A Survey Assessing Github Copilot

Data Skeptic · Beginner ·💻 AI-Assisted Coding ·2y ago

Key Takeaways

Github Copilot and AI programming assistants are assessed in a survey conducted by Jenny Liang, a PhD student at Carnegie Mellon University, focusing on usability and concerns around intellectual property and code access.

Original Description

In this episode, we are joined by Jenny Liang, a PhD student at Carnegie Mellon University, where she studies the usability of code generation tools. She discusses her recent survey on the usability of AI programming assistants. Jenny discussed the method she used to gather people to complete her survey. She also shared some questions in her survey alongside vital takeaways. She shared the major reasons for developers not wanting to us code-generation tools. She stressed that the code-generation tools might access the software developers' in-house code, which is intellectual property. Learn more about Jenny Liang via https://jennyliang.me/
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Data Skeptic · Data Skeptic · 0 of 60

← Previous Next →
1 Data Skeptic book giveaway contest winner selection
Data Skeptic book giveaway contest winner selection
Data Skeptic
 2 OpenHouse - Front end and API overview
OpenHouse - Front end and API overview
Data Skeptic
 3 OpenHouse Crawling with AWS Lambda
OpenHouse Crawling with AWS Lambda
Data Skeptic
 4 [MINI] Logistic Regression on Audio Data
[MINI] Logistic Regression on Audio Data
Data Skeptic
 5 Data Provenance and Reproducibility with Pachyderm
Data Provenance and Reproducibility with Pachyderm
Data Skeptic
 6 [MINI] Primer on Deep Learning
[MINI] Primer on Deep Learning
Data Skeptic
 7 Big Data Tools and Trends
Big Data Tools and Trends
Data Skeptic
 8 [MINI] Automated Feature Engineering
[MINI] Automated Feature Engineering
Data Skeptic
 9 The Data Refuge Project
The Data Refuge Project
Data Skeptic
 10 [MINI] The Perceptron
[MINI] The Perceptron
Data Skeptic
 11 [MINI] Feed Forward Neural Networks
[MINI] Feed Forward Neural Networks
Data Skeptic
 12 Data Science at Patreon
Data Science at Patreon
Data Skeptic
 13 [MINI] Backpropagation
[MINI] Backpropagation
Data Skeptic
 14 [MINI] GPU CPU
[MINI] GPU CPU
Data Skeptic
 15 OpenHouse
OpenHouse
Data Skeptic
 16 [MINI] Generative Adversarial Networks
[MINI] Generative Adversarial Networks
Data Skeptic
 17 [MINI] AdaBoost
[MINI] AdaBoost
Data Skeptic
 18 [MINI] The Bootstrap
[MINI] The Bootstrap
Data Skeptic
 19 [MINI] Dropout
[MINI] Dropout
Data Skeptic
 20 [MINI] Gini Coefficients
[MINI] Gini Coefficients
Data Skeptic
 21 [MINI] Random Forest
[MINI] Random Forest
Data Skeptic
 22 [MINI] Heteroskedasticity
[MINI] Heteroskedasticity
Data Skeptic
 23 [MINI] ANOVA
[MINI] ANOVA
Data Skeptic
 24 Urban Congestion
Urban Congestion
Data Skeptic
 25 [MINI] The CAP Theorem
[MINI] The CAP Theorem
Data Skeptic
 26 Unstructured Data for Finance
Unstructured Data for Finance
Data Skeptic
 27 Detecting Terrorists with Facial Recognition?
Detecting Terrorists with Facial Recognition?
Data Skeptic
 28 Predictive Models on Random Data
Predictive Models on Random Data
Data Skeptic
 29 [MINI] Entropy
[MINI] Entropy
Data Skeptic
 30 [MINI] F1 Score
[MINI] F1 Score
Data Skeptic
 31 Causal Impact
Causal Impact
Data Skeptic
 32 Machine Learning on Images with Noisy Human-centric Labels
Machine Learning on Images with Noisy Human-centric Labels
Data Skeptic
 33 The Library Problem
The Library Problem
Data Skeptic
 34 Stealing Models from the Cloud
Stealing Models from the Cloud
Data Skeptic
 35 Data Science at eHarmony
Data Science at eHarmony
Data Skeptic
 36 Multiple Comparisons and Conversion Optimization
Multiple Comparisons and Conversion Optimization
Data Skeptic
 37 Election Predictions
Election Predictions
Data Skeptic
 38 [MINI] Calculating Feature Importance
[MINI] Calculating Feature Importance
Data Skeptic
 39 MS Connect Conference
MS Connect Conference
Data Skeptic
 40 Music21
Music21
Data Skeptic
 41 The Police Data and the Data Driven Justice Initiatives
The Police Data and the Data Driven Justice Initiatives
Data Skeptic
 42 Studying Competition and Gender Through Chess
Studying Competition and Gender Through Chess
Data Skeptic
 43 [MINI] Goodhart's Law
[MINI] Goodhart's Law
Data Skeptic
 44 Trusting Machine Learning Models with LIME
Trusting Machine Learning Models with LIME
Data Skeptic
 45 [MINI] Leakage
[MINI] Leakage
Data Skeptic
 46 Predictive Policing
Predictive Policing
Data Skeptic
 47 Mutli-Agent Diverse Generative Adversarial Networks
Mutli-Agent Diverse Generative Adversarial Networks
Data Skeptic
 48 [MINI] Convolutional Neural Networks
[MINI] Convolutional Neural Networks
Data Skeptic
 49 Unsupervised Depth Perception
Unsupervised Depth Perception
Data Skeptic
 50 [MINI] Max-pooling
[MINI] Max-pooling
Data Skeptic
 51 MS Build 2017
MS Build 2017
Data Skeptic
 52 Activation Functions
Activation Functions
Data Skeptic
 53 Doctor AI
Doctor AI
Data Skeptic
 54 [MINI] The Vanishing Gradient
[MINI] The Vanishing Gradient
Data Skeptic
 55 CosmosDB
CosmosDB
Data Skeptic
 56 Estimating Sheep Pain with Facial Recognition
Estimating Sheep Pain with Facial Recognition
Data Skeptic
 57 [MINI] Conditional Independence
[MINI] Conditional Independence
Data Skeptic
 58 MINI: Bayesian Belief Networks
MINI: Bayesian Belief Networks
Data Skeptic
 59 Project Common Voice
Project Common Voice
Data Skeptic
 60 [MINI] Recurrent Neural Networks
[MINI] Recurrent Neural Networks
Data Skeptic

