Angry bots demo software#
Angry bots demo code#

However, they also have important limitations for reproducibility and data management. Scripting languages are easy to learn and use, and they allow complex tasks to be specified and executed in fewer steps than with traditional programming languages. Scripts are widely used to design and run scientific experiments. The experiments conducted on two racing games show that (1) PingUMiL outperforms classical machine learning methods and (2) representation learning can be used to detect long-range cause-and-effect relationships in only partially observed game data provenance graphs. We evaluate the generalization capacity of PingUMiL when learning from similar games and compare its performance to classical machine learning methods. The embeddings learned from the data pose as the features of a machine learning task tailored towards detecting long-range cause-and-effect relationships.

In this paper, we contribute with a framework named PingUMiL that leverages the recently proposed graph embeddings to represent game provenance graphs in a latent space. However, since game provenance data capture is guided by a set of strict predefined rules established by the game developers, the detection of long-range cause-and-effect relationships may demand huge coding efforts. Thus, game provenance graph tools have been proposed to capture cause-and-effect relationships occurring in a gameplay session to assist the game design process. To succeed in Game Analytics, it is essential to identify what is happening in a game (an effect) and track its causes. Game Analytics comprises a set of techniques to analyze both the game quality and player behavior.

Angry bots demo software#

With the help of visual provenance representations, project managers can gain an overview and insights into development progress, effects of process changes, and interactions among developers and with external contributors, which we exemplify with a case study of a software with high social relevance. To gain insights and knowledge about software development processes, we extract the provenance of development processes, especially from version control systems for Git-based software projects, and visualize the provenance information using graph visualization, metrics representation, and development timelines including an integration of these methods into a web-based dashboard. In addition to the analysis and visualization of software systems, it is useful to analyze the software development process to obtain better information about the quality, reliability, and trustworthiness of the software.

Angry bots demo code#

Software development is a complex process involving many people and development tools and their interactions during development, a lot of data such as source code, documents, or software artifacts and information such as issues, discussions, or code analyses are generated or modified. Evaluations were carried out, and some evidence was found that the framework assists in the understanding and analysis of provenance data when decision-making is needed. Visionary is an application domain-free framework adapted to any system that uses the PROV provenance model. The visualization presents and highlights inferences and results obtained with the data analysis. The framework captures the provenance data and generates new information using ontologies and structural analysis of the provenance graph. This paper presents the Visionary framework, designed to assist in the understanding and use of provenance data through ontologies, complex network analysis, and software visualization techniques. Ontology, complex networks, and software visualization can help in this process by generating new data insights and strategic information for decision-making. However, for a better understanding and use of provenance data, efficient and user-friendly mechanisms are needed.

We consider that these requirements can also be used in new application domains, such as software processes and IoT. In scientific workflows, provenance is considered essential to support experiments’ reproducibility, interpretation of results, and problem diagnosis. Provenance is recognized as a central challenge to establish the reliability and provide security in computational systems.