Mısra, Sanjay

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/8515
Name Variants
M.,Sanjay
Misra, Sanjay
Mısra,S.
Mısra, Sanjay
Misra,S.
S.,Misra
Sanjay, Mısra
Sanjay, Misra
S., Misra
S.,Mısra
M., Sanjay
Misra, S.
Job Title
Profesör Doktor
Email Address
sanjay.misra@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
3
Research Products
ZERO HUNGER2
ZERO HUNGER
5
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
2
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
6
Research Products
GENDER EQUALITY5
GENDER EQUALITY
2
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
1
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
8
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
11
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
11
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
2
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
3
Research Products
CLIMATE ACTION13
CLIMATE ACTION
7
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
10
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
4
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
9
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

199

Articles

133

Views / Downloads

125/33

Supervised MSc Theses

3

Supervised PhD Theses

0

WoS Citation Count

2811

Scopus Citation Count

4107

Patents

0

Projects

0

WoS Citations per Publication

14.13

Scopus Citations per Publication

20.64

Open Access Source

53

Supervised Theses

3

JournalCount
Acta Polytechnica Hungarica12
Tehnicki Vjesnik8
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -- 2011 International Conference on Computational Science and Its Applications, ICCSA 2011 -- 20 June 2011 through 23 June 2011 -- Santander -- 854805
Journal of Physics: Conference Series -- 3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018 -- 18 September 2018 through 19 September 2018 -- Medan, Sumatera Utara -- 1498654
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -- 12th International Conference on Computational Science and Its Applications, ICCSA 2012 -- 18 June 2012 through 21 June 2012 -- Salvador de Bahia -- 909454
Current Page: 1 / 22

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2018 - 20192
Reset filters

Settings

Author: Damasevicius, RobertasSubject: Entropy

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 30
    Citation - Scopus: 40
    Software Code Smell Prediction Model Using Shannon, Renyi and Tsallis Entropies
    (MDPI, 2018) Blazauskas, Tomas; Gupta, Aakanshi; Misra, Sanjay; Suri, Bharti; Kumar, Vijay; Damasevicius, Robertas
    The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Renyi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics (R-2, adjusted R-2, Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.
  • Thumbnail Image
    Article
    Citation - WoS: 20
    Citation - Scopus: 25
    Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug
    (MDPI, 2019) Misra, Sanjay; Kumari, Madhu; Misra, Ananya; Damasevicius, Robertas; Fernandez Sanz, Luis; Sanz, Luis Fernandez; Singh, V. B.
    A software bug is characterized by its attributes. Various prediction models have been developed using these attributes to enhance the quality of software products. The reporting of bugs leads to high irregular patterns. The repository size is also increasing with enormous rate, resulting in uncertainty and irregularities. These uncertainty and irregularities are termed as veracity in the context of big data. In order to quantify these irregular and uncertain patterns, the authors have appliedentropy-based measures of the terms reported in the summary and the comments submitted by the users. Both uncertainties and irregular patterns have been taken care of byentropy-based measures. In this paper, the authors considered that the bug fixing process does not only depend upon the calendar time, testing effort and testing coverage, but it also depends on the bug summary description and comments. The paper proposed bug dependency-based mathematical models by considering the summary description of bugs and comments submitted by users in terms of the entropy-based measures. The models were validated on different Eclipse project products. The models proposed in the literature have different types of growth curves. The models mainly follow exponential, S-shaped or mixtures of both types of curves. In this paper, the proposed models were compared with the modelsfollowingexponential, S-shaped and mixtures of both types of curves.