Emergent, crowd-scale programming practice in the IDE

By Ethan Fast, Daniel Steffee, Lucy Wang, Lucy Lu Wang, Joel Brandt, Michael S. Bernstein. 2014

42 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Information Systems and Management, Program Analysis & Verification, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Ranjay Krishna
2. Li Fei-Fei
3. Kenji Hata
4. Ethan Fast
5. Yue Jia
6. Mark Harman
7. Ke Mao
8. Michael S. Bernstein

In Information Systems

1. Yue Jia
2. Ke Mao
3. Mark Harman
4. Michael S. Bernstein
5. Ethan Fast
6. Licia Capra
7. Li Fei-Fei
8. Phil McMinn

In Information Systems and Management

1. Mark Harman
2. Michael S. Bernstein

In Program Analysis & Verification

1. Yue Jia
2. Mark Harman

In Human-Computer Interaction

1. Ethan Fast
2. Michael S. Bernstein
3. Li Fei-Fei

Citations in the corpus

• A survey of the use of crowdsourcing in software engineering (2017, methodology): “Evaluations were performed on each of the proposed tools, revealing that the concept of Web 2.0 can be leveraged to improve the developer’s IDE. Fast et al. (2014) conducted a study that echoes the idea of1070 IDE 2.0.”
• Empath: Understanding Topic Signals in Large-Scale Text (2016, background): “In other research communities, systems have used unsupervised models to capture emergent practice in open source code [11] or design [20].”
• Embracing Error to Enable Rapid Crowdsourcing (2016, background): “Social science [28, 41], interactive systems [15, 30] and machine learning [13, 38] are becoming more and more reliant on large-scale, human-annotated data.”