Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Bora, Dibya Jyoti
- An Efficient Heuristic Based Test Suite Minimization Approach
Abstract Views :183 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Applications, Barkatullah University, Bhopal – 462026, Madhya Pradesh, IN
1 Department of Computer Science and Applications, Barkatullah University, Bhopal – 462026, Madhya Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 10, No 29 (2017), Pagination:Abstract
Objectives: Development of an efficient test suite minimization approach in order to reduce the size of a previously acquired test suite and produce a new representative suite which will guarantee the same requirement coverage that was achieved before minimization for an effective and efficient regression testing. Method: Test suite minimizations techniques try to reduce the size and redundancy of test suite by removing certain test cases since requirement covered by them are already covered by other test cases. But, it has been found that the acquired test cases after minimization severely lacks ability to achieve the desirable code coverage because the minimization was done based on a single test adequacy criteria. In this paper, we propose an efficient heuristic based test suite minimization algorithm which will reduce the size of the test suites with respective to multiple test adequacy criterions in order to preserve the fault detection effectiveness and code coverage characteristics of the final test suite. Findings: Our experimental results indicate that a significant percentage of reduction in the test suite size is achieved when the minimization is performed with respect to multiple test adequacy criterions. Our approach is unique compared to the existing approaches in the sense that, we carried out minimization based on multiple test adequacy criterions while most of the existing approaches usually take one or two criterions into consideration. The proposed approach is evaluated based on two well known software testing metrics; one indicate the percentage of reduction in test suite size and the second one indicate the percentage of code coverage achieved by the minimized test suite. Our experimental results indicate that a significant percentage of reduction in the size as well as significant code coverage characteristics is achieved when the minimization is done according to the proposed approach. Improvements: The important contribution of this study is that, it presents a novel and efficient test suite minimization technique that optimizes the test suite size based on multiple adequacy criterions.Keywords
Regression Testing, Software Testing, Test Data Generation, Test Suite Minimization, Test Suite Selection and Data Clustering.- Flight Price Prediction Using Machine Learning
Abstract Views :135 |
PDF Views:0
Authors
Victor Sarmacharjee
1,
Himangshu Nath
1,
Ashim Buragohain
1,
Rajesh Kumar Gouda
1,
Dibya Jyoti Bora
1
Affiliations
1 Department of Information Technology, SCS, The Assam Kaziranga University, IN
1 Department of Information Technology, SCS, The Assam Kaziranga University, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 97-106Abstract
Those who frequently travel will be better educated about the best offers and the best times to buy tickets. For financial reasons, a lot of airlines change their prices according to the seasons or the time of year. The price will increase as more people travel. The real idea behind our travel prediction system is to forecast flight expenses by comparing today's pricing to yesterday's. Using various machine learning techniques on a sizable dataset, we will build a model to forecast flight prices, and the effectiveness of the models will be compared.Keywords
Indian Airlines, Machine Learning, Exploratory Data Analysis, Prediction Model, Pricing Models, Model Training & Testing, Model Evaluation.References
- K. Tziridis T. Kalampokas G.Papakostas and K. Diamantaras "Airfare price prediction using machine learning techniques" in European Signal Processing Conference (EUSIPCO), DOI: 10.23919/EUSIPCO .2017.8081365L. Li Y. Chen and Z. Li” Yawning detection for monitoring driver fatigue based on two cameras” Proc. 12th Int. IEEE Intel Conf.. Transp. Syst. pp.1-6Oct.2009–
- Supriya Rajankar, Neha sakhrakar and Omprakash rajankar “Flight fare prediction using machine learning algorithms” International journal of Engineering Research and Technology (IJERT) June 2019 –
- “A survey on machine learning-based flight pricing prediction.” Supriya Rajankar and Neha Sakharkar –
- T. Wang et al., "A Framework for Airfare Price Prediction: A Machine Learning Approach," doi: 10.1109/IRI.2019.00041 –
- K. Tziridis, T. Kalampokas, G. A. Papakostas and K. I. Diamantaras, "Airfare prices prediction using machine learning techniques," doi: 10.23919/EUSIPCO.2017.8081365 –
- “A survey on machine learning-based flight pricing prediction.” Supriya Rajankar and Neha Sakharkar
- “A PROPOSAL FOR INDIAN FLIGHT FARE PREDICTION” Udhhav Arora, Jaywrat Singh Champawat, and Dr. K. Vijaya –
- T. Janssen, T. Dijkstra, S. Abbas, and A. C. van Riel, “A linear quantile mixed regression model for prediction of airline ticket prices,” Radboud University, 2014.
- R. Ren, Y. Yang, and S. Yuan, “Prediction of airline ticket price,” University of Stanford, 2014.
- T. Wohlfarth, S. Clemenc¸on, F. Roueff, and X. Casellato, “A data-mining approach to travel price forecasting,” in the 10th international conference on machine learning and applications and workshops, vol. 1, 2011, pp. 84–89
- “Airfare Prices Prediction Using Machine Learning Techniques”, K. Tziridis, Th. Kalampokas, G.A. Papakostas HUMAIN-Lab, 2017 25th European Signal Processing Conference (EUSIPCO)
- B. Burger and M. Fuchs, “Dynamic pricing – A future airline business model,” Journal of Revenue and Pricing Management, vol. 4, no. 1, pp. 39–53, 2005
- K. Rama-Murthy, “Modelling of united states airline fares– using the official airline guide (OAG) and airline origin and destination survey (DB1B),” Ph.D. dissertation, Virginia Tech, 2006.
- B. Derudder and F. Witlox, “An appraisal of the use of airline data in assessing the world city network: a research notes on data,” Urban Studies, vol. 42, no. 13, pp. 2371–2388, 2005.
- T. Liu, J. Cao, Y. Tan, and Q. Xiao, “ACER: An adaptive context-aware ensemble regression model for airfare price prediction,” in the international conference on progress in informatics and computing, 2017, pp. 312–317.