Tootfinder

Thought experiment: Most programming languages were designed in an era where mostly one programmer coded in isolation. If you were designing a language for modern collaborative programming/commenting/etc., what would you change? How would you *modify* existing languages?

The Autonomous Data Language -- Concepts, Design and Formal Verification
Tom T. P. Franken, Thomas Neele, Jan Friso Groote
https://arxiv.org/abs/2506.19457

The Autonomous Data Language -- Concepts, Design and Formal Verification
Nowadays, the main advances in computational power are due to parallelism. However, most parallel languages have been designed with a focus on processors and threads. This makes dealing with data and memory in programs hard, which distances the implementation from its original algorithm. We propose a new paradigm for parallel programming, the data-autonomous paradigm, where computation is performed by autonomous data elements. Programs in this paradigm are focused on making the data collaborate…

Anysphere launches Bugbot, an AI-powered tool that integrates with GitHub to detect coding errors introduced by humans or AI agents, for $40 per month per user (Lauren Goode/Wired)
https://www.wired.com/story/cursor-releases-new-ai-tool-for-debugging-code/

Cursor’s New Bugbot Is Designed to Save Vibe Coders From Themselves
One of the most popular platforms for AI-assisted programming says the next era of vibe coding is all about supercharging error detection.

"We think of generating source code from a prompt as an AI-powered feature of modern IDEs, but the general problem has a rich history in research efforts and domain-specific programming systems." Join William Benton at this year's Berlin Buzzwords to hear him talk about the history of program synthesis, its relationship to the history of AI, and the lessons we can learn from it today.
Learn more:

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing
Noah van der Vleuten
https://arxiv.org/abs/2507.15889 https://

Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing
Language models for program synthesis are usually trained and evaluated on programming competition datasets (MBPP, APPS). However, these datasets are limited in size and quality, while these language models are extremely data hungry. Additionally, the language models have a misaligned program synthesis process compared to humans. While humans iteratively develop code with the help of a compiler, most program synthesis models currently produce code in one go. To solve these issues, we introduce …

Higher-Order Behavioural Conformances via Fibrations
Henning Urbat
https://arxiv.org/abs/2507.18509 https://arxiv.org/pdf/2507.18509

Higher-Order Behavioural Conformances via Fibrations
Coinduction is a widely used technique for establishing behavioural equivalence of programs in higher-order languages. In recent years, the rise of languages with quantitative (e.g.~probabilistic) features has led to extensions of coinductive methods to more refined types of behavioural conformances, most notably notions of behavioural distance. To guarantee soundness of coinductive reasoning, one needs to show that the behavioural conformance at hand forms a program congruence, i.e. it is suit…

Generating Shakespeare-like text with an n-gram language model is straight forward and quite simple. But, don't expect to much of it. It will not be able to recreate a lost Shakespear play for you ;-) It's merely a parrot, making up well sounding sentences out of fragments of original Shakespeare texts...
#ise2025

The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students
Emma R. Dodoo, Tamara Nelson-Fromm, Mark Guzdial
https://arxiv.org/abs/2506.14147

The Teacher's Dilemma: Balancing Trade-Offs in Programming Education for Emergent Bilingual Students
K-12 computing teachers must navigate complex trade-offs when selecting programming languages and instructional materials for classrooms with emergent bilingual students. While they aim to foster an inclusive learning environment by addressing language barriers that impact student engagement, they must also align with K-12 computer science curricular guidelines and prepare students for industry-standard programming tools. Because programming languages predominantly use English keywords and most…

YATE: The Role of Test Repair in LLM-Based Unit Test Generation
Michael Konstantinou, Renzo Degiovanni, Jie M. Zhang, Mark Harman, Mike Papadakis
https://arxiv.org/abs/2507.18316

YATE: The Role of Test Repair in LLM-Based Unit Test Generation
Recent advances in automated test generation utilises language models to produce unit tests. While effective, language models tend to generate many incorrect tests with respect to both syntax and semantics. Although such incorrect tests can be easily detected and discarded, they constitute a "missed opportunity" -- if fixed, they are often valuable as they directly add testing value (they effectively target the underlying program logic to be tested) and indirectly form good seeds for generating…

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Masudul Hasan Masud Bhuiyan, Gianluca De Stefano, Giancarlo Pellegrino, Cristian-Alexandru Staicu
https://arxiv.org/abs/2506.18191

Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
Static analysis plays a key role in finding bugs, including security issues. A critical step in static analysis is building accurate call graphs that model function calls in a program. However, due to hard-to-analyze language features, existing call graph construction algorithms for JavaScript are neither sound nor complete. Prior work shows that even advanced solutions produce false edges and miss valid ones. In this work, we assist these tools by identifying missed call edges. Our main idea i…