The rapid development of large language models such as ChatGPT have made them particularly useful to developers in generating code snippets for their projects. To understand how ChatGPT's generated code is leveraged by developers, we conducted an empirical study of 3,044 ChatGPT-generated code snippets integrated within GitHub projects. A median of 54% of the generated lines of code is found in the project's code and this code typically remains unchanged once added. The modifications of the 76 code snippets that changed in a subsequent commit, consisted of minor functionality changes and code reorganizations that were made within a day. Our findings offer insights that help drive the development of AI-assisted programming tools. We highlight the importance of making changes in ChatGPT code before integrating it into a project.

Gameplay videos contain rich information about how players interact with the game and how the game responds. Sharing gameplay videos on social media platforms, such as Reddit, has become a common practice for many players. Often, players will share game-play videos that showcase video game bugs. Such gameplay videos are software artifacts that can be utilized for game testing, as they provide insight for bug analysis. Although large repositories of gameplay videos exist, parsing and mining them in an effective and structured fashion has still remained a big challenge. In this paper, we propose a search method that accepts any English text query as input to retrieve relevant videos from large repositories of gameplay videos. Our approach does not rely on any external information (such as video metadata); it works solely based on the content of the video. By leveraging the zero-shot transfer capabilities of the Contrastive Language-Image Pre-Training (CLIP) model, our approach does not require any data labeling or training. To evaluate our approach, we present the GamePhysics dataset consisting of 26,954 videos from 1,873 games, that were collected from the GamePhysics section on the Reddit website. Our approach shows promising results in our extensive analysis of simple queries, compound queries, and bug queries, indicating that our approach is useful for object and event detection in gameplay videos. An example application of our approach is as a gameplay video search engine to aid in reproducing video game bugs. Please visit the following link for the code and the data: https://asgaardlab.github.io/CLIPxGamePhysics/

Single-statement bugs (SStuBs) can have a severe impact on developer productivity. Despite usually being simple and not offering much of a challenge to fix, these bugs may still disturb a developer's workflow and waste precious development time. However, few studies have paid attention to these simple bugs, focusing instead on bugs of any size and complexity. In this study, we explore the occurrence of SStuBs in some of the most popular open-source Python projects on GitHub, while also characterizing their patterns and distribution. We further compare these bugs to SStuBs found in a previous study on Java Maven projects. We find that these Python projects have different SStuB patterns than the ones in Java Maven projects and identify 7 new SStuB patterns. Our results may help uncover the importance of understanding these bugs for the Python programming language, and how developers can handle them more effectively.

Performance regressions, such as a higher CPU utilization than in the previous version of an application, are caused by software application updates that negatively affect the performance of an application. Although a plethora of mining software repository research has been done to detect such regressions, research tools are generally not readily available to practitioners. Application Performance Management (APM) tools are commonly used in practice for detecting performance issues in the field by mining operational data.

Developers leverage logs for debugging, performance monitoring and load testing. The increased dependence on logs has lead to the development of numerous logging libraries which help developers in logging their code. As new libraries emerge and current ones evolve, projects often migrate from an older library to another one.