How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow
An academic contribution to computer science becomes impactful when incorporated into a real software project. Speaking about machine learning (ML), the presence of open-source frameworks facilitates researchers to exploit and share their research output with other researchers and practitioners. However, such contributions—as other changes—need to be properly reviewed. This paper reports preliminary findings of an investigation conducted on Tensorflow aimed at analyzing how contributions originating from scientific articles are reviewed and how such a review process compares with code review of conventional software systems. We have quantitatively and qualitatively analyzed 16 cases in which ideas/solutions from articles made into TensorFlow after a pull request review, investigating (i) the nature of pull request review comments, (ii) the role of the reviewer, and (iii) the artifacts being reviewed or shared during the review process. The results show how, in line with previous investigations on the development process of ML systems, the code review process sees the interaction of data scientists and academics with software developers. At the same time, it interleaves phases assessing the scientific merits and compatibility of the article’s solution with conventional code review focused on code readability and maintainability issues.
Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | Empirical Findings, Future Visions, Recommendations Replications and Negative Results (RENE) / Early Research Achievements (ERA) / Tool Demonstration / Research Track at 205 Chair(s): Mark Hills Appalachian State University, Coen De Roover Vrije Universiteit Brussel, Gema Rodríguez-Pérez Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus | ||
11:00 10mTalk | Terminal Lucidity: Envisioning the Future of the Terminal Research Track Michael MacInnis Carleton University, Olga Baysal Carleton University, Michele Lanza Software Institute - USI, Lugano Pre-print | ||
11:10 6mTalk | Exploring Code Comprehension in Scientific Programming: Preliminary Insights from Research Scientists Early Research Achievements (ERA) Alyssia Chen University of Hawaii at Manoa, Carol Wong University of Hawaii at Manoa, Bonita Sharif University of Nebraska-Lincoln, USA, Anthony Peruma University of Hawai‘i at Mānoa Pre-print | ||
11:16 10mTalk | Method Names in Jupyter Notebooks: An Exploratory Study Research Track Carol Wong University of Hawaii at Manoa, Gunnar Larsen University of Hawaii at Manoa, Rocky Huang University of Hawaii at Manoa, Bonita Sharif University of Nebraska-Lincoln, USA, Anthony Peruma University of Hawai‘i at Mānoa | ||
11:26 6mTalk | SCALAR: A Part-of-speech Tagger for Identifiers Tool Demonstration Christian Newman , Brandon Scholten Kent State University, Sophia Testa Kent State University, Joshua Behler Kent State University, Syreen Banabilah Kent State University, Michael L. Collard The University of Akron, Michael J. Decker Bowling Green State University, Mohamed Wiem Mkaouer University of Michigan - Flint, Marcos Zampieri George mason University, Eman Abdullah AlOmar Stevens Institute of Technology, USA, Reem Alsuhaibani Prince Sultan University, Anthony Peruma University of Hawai‘i at Mānoa, Jonathan I. Maletic Kent State University | ||
11:32 6mTalk | How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow Early Research Achievements (ERA) Federica Pepe University of Sannio, Claudia Farkas York University, Maleknaz Nayebi York University, Giulio Antoniol Ecole Polytechnique de Montreal, Massimiliano Di Penta University of Sannio, Italy | ||
11:38 4mTalk | Toward Neurosymbolic Program Comprehension Early Research Achievements (ERA) Alejandro Velasco William & Mary, Aya Garryyeva William and Mary, David Nader Palacio William & Mary, Antonio Mastropaolo William and Mary, USA, Denys Poshyvanyk William & Mary Pre-print | ||
11:42 10mTalk | Combining Static Analysis Techniques for Program Comprehension Using Slicito Tool Demonstration Pre-print File Attached | ||
11:52 6mTalk | Mining Code Change Patterns in Ada Projects Replications and Negative Results (RENE) | ||
11:58 6mTalk | Telling Software Evolution Stories With Sonification Early Research Achievements (ERA) | ||
12:04 10mTalk | Attributed Multiplex Learning for Analogical Third-Party Library Recommendation and Retrieval Research Track Baihui Sang State Key Laboratory for Novel Software Technology, Nanjing University, Liang Wang Nanjing University, Jierui Zhang Nanjing University, Xianping Tao Nanjing University | ||
12:14 6mTalk | LLM2FedLLM - A Tool for Simulating Federated LLMs for Software Engineering Tasks Tool Demonstration Jahnavi Kumar Indian Institute of Technology Tirupati, India, Siddhartha Gandu Indian Institute of Technology Tirupati, Sridhar Chimalakonda Indian Institute of Technology Tirupati | ||
12:20 10mLive Q&A | Session's Discussion: "Empirical Findings, Future Visions, Recommendations" Research Track | ||