|
|
Applying Code Change Patterns during Analysis of Program Equivalence
Šilling, Petr ; Fiedor, Tomáš (oponent) ; Malík, Viktor (vedoucí práce)
The goal of this thesis is to propose a static analysis method for recognition of code change patterns describing recurrent changes between different versions of low-level code. The thesis proposes an encoding method of patterns, which uses the LLVM intermediate representation, and a pattern matching algorithm based on gradual comparison of instructions according to their control flow. The proposed analysis has been implemented as an extension of DiffKemp, a tool for analysing semantic differences between versions of large C projects. Results of experiments conducted on three pairs of past versions of the Linux kernel show that the extension is able to eliminate a substantial amount of false-positive or generally undesirable differences from the output of DiffKemp, which would otherwise require manual inspection.
|
|
|
Deep Learning for Image Stitching
Šilling, Petr ; Beran, Vítězslav (oponent) ; Španěl, Michal (vedoucí práce)
Image stitching is an essential technique for reconstructing volumes of biological samples from overlapping tiles of electron microscopy (EM) images. Current volume EM stitching methods generally rely on handcrafted features, such as those produced by SIFT. However, recent developments indicate that convolutional neural networks (CNNs) can improve stitching accuracy by learning discriminative features directly from training images. Taking into account the potential of CNNs, this thesis proposes DEMIS, a novel EM image stitching tool based on LoFTR, an attention-based feature matching network. The thesis also proposes a novel dataset generated by splitting high-resolution EM images into grids of overlapping image tiles. The dataset is used to fine-tune LoFTR and to evaluate the DEMIS tool. Experiments on the synthetic dataset reveal higher feature matching accuracy compared to SIFT. Moreover, experiments on challenging images with small overlap regions and high resolution demonstrate significantly higher stitching robustness than SIFT. Overall, the results suggest that deep learning methods could be beneficial for EM imaging, for example, by allowing the use of smaller tile overlaps.
|
|
|
Applying Code Change Patterns during Analysis of Program Equivalence
Šilling, Petr ; Fiedor, Tomáš (oponent) ; Malík, Viktor (vedoucí práce)
The goal of this thesis is to propose a static analysis method for recognition of code change patterns describing recurrent changes between different versions of low-level code. The thesis proposes an encoding method of patterns, which uses the LLVM intermediate representation, and a pattern matching algorithm based on gradual comparison of instructions according to their control flow. The proposed analysis has been implemented as an extension of DiffKemp, a tool for analysing semantic differences between versions of large C projects. Results of experiments conducted on three pairs of past versions of the Linux kernel show that the extension is able to eliminate a substantial amount of false-positive or generally undesirable differences from the output of DiffKemp, which would otherwise require manual inspection.
|
host ::
RSS.