This episode discusses a survey on the usability of AI programming assistants, specifically Github Copilot, and highlights concerns around intellectual property and code access. The survey provides insights into the reasons why developers may not want to use code-generation tools. By listening to this episode, you can learn about the current state of AI coding and the potential risks and benefits associated with using AI programming assistants.

Key Takeaways
  1. Conduct a survey to gather feedback on AI programming assistants
  2. Assess the usability of code generation tools
  3. Identify concerns around intellectual property and code access
  4. Analyze the reasons why developers may not want to use code-generation tools
💡 The survey highlights that one of the major reasons for developers not wanting to use code-generation tools is the potential risk of accessing their in-house code, which is intellectual property.

Related Reads

📰
Cursor Pricing 2026: Free vs Pro vs Ultra — Which Plan?
Learn how to choose the right Cursor plan for your coding needs and budget, and discover how this Agentic AI coding tool can boost your productivity
Dev.to AI
 📰
enable Consistent AI Coding with Persistent Context Layers
Learn how persistent context layers can improve AI coding consistency and reliability
Dev.to AI
 📰
LeetCode Isn’t Dead. Your Interview Prep Strategy Is.
Update your interview prep strategy to focus on practical skills and real-world problem-solving, as LeetCode-style interviews are evolving
Medium · Programming
 📰
Build a UGC video moderation pipeline with FFmpeg + NudeNet
Learn to build a UGC video moderation pipeline using FFmpeg and NudeNet to ensure safe and respectful user-generated content
Dev.to · Mason K
Up next
Copilot Cowork: Setup, Skills, Plugins & Pricing
Matt Tutorials
Watch →