Video games represent a unique type of complex software project, despite differing in aspects such as methodologies, programming languages, and design patterns. Similar to traditional software, games can be released as open-source software (OSS) projects, thus fostering collaboration and knowledge sharing among developers. However, one of the primary challenges in this domain is the difficulty in finding source code for game development, which is often scattered across various repositories and platforms. Moreover, the lack of proper guidelines to document game projects is still missing, thus worsening this issue. In this paper, we envision a set of initial guidelines leveraging a mining-based methodology considering two different OSS platforms, i.e. itch.io and GitHub. First, we collect data from 765 open-source games from the itch.io platform and map the retrieved games to the corresponding repositories on GitHub, searching for documentation and source code. We further refine the list of games by manually analyzing the repositories, focusing on the quality of the documentation and the presence of source code, ending up with 613 games. On top of this gold set, we elicited a set of ten reproducibility guidelines specifically tailored for games. Our results show that the majority of the games do not have source code available, and the documentation quality is generally low, apart from games with high-rated GitHub projects. In addition, we provide a set of takeaways that can be further investigated by extending the provided guidelines. We believe that our dataset and methodology can be used as a starting point for future research in this domain, providing insights into the challenges and opportunities for assisting newcomers to game development using open-source projects.

Being predominant in digital entertainment for decades, video games have been recognized as valuable software artifacts by the software engineering (SE) community just recently. Such an acknowledgment has unveiled several research opportunities, spanning from empirical studies to the application of AI techniques for classification tasks. In this respect, several curated game datasets have been disclosed for research purposes even though the collected data are insufficient to support the application of advanced models or to enable interdisciplinary studies. Moreover, the majority of those are limited to PC games, thus excluding notorious gaming platforms, e.g., PlayStation, Xbox, and Nintendo. In this paper, we propose PlayMyData, a curated dataset composed of 99,864 multi-platform games gathered by the IGDB website. By exploiting a dedicated API, we collect relevant metadata for each game, e.g., description, genre, rating, gameplay video URLs, and screenshots. Furthermore, we enrich PlayMyData with the timing needed to complete each game by mining the HLTB website. To the best of our knowledge, this is the most comprehensive dataset in the domain that can be used to support different automated tasks in SE. More importantly, PlayMyData can be used to foster cross-domain investigations built on top of the provided multimedia data.

Cross-platform mobile app development frame-works allow developers to use a single codebase to develop apps targeting different platforms. As these frame-works provide distinct features and may impact the apps' quality, their selection must be done with care. Although many works evaluated mobile frame-works, there is no synthesis on these studies. In this paper, we present a Systematic Literature Review (SLR) on approaches that evaluated cross- platform frame-works. Our SLR covers 75 papers and provides insights on 1) the most studied frame-works, 2) the criteria used for evaluation, 3) the evaluation methods used and 4) the results of these evaluations. The SLR shows that prior works generally used a prototype app to evaluate the frame-works but none explored the impact of the frame-works on the app's code quality. Thus, we carried out a preliminary empirical study on 3,566 mobile apps to evaluate the impact of mobile frame-works on the number of bugs and code smells in apps. The results of the study on native Android and React Native indicate that the latter has fewer code smells than native Android apps. Native Android apps generally had worse quality considering the number of bugs and code smells.

Machine Learning (ML) has revolutionized the field of computer software development, enabling data-based predictions and decision-making across several domains. Following modern software development practices, developers use third-party libraries—e.g., Scikit Learn, TensorFlow, and PyTorch—to integrate ML-based functionalities into their applications. Due to the complexity inherent in ML techniques, the models available in the APIs of these tools often require an extensive list of arguments to be set up. Library maintainers overcome this issue by defining default values for most of these arguments so developers can use ML models in their client applications effortlessly. By relying on these default arguments, the clients inadvertently depend on the value defined in these parameters to keep running as expected. We interpret this problem as a semantical breaking change variant, which we named Default Argument Breaking Change (DABC). In this work, we leverage 77 DABCs in Scikit Learn—a well-known ML library—and investigate how 194K client applications are vulnerable to them. Our results show that 72 DABCs (93%) are responsible for exposing 67,747 clients (35%). We also detected that most DABCs (61, 79%) involve APIs used in ML model training and model evaluation stages. Finally, we discuss the importance of managing DABCs in third-party ML libraries and provide insights for developers to mitigate the potential impact of these changes in their applications.

Video-game projects are notorious for having day- one bugs, no matter how big their budget or team size. The quality of a game is essential for its success. This quality could be assessed and ensured through testing. However, to the best of our knowledge, little is known about video-game testing. In this paper, we want to understand how game developers perform game testing. We investigate, through a survey, the academic and gray literature to identify and report on existing testing processes and how they could automate them. We found that game developers rely, almost exclusively, upon manual play-testing and the testers' intrinsic knowledge. We conclude that current testing processes fall short because of their lack of automation, which seems to be the natural next step to improve the quality of games while maintaining costs. However, the current game-testing techniques may not generalize to different types of games.

Different from traditional software development, there is little information about the software-engineering process and techniques in video-game development. One popular way to share knowledge among the video-game developers' community is the publishing of postmortems, which are documents summarizing what happened during the video-game development project. However, these documents are written without formal structure and often providing disparate information. Through this paper, we provide developers and researchers with grounded dataset describing software-engineering problems in video-game development extracted from postmortems. We created the dataset using an iterative method through which we manually coded more than 200 postmortems spanning 20 years (1998 to 2018) and extracted 1,035 problems related to software engineering while maintaining traceability links to the postmortems. We grouped the problems in 20 different types. This dataset is useful to understand the problems faced by developers during video-game development, providing researchers and practitioners a starting point to study video-game development in the context of software engineering.