The development of reliable software depends heavily on the effective collaboration between teams responsible for development and testing. Despite ongoing efforts, many software programs still contain bugs that can lead to financial losses and business risks. Therefore, detecting and fixing software defects after release is crucial. While binary classification methods have been commonly used for this purpose, recent Artificial Intelligence (AI) advancements offer new opportunities for software teams to create more robust software. To address challenges in Software Defect Prediction (SDP), we conducted a thorough bibliographic survey of 79 research articles from the year 2011 to 2023 that examined previous models, datasets, data validation techniques, defect detection, prediction methods, and SDP tools. The survey revealed that previous research often lacked appropriate datasets with the necessary characteristics and data validation methods. Additionally, many standard datasets suffer from a lack of labels, which hinders effective defect detection. Systematic literature reviews on SDP are scarce, further emphasizing the importance of this study. Based on the findings, we provide crucial recommendations for designing effective SDP models and tools. The proposed survey outlines an architecture for constructing SDP datasets with the appropriate characteristics, as well as multi-label classification and data validation methodologies for software defects. This approach aims to enhance SDP research and contribute to the development of high-quality software products by improving defect prediction accuracy.

Recent works have demonstrated the possibility to craft successful statistical attacks against keystroke dynamic biometric password. Those attacks leverage the possibility to capture several keystroke dynamics samples for a given password string, and then extract and use their distributional properties to craft the attack. These approaches are by design more likely to be successful when launched against fixed passwords, as several samples of the passwords can be captured through successive login sessions. Although the dynamics obtained from specific keys or key sequences for consecutive passwords slightly vary, by definition the distributional properties remain fairly stable for the same user. One way to thwart such that attack to use a variable password, also know as one‐time password (OTP). However, the fact that the keystroke dynamic OTP is different from one session to the other, makes it extremely difficult to reconstruct a valid biometric profile for a user. Modeling accurate keystroke dynamic OTP is challenging, due to the underlying variability and the sparse amount of information involved. We tackle the aformentioned challenge by presenting, in this paper, by presenting a multimodal approach tat combines fixed and variable keystroke dynamic biometric passwords. We investigate two different fusion models and evaluate our approach using a data set involving 100 different users, yielding encouraging performance results in terms of accuracy and resistance against statistical attacks.

Neuroblastoma, a challenging pediatric cancer, requires effective prediction models to improve patient outcomes. This paper introduces MoX, a hybrid deep-learning model designed to predict event-free survival (EFS) in neuroblastoma patients by integrating both clinical and genetic data. The MoX model combines feature selection via Random Forest with a dual-branch deep learning architecture that processes gene expression and clinical features separately before integrating them for final predictions. It was evaluated using the GSE85047 dataset, encompassing gene expression profiles and clinical attributes. The model’s performance is evaluated through K-Fold Cross-Validation, and SHAP (SHapley Additive exPlanations) is utilized to ensure interpretability by elucidating the contribution of individual features. MoX demonstrated exceptional performance with a training accuracy of 96.79%, validation accuracy of 98.15%, and test accuracy of 98.89%. The model achieved a high Area Under the Curve (AUC) of 0.992 for the test data ROC curve and a Precision-Recall (PR) curve with an AUC of 0.989, highlighting its ability to distinguish between event-free and non-event-free survival effectively. The SHAP analysis revealed that genes such as CD9, GABRB3, FRAS1, and IGF2BP1 were the most influential in predicting outcomes, offering valuable insights into the model's interpretability. MoX's low training and validation loss values (0.082 and 0.099, respectively) and high F1 score of 0.989 underscore its robust performance and reliability. MoX provides a powerful tool for enhancing personalized treatment strategies and guiding future research in multi-omic data integration. This paper marks a significant advancement in neuroblastoma prognosis by presenting a model that not only enhances predictive performance but also provides a high level of interpretability, supporting precision oncology and guiding future research in multi-omic data integration.