3 posts tagged with "paper"

The efficiency of a Pull Request (PR) process hinges on how quickly maintainers and contributors respond to each other. Knowing how long this might take can improve interactions and manage expectations.

Our new study introduces a machine-learning method to predict these response times by analyzing data from 20 popular open-source GitHub projects. We examine various features of the projects and PRs, and identified key factors that influence response times.

• PRs submitted earlier in the week, with a moderate number of commits and clear descriptions, tend to get quicker responses.
• Contributors who are more engaged and have a good track record also tend to respond faster.
• We also highlight how understanding and predicting response times can enhance the PR review process.

Finding performance regressions usually require the execution of long and costly performance test suites. This is because performance tests often have to test the system end-to-end. Could we reduce the testing costs by testing locally (e.g., module, a service, method) and using a model to predict the impact of local changes no the system as a whole?

Our new paper proposes exactly this! The paper entitled "Early Detection of Performance Regressions by Bridging Local Performance Data and Architectural Models" has been accepted at 47th IEEE/ACM International Conference on Software Engineering (ICSE 2025).

We are currently finalizing the camera-ready version of the paper, and we will share the preprint soon. Stay tuned for more updates!

Are you interested in training Chatbots for Software Engineering tasks? Our paper "A Transformer-based Approach for Augmenting Software Engineering Chatbots Datasets" has been accepted at 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2024).

• In this paper, we propose an approach to augment Chatbot training datasets tailored for Sofdrware Engineering tasks.