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An Empirical Approach for Estimation of the Software Development Effort
Amit Kumar Jakhar,Kumar Rajnish 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.2
The number of standards and methods has been developed for software estimation in the last many decades. These methods help practitioners and engineers to estimate the effort accurately, but they found their inability to estimate software precisely. In this paper, an empirical approach is developed to measure the software development effort. The well-known PROMISE data sets of 217 projects are collected and the several performance factors are used in this paper to validate the estimated results. This work also considers the most popular model for software effort estimating, i.e. COCOMO. And the result of the COCOMO model and proposed an empirical approach is compared with the actual effort, and concludes that the proposed approach estimates the software development effort better than COCOMO in several aspects.
Crystallization kinetics for carbon dioxide gas hydrate in fixed bed and stirred tank reactor
Rajnish Kumar,Asheesh Kumar,Dishant Khatri,Ju-dong Lee 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.6
The phase change from germ nuclei to growth nuclei and subsequent volume transformation in a crystallization process was modeled by Avrami equations. The phase change during the hydrate formation was fitted with the classical Avrami model by utilizing gas uptake data. The idea is to understand the difference in growth behavior of hydrate crystals when in small pores compared to a stirred tank reactor which does not pose any physical restrictions to hydrate growth. The parameters n and k of the Avrami equation were determined explicitly for CO2 hydrate formation.
Finding Best Mining Scheme for Development of Multinomial Software Fault Prediction Model
Dipti Kumari,Kumar Rajnish 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.5
This paper discuss different classification methods toward reliability and quality improvement of software systems by predicting fault-prone module before testing. Classification capability of Data mining techniques and Object-oriented property based knowledge stored in Object-Oriented metrics are used to classify the software module as fault-prone in different error categories or not fault-prone. Three versions of Eclipse, the java-based Open source Integrated Development environment as dataset for training and testing all the classification based data mining techniques are used. First of all, Threshold base feature ranking (i.e. Area under the ROC curve) is used for selecting effective OO-metrics in building prediction model. After that using those subsets of selected attributes, classification models are built with 41 different classifiers for multinomial classification in fault detection systems. Finally, the performance of a classifier is evaluated with respect to the PRC performance metric. Based on the performance results appropriate classifiers (Random Committee, Random Tree, Randomizable Filtered classifier and IBK) which depict a higher performance and accuracy compared to the others are selected. Our results indicate that Random Tree, Random Committee and Randomizable Filtered Classifier have same performance. IBK classifier also has same performance but little bit less and Kstar has less performance compared to previous four selected classifiers.
Investigating the Effect of Object-oriented Metrics on Fault Proneness Using Empirical Analysis
Dipti Kumari,Kumar Rajnish 보안공학연구지원센터 2015 International Journal of Software Engineering and Vol.9 No.2
This paper presents an innovative metric based on a class abstraction to capture aspects of software complexity through combinations of class characteristics. The study also used software metrics effectiveness in finding the classes in different error categories for the three versions of Eclipse, the Java-based open-source Integrated Development Environment. Many studies used Logistic regression models to investigate the ability of OO software metrics to predict fault prone classes. We also used this method not only for binary but also multinomial categorization and empirically validate the ability of metrics to predict fault prone classes in different category using fault data. We conclude that this proposed metric is as effective as the traditional metrics in identifying fault-prone classes in binary categorization and also showing most efficient result for multinomial categorization. We also find that Univariate model for these metrics have same performance as the individual metric with no any learning technique in prediction of fault-proneness.
Evolving Constructs & Measurements of Aviation Fuel Consumption: An Analytical View
Jagroop Singh,Somesh Kumar Sharma,Rajnish Srivastava,Deepjyoti Das 대한토목학회 2018 KSCE Journal of Civil Engineering Vol.22 No.7
Aviation fuel is a major stakeholder in total operating cost of air transportation. Increasing competition & challenge to keep thetravel expenses low to survive have created interest in fuel consumption reduction. Although interest of researchers for Reduction inAviation Fuel Consumption (RAFC) is growing, yet no study has undertaken initiative towards a development of RAFC frameworkand instruments. Therefore, this article is an effort to present measurement models with different variables for estimating the RAFCthrough non-financial and financial measures. The study, through extensive literature review identifies and integrates various fuelconsumption reduction initiatives and measures to develop key RAFC constructs conducive for further improvement in aviation fuelefficiency. The RAFC is sub classified as driving forces measurement model, implementation measurement model and performancemeasurement model. Factor Analysis (FA) and measurement models were used to investigate various constructs of RAFC. Aftersequence of tests and analysis, study provide refined measurements with acceptable psychometric properties. These measurementscan also be applied to varying contexts to extend conceptualization or to test different conceptual models, advancing RAFC theorybuilding.