Gabriela Czibula

Software defect prediction (SDP) aims to identify defect-prone code modules in order to optimize testing resources and improve software quality. While traditional approaches rely on software metrics derived from code structure, this paper proposes , an approach that enhances code embeddings using effort-related and test coverage metrics. We introduce three feature sets: the top-30 software metrics selected through clustering relevance ranking, 170 effort-related metrics capturing development process characteristics, and five test coverage metrics derived from unit tests. Our evaluation on 15 releases of Apache Calcite and 6 releases of Apache Ant-Ivy demonstrates that effort-related and test coverage metrics provide better defect prediction performance with far fewer features than traditional software metrics. In unsupervised clustering experiments, effort-based embeddings achieve better alignment with defect labels. In supervised classification, effort-related features achieve an AUC of 0.932 on Calcite and 0.883 on Ant-Ivy, outperforming the top-30 software metrics at comparable feature count (AUC of 0.632 and 0.587, respectively). Feature importance analysis reveals that test coverage metrics are the strongest predictors of defect proneness. These findings suggest that process-oriented metrics, particularly those related to code testing and development history, capture defect patterns more effectively per feature than static code structure metrics, offering practical guidance for software quality assurance.

Accurately differentiating between fungal and bacterial infections is critical for treatment but remains challenging due to slow manual diagnostics and class imbalance in medical datasets. This paper introduces OD-Abs, a one-class outlier detection classifier based on an autoencoder architecture to distinguish infection types in microscopic images. By training solely on the reference fungal class, the model learns to identify bacterial instances as outliers. Our approach yields statistically significant improvements compared to other one-class approaches, achieving an F1-score of 0.967, an AUC of 0.980, and a specificity of 0.992. Additionally, we explore the explainability of our method by analysing reconstruction error maps from both a technical and biological perspective and conclude that the model’s decision-making aligns with biologically relevant morphological features. This proof of concept study establishes the OD-Abs autoencoder model as a highly robust tool for handling the task of classifying images of bacteria and fungi from abscesses.

Recent breakthroughs in the study of Denoising Diffusion Models have constituted a driving factor in advancing the field of Computer Vision through the aid of generative Machine Learning. Challenging problems, such as image or video synthesis, become intertwined with Natural Language Processing techniques to push the boundaries of computational capabilities and foster human ingenuity. We present an approach to solving text-guided video completion, a difficult task of producing the temporal future progression of an image, conditioned by textual description. Our proposed framework applies the latest academic leaps in Text-to-Image and Text-to-Video generation to perform Image Animation guided by text, an insufficiently explored topic due to its strenuous nature. We present how adapting generalized pre-trained Text-to-Image models can achieve good performance solving the described problem in a Zero-Shot context. Our experimental evaluation shows that the proposed solution is robust, capable of animating any source image guided by a language prompt, by inheriting the generality of the backbone pre-trained Text-to-Image model.