2025-07-14
Patrik P. Süli, Judit Knoll, Zoltán Porkoláb
Ensuring thread safety in applications is crucial for preventing subtle and challenging bugs in concurrent programming. This paper presents two algorithmic approaches to improve thread safety through static analysis and to demonstrate their benefits in real life, the authors also implemented them as two detectors in SpotBugs static analyzer. These checkers are designed to identify unsafe usages of shared resources and improper atomic operations in concurrent Java programming, aiming to mitigate common multithreading issues such as race conditions. By emphasizing consistent locking strategies and the correct use of atomic types, the study offers insight into how to improve the reliability of multithreaded applications.
2025-05-20
Zsófia Erdei, István Bozó, Melinda Tóth
The identification of the sources of a runtime error is a common task for Erlang developers. Dynamic and static tools can assist in this task. Our work aims to help Erlang developers in debugging processes to reproduce a runtime error. We would like to use and extend the static analyser framework of RefactorErl with new algorithms to support this fault localisation process. In our previous paper, we presented a symbolic execution-based analysis method to find the source of runtime errors. This paper extends that work with path selection heuristics to improve the efficiency of the algorithm in the RefactorErl framework.
2025-03-24
A. H. M. Sajedul Hoque, Gergő Bognár, Sándor Fridli
Radiomics is an emerging field of CT image processing, that offers noninvasive quantification of tumour phenotypes using quantitative image features. Radiomics analysis has promising applications in cancer treatment and personalized medicine, like treatment planning and the prediction of clinical factors. However, the optimal feature selection is not established in the literature, and the applications usually involve data mining of a large pool of features. In this paper, we propose to extract higher-level radiomic features using Radial Harmonic Fourier moments (RHFM). Image moments, and specially orthogonal Fourier moments are widely used in image processing, providing efficient and invariant shape descriptors. In particular, RHFMs are known to perform well on small noisy images, making them a promising candidate for CT tumour analysis. Motivated by these advantages, we developed a feature extraction scheme based on RHFM, and we performed radiomics analysis on lung CT images of non-small cell lung cancer patients. The proposed method is validated on multiple annotated datasets following the literature guidelines, evaluating the accuracy, stability, reliability, and prognostic value of the proposed features. The results show better reliability and otherwise comparable performance compared to the state-of-the-art wavelet descriptors. Furthermore, Fourier moments provide higher level of flexibility and possible adaptivity compared to wavelets, and unlike wavelet features, RHFM features are invariant of position, size and orientation in the tumor region.