Technical debt prediction involves anticipating problems and challenges in software projects. Because of lousy coding choices, these projects are often far from the optimal design. These issues can accumulate over time and affect long-term maintainability and stability. Numerous studies are available for predicting technical debt. For instance, some studies utilize static analysis tools like Pylint(1), and others use dynamic code analysis tools like SZZ [1]. This study addresses AI predictions using data obtained from static analysis tools. This paper presents technical debt prediction using static code analysis data generated with SonarQube(2) and Radon(3). It covers both short-term and long-term predictions by analyzing code quality metrics such as complexity, duplication, and adherence to standards. The study also integrates git commit classification to analyze commit history, providing valuable insights into the evolution and management of technical debt within the project.
Automated machines are widely used in industrial environments for the production of different items and generate lots of data following these processes. The proper execution of the machines influences the production output, thus the detection of anomalous behavior in machines' activity using the generated data must be considered to avoid unpleasant outcomes. This paper presents a novel ensemble unsupervised anomaly detection method characterized by parameterized prediction. The proposed method consists of two stages - the first stage uses statistical-based methods to assign artificial labels to the input data. In the second stage, the artificially labeled instances and a feature bagging technique are employed to construct the model's estimators - each estimator calculates the percentiles of the distances computed between its centroid and each instance from the training subset. The model's prediction function is parameterized by a percentile rank. Each estimator computes the distance between its centroid and the evaluated instance: if this distance exceeds the value of the specified percentile, the estimator classifies the instance as an anomaly. A majority vote is applied to establish the outcome.
Lung cancer persists as a global leader in cancer-related deaths, highlighting the critical need for precise and efficient detection methods. This paper investigates the use of the Medical Segmentation Decathlon dataset to train neural networks for lung cancer segmentation in CT scans via semantic segmentation. We propose and evaluate four new data adaptation techniques specifically designed for this dataset, with each technique being assessed using U-Net-based architectures. Our approach incorporates a thorough exploratory data analysis to uncover the dataset's strengths and weaknesses, which in turn guided our data preprocessing and augmentation strategies.
Disassembly is the process that translates machine code into a higher-level and more human-readable form. From the security analysis and malware detection perspective, it is an essential technique. However, traditional disassembly tools often display the information in a textual format, thus making it difficult to navigate and understand. In this paper, we explore the concepts involved in creating a viewer for disassembly that focuses on static analysis and boosts the user experience like clearly displaying the code, showcasing relevant parts, guiding the user, offering an interactive experience, map data structures and seamless integration with other tools. Furthermore, we emphasize the importance of having both a user-friendly and also customizable interface without having to concede on overall speed and performance. In this manner, even a novice in security analysis could have a positive experience. Considering all these features, the final result leads to a more productive and efficient analysis.
Testing and validating embedded software is paramount when it comes to safety-critical systems. BTC EMBEDDEDPlatform((R)) supports these activities using various approaches including test case definition means. In this system description we present SCRIPTVIEW, a domain-specific language designed to support test case implementation in a simple programmatic way. The language has been integrated in our platform and can be used in production by our customers.
