Proceedings of the 1st International Conference on Live Coding

Open Access peer reviewed papers on live coding published at the 1st International Conference on Live Coding (ICLC) in Leeds

The Live Programming Lecturing Technique: A Study of the Student Experience in Introductory and Advanced Programming Courses

This paper investigates the topic of teaching programming in higher education. The teaching method often referred to as live programming has become a widely applied lecturing strategy for teaching programming subjects in an interactive fashion. Lectures based on live programming normally involve live demonstrations, explanations and interaction with the students. Although this technique seems to be very popular amongst students and instructors, we hypothesise that it also involves potential challenges. In this paper, we investigate the perceived difficulty and promise of following such an approach from a student perspective. We present results from interviews with 1st and 2nd year IT Bachelor students about their experience with live programming. Our results indicate that students’ engagement and desire to learn through active learning techniques still are very much valid also in introductory and advanced programming courses. Furthermore, we also interpret from our findings a suggested model of a repeated cycle of lecture, demo and exercise as highly beneficial to the student learning process

Live & Local Schema Change: Challenge Problems

Schema change is an unsolved problem in both live programming and local-first software. We include in schema change any change to the expected shape of data, whether that is expressed explicitly in a database schema or type system, or whether those expectations are implicit in the behavior of the code. Schema changes during live programming can create a mismatch between the code and data in the running environment. Similarly, schema changes in local-first programming can create mismatches between data in different replicas, and between data in a replica and the code colocated with it. In all of these situations the problem of schema change is to migrate or translate existing data in coordination with changes to the code. This paper contributes a set of concrete scenarios involving schema change that are offered as challenge problems to the live programming and local-first communities. We hope that these problems will spur progress by providing concrete objectives and a basis for comparing alternative solutions.Comment: To appear at LIVE Programming Workshop, October 24, 2023, ACM SIGPLAN conference on Systems, Programming, Languages, and Applications (SPLASH) Cascais, Portuga

DataTV 2019: 1st International Workshop on Data-Driven Personalisation of Television

The first international workshop on Data-driven Personalisation of Television aims to highlight the significantly growing importance of data in the support of new television content consumption experiences. This includes automatic video summarization, dynamic insertion of content into media streams and object based media broadcasting, to serve the recommendation of TV content and personalization in media delivery. The workshop has two keynote talks alongside five paper presentations and several related demos.info:eu-repo/semantics/publishedVersio

Validating AI-Generated Code with Live Programming

AI-powered programming assistants are increasingly gaining popularity, with GitHub Copilot alone used by over a million developers worldwide. These tools are far from perfect, however, producing code suggestions that may be incorrect in subtle ways. As a result, developers face a new challenge: validating AI's suggestions. This paper explores whether Live Programming (LP), a continuous display of a program's runtime values, can help address this challenge. To answer this question, we built a Python editor that combines an AI-powered programming assistant with an existing LP environment. Using this environment in a between-subjects study (N=17), we found that by lowering the cost of validation by execution, LP can mitigate over- and under-reliance on AI-generated programs and reduce the cognitive load of validation for certain types of tasks.Comment: 8 pages, 4 figure

Music Information Retrieval in Live Coding: A Theoretical Framework

The work presented in this article has been partly conducted while the first author was at Georgia Tech from 2015–2017 with the support of the School of Music, the Center for Music Technology and Women in Music Tech at Georgia Tech. Another part of this research has been conducted while the first author was at Queen Mary University of London from 2017–2019 with the support of the AudioCommons project, funded by the European Commission through the Horizon 2020 programme, research and innovation grant 688382. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Music information retrieval (MIR) has a great potential in musical live coding because it can help the musician–programmer to make musical decisions based on audio content analysis and explore new sonorities by means of MIR techniques. The use of real-time MIR techniques can be computationally demanding and thus they have been rarely used in live coding; when they have been used, it has been with a focus on low-level feature extraction. This article surveys and discusses the potential of MIR applied to live coding at a higher musical level. We propose a conceptual framework of three categories: (1) audio repurposing, (2) audio rewiring, and (3) audio remixing. We explored the three categories in live performance through an application programming interface library written in SuperCollider, MIRLC. We found that it is still a technical challenge to use high-level features in real time, yet using rhythmic and tonal properties (midlevel features) in combination with text-based information (e.g., tags) helps to achieve a closer perceptual level centered on pitch and rhythm when using MIR in live coding. We discuss challenges and future directions of utilizing MIR approaches in the computer music field