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Full-Text Articles in Physical Sciences and Mathematics
An Empirical Study Towards Characterizing Deep Learning Development And Deployment Across Different Frameworks And Platforms, Qianyu Guo, Sen Chen, Xiaofei Xie, Lei Ma, Qiang Hu, Hongtao Liu, Yang Liu, Jianjun Zhao, Xiaohong Li
An Empirical Study Towards Characterizing Deep Learning Development And Deployment Across Different Frameworks And Platforms, Qianyu Guo, Sen Chen, Xiaofei Xie, Lei Ma, Qiang Hu, Hongtao Liu, Yang Liu, Jianjun Zhao, Xiaohong Li
Research Collection School Of Computing and Information Systems
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment. Till now, there is no study on how various mainstream frameworks and platforms influence both DL software development and deployment in practice.To fill this gap, we take the first step towards understanding how the most widely-used DL frameworks and platforms support the DL software development and deployment. We conduct a systematic study on these frameworks …
How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy
How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy
Research Collection School Of Computing and Information Systems
Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). …
Why Is My Code Change Abandoned?, Qingye Wang, Xin Xia, David Lo, Shanping Li
Why Is My Code Change Abandoned?, Qingye Wang, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Software developers contribute numerous changes every day to the code review systems. However, not all submitted changes are merged into a codebase because they might not pass the code review process. Some changes would be abandoned or be asked for resubmission after improvement, which results in more workload for developers and reviewers, and more delays to deliverables. To understand the underlying reasons why changes are abandoned, we conduct an empirical study on the code review of four open source projects (Eclipse, LibreOffice, OpenStack, and Qt).First, we manually analyzed 1459 abandoned changes. Second, we leveraged the open card sorting method to …
On Reliability Of Patch Correctness Assessment, Xuan-Bach D. Le, Lingfeng Bao, David Lo, Xin Xia, Shanping Li, Corina S. Pasareanu
On Reliability Of Patch Correctness Assessment, Xuan-Bach D. Le, Lingfeng Bao, David Lo, Xin Xia, Shanping Li, Corina S. Pasareanu
Research Collection School Of Computing and Information Systems
Current state-of-the-art automatic software repair (ASR) techniques rely heavily on incomplete specifications, or test suites, to generate repairs. This, however, may cause ASR tools to generate repairs that are incorrect and hard to generalize. To assess patch correctness, researchers have been following two methods separately: (1) Automated annotation, wherein patches are automatically labeled by an independent test suite (ITS) – a patch passing the ITS is regarded as correct or generalizable, and incorrect otherwise, (2) Author annotation, wherein authors of ASR techniques manually annotate the correctness labels of patches generated by their and competing tools. While automated annotation cannot ascertain …
Characterizing And Identifying Reverted Commits, Meng Yan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li
Characterizing And Identifying Reverted Commits, Meng Yan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
In practice, a popular and coarse-grained approach for recovering from a problematic commit is to revert it (i.e., undoing the change). However, reverted commits could induce some issues for software development, such as impeding the development progress and increasing the difficulty for maintenance. In order to mitigate these issues, we set out to explore the following central question: can we characterize and identify which commits will be reverted? In this paper, we characterize commits using 27 commit features and build an identification model to identify commits that will be reverted. We first identify reverted commits by analyzing commit messages and …