Bug analysis in Jupyter notebook projects An empirical study
The paper presents a systematic study of bugs and challenges that Jupyter practitioners face through a large-scale empirical investigation. The authors mined 14,740 commits from 105 GitHub open-source projects with Jupyter notebook code. Next, they analyzed 30,416 Stack Overflow posts which gave them insights into bugs that practitioners face when developing Jupyter notebook projects.
• RQ1. What types of bugs are more frequent?
• RQ2. What are the root causes of bugs?
• RQ3. What are the frequent impacts of bugs?
• RQ4. What challenges do data scientists face in practice on Jupyter Projects?
The paper presents a systematic study of bugs and challenges that Jupyter practitioners face through a large-scale empirical investigation. The authors mined 14,740 commits from 105 GitHub open-source projects with Jupyter notebook code. Next, they analyzed 30,416 Stack Overflow posts which gave them insights into bugs that practitioners face when developing Jupyter notebook projects.
• RQ1. What types of bugs are more frequent?
• RQ2. What are the root causes of bugs?
• RQ3. What are the frequent impacts of bugs?
• RQ4. What challenges do data scientists face in practice on Jupyter Projects?
UL2: Unifying Language Learning Paradigms (Google)
A novel language pre-training paradigm called Unified Language Learner (UL2) frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers that samples from a varied set of such objectives, each with different configurations.
A novel language pre-training paradigm called Unified Language Learner (UL2) frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers that samples from a varied set of such objectives, each with different configurations.
What is it like to program with artificial intelligence (Microsoft)
The authors explore how programming with large language models (LLM-assisted programming) is similar to, and differs from, prior conceptualisations of programmer assistance. They find that while LLM-assisted programming shares some properties of compilation, pair programming, and programming via search and reuse, there are fundamental differences both in the technical possibilities as well as the practical experience. Thus, LLM-assisted programming ought to be viewed as a new way of programming with its own distinct properties and challenges.
The authors explore how programming with large language models (LLM-assisted programming) is similar to, and differs from, prior conceptualisations of programmer assistance. They find that while LLM-assisted programming shares some properties of compilation, pair programming, and programming via search and reuse, there are fundamental differences both in the technical possibilities as well as the practical experience. Thus, LLM-assisted programming ought to be viewed as a new way of programming with its own distinct properties and challenges.
Forest: Structural Code Editing with Multiple Cursors (ETH)
Forest allows to perform a single action simultaneously in multiple program locations and thus support complex refactorings.
Forest allows to perform a single action simultaneously in multiple program locations and thus support complex refactorings.
Minerva: Solving Quantitative Reasoning Problems with Language Models (Google)
Minerva is a large language model pretrained on general natural language data and further trained on technical content. The main novelty of the paper is a large training dataset that juxtaposes natural language with the correct use of formal mathematical language, such as equations and diagrams. The data is collected from the arXiv preprint server and from web pages.
Minerva is a large language model pretrained on general natural language data and further trained on technical content. The main novelty of the paper is a large training dataset that juxtaposes natural language with the correct use of formal mathematical language, such as equations and diagrams. The data is collected from the arXiv preprint server and from web pages.
Code as Policies: Language Model Programs for Embodied Control (Google)
Large language models trained on code completion have been shown to be capable of synthesizing simple Python programs from docstrings. These models can be re-purposed to write robot policy code, given natural language commands.
Project website: https://code-as-policies.github.io/
Large language models trained on code completion have been shown to be capable of synthesizing simple Python programs from docstrings. These models can be re-purposed to write robot policy code, given natural language commands.
Project website: https://code-as-policies.github.io/
code-as-policies.github.io
Code as Policies
Project page for Code as Policies: Language Model Programs for Embodied Control.
Fixing Dockerfile Smells: An Empirical Study
RQ1: How do developers fix Dockerfile smells?
RQ2: Which Dockerfile smells are developers willing to address?
RQ1: How do developers fix Dockerfile smells?
RQ2: Which Dockerfile smells are developers willing to address?
DALL·E API Now Available in Public Beta
Developers can now integrate DALL·E directly into their apps and products through API.
Developers can now integrate DALL·E directly into their apps and products through API.
Openai
DALL·E API now available in public beta
Starting today, developers can begin building apps with the DALL·E API.
Microsoft sued for open-source piracy through GitHub Copilot
Programmer and lawyer Matthew Butterick has sued Microsoft, GitHub, and OpenAI, alleging that GitHub's Copilot violates the terms of open-source licenses and infringes the rights of programmers.
Apart from the license violations, Butterick also alleges that the development feature violates the following:
- GitHub's terms of service and privacy policies,
- DMCA 1202, which forbids the removal of copyright-management information,
- the California Consumer Privacy Act,
- and other laws giving rise to the related legal claims.
The complaint was submitted to the U.S. District Court of the Northern District of California, demanding the approval of statutory damages of $9,000,000,000.
Programmer and lawyer Matthew Butterick has sued Microsoft, GitHub, and OpenAI, alleging that GitHub's Copilot violates the terms of open-source licenses and infringes the rights of programmers.
Apart from the license violations, Butterick also alleges that the development feature violates the following:
- GitHub's terms of service and privacy policies,
- DMCA 1202, which forbids the removal of copyright-management information,
- the California Consumer Privacy Act,
- and other laws giving rise to the related legal claims.
The complaint was submitted to the U.S. District Court of the Northern District of California, demanding the approval of statutory damages of $9,000,000,000.
BleepingComputer
Microsoft sued for open-source piracy through GitHub Copilot
Programmer and lawyer Matthew Butterick has sued Microsoft, GitHub, and OpenAI, alleging that GitHub's Copilot violates the terms of open-source licenses and infringes the rights of code authors.
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TOSS: Revisiting Code Search in a Two-Stage Paradigm (Microsoft)
The paper proposes a combination of two main DL-based approaches to code search — a fusion of bi-encoder and cross-encoder methods. The framework achieves state-of-the-art accuracy with an overall mean reciprocal ranking score of 0.763, compared to the best baseline result on the CodeSearchNet benchmark of 0.713.
The paper proposes a combination of two main DL-based approaches to code search — a fusion of bi-encoder and cross-encoder methods. The framework achieves state-of-the-art accuracy with an overall mean reciprocal ranking score of 0.763, compared to the best baseline result on the CodeSearchNet benchmark of 0.713.
μBERT: Mutation Testing using Pre-Trained Language Models
μBERT is a mutation testing tool. It exploits CodeBERT to generate mutants. The proposed approach is compared with PiTest on fault detection and assertion inference.
μBERT is a mutation testing tool. It exploits CodeBERT to generate mutants. The proposed approach is compared with PiTest on fault detection and assertion inference.