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17,810 results on '"Idrees A"'

316. Rutin encapsulated decellularized earthworm granulation hydrogel promotes angiogenesis in wound healing of diabetic rabbit model by inhibiting TRAF1/NF-κB pathway

Author

Ali, Sofi Imtiyaz, Elkhalifa, Ahmed M.E., Nabi, Showkat Ul, Taifa, Syed, Shah, Iqra Hussain, Rakhshan, Rabia, Malik, Masood, Ramzan, Zahid, Din Dar, Mehraj U., Mir, Muzafar Ahmad, Elamin, Elham, Bazie, Elsharif A., Alzerwi, Nasser A.N., Rayzah, Musaed, Idrees, Bandar, Rayzah, Fares, Baksh, Yaser, Alsultan, Afnan, and Alzahrani, Ahmed M.

Published
2024
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332. CharmFL: A Fault Localization Tool for Python

Author

Sarhan, Qusay Idrees, Szatmari, Attila, Toth, Rajmond, and Beszedes, Arpad

Subjects
Computer Science - Software Engineering
Abstract

Fault localization is one of the most time-consuming and error-prone parts of software debugging. There are several tools for helping developers in the fault localization process, however, they mostly target programs written in Java and C/C++ programming languages. While these tools are splendid on their own, we must not look over the fact that Python is a popular programming language, and still there are a lack of easy-to-use and handy fault localization tools for Python developers. In this paper, we present a tool called "CharmFL" for software fault localization as a plug-in for PyCharm IDE. The tool employs Spectrum-based fault localization (SBFL) to help Python developers automatically analyze their programs and generate useful data at run-time to be used, then to produce a ranked list of potentially faulty program elements (i.e., statements, functions, and classes). Thus, our proposed tool supports different code coverage types with the possibility to investigate these types in a hierarchical approach. The applicability of our tool has been presented by using a set of experimental use cases. The results show that our tool could help developers to efficiently find the locations of different types of faults in their programs., Comment: 6 Pages

Published
2021

333. Method Calls Frequency-Based Tie-Breaking Strategy For Software Fault Localization

Author

Sarhan, Qusay Idrees, Vancsics, Bela, and Beszedes, Arpad

Subjects
Computer Science - Software Engineering
Abstract

In Spectrum-Based Fault Localization (SBFL), a suspiciousness score is assigned to each code element based on test coverage and test outcomes. The scores are then used to rank the code elements relative to each other in order to aid the programmer during the debugging process when seeking the source of a fault. However, probably none of the known SBFL formulae are guaranteed to produce different scores for all the program elements, hence ties emerge between the code elements. Based on our experiments, ties in SBFL are prevalent: in Defects4J, 54-56% of buggy methods are members of ties, i.e., there is at least one other method with the same score in these cases (but typically much more, on average 6), and this inevitably reduces the effectiveness of any SBFL approach. In this work, we present a technique to break ties in such cases based on the so-called method calls frequencies. This counts the number of different contexts of method calls (both as callees and as callers) in failing test cases. The intuition is that if a method appears in many different calling contexts during a failing test case, it will be more suspicious and get a higher rank position compared to other methods with the same scores. This method can be applied to any underlying SBFL formula, and can favourably break the occurring ranks in the ties in many cases. The experimental results show that our novel tie-breaking strategy achieved a significant reduction in both size and number of critical ties in our benchmark. In 72-73% of the cases, the ties were completely eliminated and the average reduction rate was more than 80%., Comment: 11 Pages

Published
2021

334. U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations

Author

Zhang, Yichi, Kers, Jesper, Cassol, Clarissa A., Roelofs, Joris J., Idrees, Najia, Farber, Alik, Haroon, Samir, Daly, Kevin P., Ganguli, Suvranu, Chitalia, Vipul C., and Kolachalama, Vijaya B.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68U10
Abstract

Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in the annotation of the same images, then the inter-expert agreement is not necessarily perfect, and no single expert annotation can precisely capture the so-called ground truth of the regions of interest on all images. Also, it is not trivial to generate a reference estimate using annotations from multiple experts. Here we present a deep neural network, defined as U-Net-and-a-half, which can simultaneously learn from annotations performed by multiple experts on the same set of images. U-Net-and-a-half contains a convolutional encoder to generate features from the input images, multiple decoders that allow simultaneous learning from image masks obtained from annotations that were independently generated by multiple experts, and a shared low-dimensional feature space. To demonstrate the applicability of our framework, we used two distinct datasets from digital pathology and radiology, respectively. Specifically, we trained two separate models using pathologist-driven annotations of glomeruli on whole slide images of human kidney biopsies (10 patients), and radiologist-driven annotations of lumen cross-sections of human arteriovenous fistulae obtained from intravascular ultrasound images (10 patients), respectively. The models based on U-Net-and-a-half exceeded the performance of the traditional U-Net models trained on single expert annotations alone, thus expanding the scope of multitask learning in the context of biomedical image segmentation., Comment: https://github.com/vkola-lab/unaah

Published
2021

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