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22 results on '"Yadav, Asmita"'

5. Bug Assignment-Utilization of Metadata Features Along with Feature Selection and Classifiers

Author

Yadav, Asmita, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Choudhary, Ankur, editor, Agrawal, Arun Prakash, editor, Logeswaran, Rajasvaran, editor, and Unhelkar, Bhuvan, editor

Published
2021
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7. Developer load balancing bug triage: Developed load balance.

Author

Yadav, Asmita, Baljon, Mohammed, Mishra, Shailendra, Singh, Sandeep Kumar, Saxena, Sharad, and Sharma, Sunil Kumar

Subjects
*JOB involvement, *MEDICAL triage, *COMPUTER software development, *WORK experience (Employment), *WORK design
Abstract

During the software development process, numerous bugs are reported daily in the software bug repositories. Bug triage assigned these bugs to the most relevant and expert developer for resolution. Moreover, assigning bugs to an incompetent or an over‐engaged developer causes repeated reassignment to other developers until it is resolved. This problem can be solved by devising a triage process that assigns bugs to not only expert developers but also to those who are either under‐engaged or reasonably engaged but are not over‐engaged in work. This paper has designed and implemented work engagement sensitive bug triage that resolves the issue of assigning bugs to developers considering their due work engagement, expertise as well as the current state of activity. For this purpose, a developer profile is built by using metrics to generate three types of scores: technical skill, work engagement and work experience. Metadata features like developer‐name, email, developer‐work‐experience in bug resolution, last and present‐work‐activity, timestamp, component and priority are used for it. A multi‐criteria‐based Henry–Garret technique is used to generate a single ranked list of developers from three ranked lists. The performance of the proposed approach is evaluated on four large OOS projects: Mozilla, Eclipse, Netbeans, and Open Office covering 895,439 bug reports accumulated for 33 years of development. The overall system accuracy of the proposed triage using all four datasets is 91.96 ± 0.05% which is 5.87% better than previously published work. The proposed method is achieved up to 90.30 ± 0.05 and 97.1 ± 0.05 MRR and accuracy of reassignment respectively that indicates how significantly it re‐assigns the bug to the relevant developers. The results demonstrate improvement in the accuracy of software‐bugs triaging as well as a reduction in the probability of bug‐tossing. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Peptide Based Therapy for Neurological Disorders

Author

Yadav, Asmita, Pandey, Damini, Ashraf, Ghulam Md., and Rachana

Abstract

Peptides are small molecules composed of amino acids linked together by peptide bonds. The targeted action of these peptides along with their magnificent ability to reach locations in the body that are complicated to access, is being considered of tremendous potential in disease modifying therapies. Synthetic as well as natural peptides like Carnosine are currently under research for the treatment of Neurodegenerative Disorders (NDDs). Peptide based vaccines are currently under immense research for diseases like dementia. Toxicity of peptide-based drugs towards eukaryotic cells due to their increased haemolytic activity is of major concern and this is being tackled by introducing modifications into the peptide structure. Some crucial peptide inhibitors currently in use for neurodegenerative disorders include Aβ (16-20) KLVFF for Alzheimers disease, NAPVSIPQ (NAP) for Parkinsons disease, towards eukaryotic cells Vasoactive Intestinal Peptides (VIP) for Huntingtons disease, Polyglutamine Binding Peptide-1(QBP1) for Dentatorubral- pallidoluysian atrophy (DRPLA). Certain peptides are involved in inhibition of Mitochondrial Permeability Transition (MPT) that plays a prominent part in the materialization of neurodegenerative diseases, one such example of peptides being Ba-V which is obtained from Bothrops atrox snake venom. New therapeutic peptides are being identified using bioinformatics tools like High Throughput Screening (HTS). These tools are being used to explore the selectivity, stability, extent of immune response and toxic side effects of peptides. Apart from neurodegenerative diseases, the potential of bioactive peptides is also being tested against cancer, diabetes and microbes. This review focuses on the recent advances in peptide therapeutics and novel peptides discovered for the treatment of NDs.

Published
2021
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22. Study of K-Means and Enhanced K-Means Clustering Algorithm.

Author

Singh, S. P. and Yadav, Asmita

Subjects
COMPUTER systems, ALGORITHMS, COMPUTER science, APPLICATION software, MATHEMATICAL programming
Abstract

Data clustering is a process of arranging data into groups or a technique for classifying a mountain of information into some manageable meaningful piles. The goal of clustering is to partitions a dataset into several groups such that the similarity within a group is better than among groups. K- means is one of the basic clustering algorithm which is commonly used in several applications, but it is computationally time consuming and the quality of the resulting clusters heavily depends on the selection of initial centroids.We can removed first limitation using the Enhanced K- Means algorithm. This paper represent the comparison analysis of basic K-Means clustering algorithm and Enhanced K- Means clustering algorithm which shows Enhanced K-Means algorithm more effective and efficient than Basic K-means algorithm. [ABSTRACT FROM AUTHOR]

Published
2013

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