, Florian Tambon, Foutse khomh, Zhen Ming, Jiang
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.
1 code implementation • 30 Jun 2022 • Arghavan Moradi Dakhel, Vahid Majdinasab, Amin Nikanjam, Foutse khomh, Michel C. Desmarais, Zhen Ming, Jiang
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.
1 code implementation • 28 Jun 2022 • Moses Openja, Amin Nikanjam, Ahmed Haj Yahmed, Foutse khomh, Zhen Ming, Jiang
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.
no code implementations • 24 Jun 2022 • Mohammad Mehdi Morovati, Amin Nikanjam, Foutse khomh, Zhen Ming, Jiang
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.
no code implementations • 21 Mar 2022 • Aaditya Bhatia, Ellis E. Eghan, Manel Grichi, William G. Cavanagh, Zhen Ming, Jiang, Bram Adams
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.
1 code implementation • 4 Feb 2022 • Boyuan Chen, Mingzhi Wen, Yong Shi, Dayi Lin, Gopi Krishnan Rajbahadur, Zhen Ming, Jiang
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).
no code implementations • 4 Feb 2022 • Yingzhe Lyu, Gopi Krishnan Rajbahadur, Dayi Lin, Boyuan Chen, Zhen Ming, Jiang
Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures.
no code implementations • 3 Nov 2021 • Gopi Krishnan Rajbahadur, Erika Tuck, Li Zi, Dayi Lin, Boyuan Chen, Zhen Ming, Jiang, Daniel M. German
Publicly available datasets are one of the key drivers for commercial AI software.
no code implementations • 2 Dec 2020 • Minke Xiu, Ellis E. Eghan, Zhen Ming, Jiang, Bram Adams
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.
no code implementations • 25 May 2019 • Minke Xiu, Zhen Ming, Jiang, Bram Adams
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