The GEODES Seminar aims to bring together members of the group in a semi-formal setting with a semi-regular timing.
We'd especially love to see students at all levels reporting their progress, entertain presentations on core or adjacent topics, host dry-runs before conferences, etc.
These events follow an in-person format. By that, we hope to re-establish the everyday social aspects of the research group in a post-COVID world.

  • Format: In person
  • Location: Pavillon Andre-Aisenstadt

Upcoming talks

There are no upcoming events scheduled currently. Check back later.

Previous talks this season

  • Characterizing and building datasets of software repositories

    Obtaining a relevant dataset is central to conducting empirical studies in software engineering. However, in the context of mining software repositories, the lack of appropriate tooling for large scale mining tasks hinders the creation of new datasets. Moreover, limitations related to data sources that change over time (e.g., code bases) and the lack of documentation of extraction processes make it difficult to reproduce datasets over time. This threatens the quality and reproducibility of empirical studies. In this talk, I will present a tool-supported approach facilitating the creation of large tailored datasets while ensuring their reproducibility. I will discuss limitations researchers face when creating or reproducing datasets and how the proposed approach may help overcoming these limitations.

    2023-03-24@11h00 Library, Pav. André-Aisenstadt
  • A Multi-Step Learning Approach to Assist Code Review

    Modern code review is a process for early detection and reduction of issues, which assists in ensuring the quality of the source code, detecting anomalies, and identifying potential improvements. However, this is a highly manual activity that requires a lot of resources and time. Recent research has addressed these problems by attempting to entirely automate this task (i.e., generating code reviews). However, we do believe that dismissing the reviewer from this process is not the best option in terms of its optimal functioning, especially considering the high error rates in the proposed approaches. Furthermore, this full automation is still too far to achieve given the complexity of the task that requires human intelligence. In this work, we aim to assist the reviewer in the code review process. We propose an approach for detecting the type of issue and locating parts of the code that need to be revised by developers. In the first phase, we propose a meta-learner that combines a learning-based model and a knowledge-based model to predict the type of issue from the review comment. Then, we use this component to create and label a large dataset composed of quadruplets . We use this data set to fine-tune a pre-trained language model to predict the types of issues (e.g., naming, resource handling, etc.), that need to be addressed in the original code snippet. Furthermore, we fine-tune another pre-trained language model to locate these issues in the source code submitted by developers. We evaluate the performance of our approach using a test set not considered during the training. Our results show that our model accurately locates and predicts the types of issues.

    2023-03-15@11h00 PAA3195, Pav. André-Aisenstadt

Previous talks in previous seasons


Got an interesting topic you'd like to present? Reach out to your favorite postdoc for details.