GitHub Actions (GHA), a powerful Continuous Integration and Continuous Deployment (CI/CD) service, has revolutionized the way developers automate tasks in the software development pipeline. Although GHA provides great convenience, if a GHA build fails, the time spent waiting for results and debugging is wasted, which can seriously affect development efficiency. In this study, we delve into GHA build results and introduce an automatic framework named GHA-BFP that uses ML models to predict the failure of GHA builds. Using GHA-BFP with Random Forest model, we achieved the highest performance in predicting the failure of GHA builds, with all key metrics (i.e., Accuracy, Precision, Recall, and F1 score) exceeding 75%. Furthermore, through ablation experiments, we have verified the essentiality of the four categories of input features. Lastly, we conducted an assessment of the importance of each individual input feature in relation to the model's predictive capabilities.

Experience precedes understanding. Humans constantly explore and learn about their environment out of curiosity, gather information, and update their models of the world. On the other hand, machines are either trained to learn passively from static and fixed datasets, or taught to complete specific goal-conditioned tasks. To encourage the development of exploratory interactive agents, we present the EXCALIBUR benchmark. EXCALIBUR allows agents to explore their environment for long durations and then query their understanding of the physical world via inquiries like: “is the small heavy red bowl made from glass?” or “is there a silver spoon heavier than the egg?”. This design encourages agents to perform free-form home exploration without myopia induced by goal conditioning. Once the agents have answered a series of questions, they can renter the scene to refine their knowledge, update their beliefs, and improve their performance on the questions. Our experiments demonstrate the challenges posed by this dataset for the present-day state-of-the-art embodied systems and the headroom afforded to develop new innovative methods. Finally, we present a virtual reality interface that enables humans to seamlessly interact within the simulated world and use it to gather human performance measures. EXCALIBUR affords unique challenges in comparison to presentday benchmarks and represents the next frontier for embodied AI research.