Although existing work demonstrates that the usage of Scratch strengthens programming skills, little is known about the effect of children encouraging or criticizing each other when programming on the development of their programming skills. To address this gap, we conducted a data analysis to reveal the correlation (or lack thereof) between the sentiment of comments on a Scratch project and the creator's inclination to continue producing projects.

In particular, using a known dataset of Scratch projects, which we enriched with new projects, we examined several independent variables that capture sentiment on comments from different perspectives, e.g. the absolute or relative number of comments with a particular sentiment score that a user receives, or the ratio of comments that fall in a certain category. We also examined several dependent variables that capture the creator's inclination to continue producing projects, e.g. the number of projects they create or the total time that they are active on the platform.

The results of our experiments reveal that the absolute and relative number of comments with a specific sentiment score given to a user have a low correlation to the time that a user is active on the platform. Additionally, the ratio of the number of positive and negative comments over the total number of comments given to a user has a low correlation to the time that a user is active on the platform. Finally, the number of comments that a user receives has a low correlation to the number of projects a user creates.

The Continuous Integration (CI) practice, has been rapidly growing and developing ever since it's introduction. This practice has been constantly providing benefits to developers such as early bug detection and feedback to development teams. In this study, we aim to identify the descriptive metrics that best illustrate the performance of the CI build stage, regarded as heart of the development process.

We conduct a small case study on repositories utilizing GitHub Actions, a CI tool that is relatively unexplored. Within this context, we classify projects using two performance indicators: build breakages and build durations. We examine two distinct sets of metrics in our analysis. The first set being build level metrics, which are closely linked to the build stage. The second set including project level metrics.

Our findings suggest that patterns traditionally associated with low breakages and durations are applicable to repositories employing GitHub Actions. However, understanding the relationship between project level metrics demands a more comprehensive approach, necessitating a thorough analysis of the project context for a holistic understanding of build performance.

Using AI to support programming has recently gained a lot of popularity. Researchers have been developing tools to support programming activities using GPT models such as ChatGPT and Codex In this paper, we present the most common pro-gramming activities that these models can support. We show that they have a varying range of success
across solving Code Generation, Code Explana-tion,and Data Visualisation problems, but are often able to solve around 50% of problems on the first try. Multiple tries can raise these averages to 75%. Additionally, specialized tools using GPT models have seen promising results regarding Data Visuali-sation, Software Vulnerability Detection, and Gen-
eral Programming Support. This shows a promis-ing trend, and can mean we will all be pair pro-gramming with AI in the near-future.

Continuous integration (CI) is a software engineering practice that promotes frequent code integration into a shared repository, improving the productivity within development teams as well as the quality of the software being developed. While CI adoption has gained traction, studies have examined its effective implementation and associated challenges. The idea that multiple contextual factors influence the adoption of CI prompts an exploration of suitable descriptive metrics for describing the CI practices employed. This paper aims to explore the metrics that best depict the level of maturity of a project, addressing the question: "What metrics can be used to describe the maturity level of a project?". With a lack of a comprehensive maturity framework, we leverage GitHub's API in an attempt to analyze various metrics to be used to create a framework for filtering projects.
Our findings indicate that project maturity cannot be captured by a single metric, but rather a combination of metrics reflecting different aspects throughout the project's lifecycle. Activity levels, including commits and pull requests, popularity indicators like stargazers, forks, and contributors, as well as repository size and age, emerge as primary indicators of maturity. By combining these metrics, a unified framework for categorizing mature projects can be established and further developed.

The recent emergence of AI-driven code generation models can potentially transform programming education. To pinpoint the current state of research on using AI code generators to support learning and teaching programming, we conducted a systematic literature review with 21 papers published since 2018. The review presents the teaching and learning practices in programming education that utilize these models, the characteristics and performance indicators of the code generation models, and aspects to be considered when utilizing the models in programming education, including the risks and challenges of using code generation models for educational practices. AI code generators can be an assistive tool for both learners and instructors if the risks are mitigated.

While continuous integration has already been proven to positively affect software development, little is known about how it should be implemented based on project context. This paper investigates how CI pipelines are configured by analysing data mined from software projects on GitHub. This re- search has shown the continued rise of the CI plat- form GitHub Actions, which enables developers to broaden CI pipelines’ functionality due to great in- tegration into GitHub. Moreover, key differences between how jobs within pipelines are structured in Travis CI and GitHub Actions are outlined. These results can be used in future research, which will be aimed at connecting project context to CI setup with the goal of informing developers on maturing their CI configuration.