https://seg.inf.unibe.ch/keywords/unsupervised-learning/Recent content in Unsupervised Learning on Software Engineering GroupHugoen-USSoftware Engineering Group (SEG), [Institute of Computer Science](https://www.inf.unibe.ch/), [University of Bern](https://www.unibe.ch/). All rights reserved.https://seg.inf.unibe.ch/teaching/student-projects/current/t0096-unsupervised-anomaly-detection-for-merge-conflictresolution/Mon, 01 Jan 0001 00:00:00 +0000https://seg.inf.unibe.ch/teaching/student-projects/current/t0096-unsupervised-anomaly-detection-for-merge-conflictresolution/This topic investigates unsupervised anomaly detection for merge conflict resolution in software engineering. Existing approaches generally formulate conflict resolution as supervised classification over predefined resolution labels. However, even a large labeled dataset may contain rare, unusual, or incorrectly reconstructed cases, and the labels may not adequately represent the full diversity of merge behavior. This project will develop methods to identify anomalous merge conflicts using the available conflict representations while initially ignoring the nine resolution labels. The objective is to determine whether anomaly scores can identify unusual conflict structures, rare resolution patterns, or cases that are difficult for conventional classifiers.https://seg.inf.unibe.ch/teaching/student-projects/current/t0097-validating-the-correctness-of-merge-conflict-resolution-labels/Mon, 01 Jan 0001 00:00:00 +0000https://seg.inf.unibe.ch/teaching/student-projects/current/t0097-validating-the-correctness-of-merge-conflict-resolution-labels/This topic investigates the quality and correctness of nine automatically assigned labels for merge-conflict resolutions. The labels were reconstructed by replaying historical Git merges and mapping the tentative conflict representation to the final developer-committed file. Such mappings are reproducible and scalable, but they can be affected by formatting changes, reordered code, structural edits, ambiguous correspondence between conflict chunks, and compound resolutions. The project will develop methods to detect potentially incorrect, ambiguous, or unstable labels and will validate a sample of these cases through systematic inspection. The work combines supervised, unsupervised, and weakly supervised methods for label-quality assessment.