東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

A Markov Chain and Likelihood-Based ...

Kashyap, Abhinav.

Linked to FindBook

Google Book

Amazon

博客來

A Markov Chain and Likelihood-Based Model Approach for Automated Test Case Generation, Validation and Prioritization: Theory and Application.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Markov Chain and Likelihood-Based Model Approach for Automated Test Case Generation, Validation and Prioritization: Theory and Application./
Author:	Kashyap, Abhinav.
Description:	106 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.
Contained By:	Dissertation Abstracts International74-06B(E).
Subject:	Engineering, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3552055
ISBN:	9781267902726

A Markov Chain and Likelihood-Based Model Approach for Automated Test Case Generation, Validation and Prioritization: Theory and Application.
Kashyap, Abhinav.

A Markov Chain and Likelihood-Based Model Approach for Automated Test Case Generation, Validation and Prioritization: Theory and Application. - 106 p.

Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.

Thesis (Ph.D.)--The George Washington University, 2013.

This research presents a model-based systems engineering approach for the creation of system behavioral models and the automation of test case generation. Although recent advances in systems engineering (SE) approaches for requirement generation have significantly improved system reliability and quality, system validation and verification still remain an arduous task. Manual generation of the test cases is a major barrier to system validation activities. With the growing complexities of modern-world systems, automatic generation and execution of the test case is desirable. In this research, Markov chains and Markov modulated processes are used to model the system behavior. Due to monetary and time constraints faced by the systems engineers and project managers in completing the projects, test case prioritization is another desirable attribute of a test suite. To address this issue we extend the presented model-based approach with a novel likelihood-based prioritization scheme for testing the most used system features and trajectories first. The effectiveness of the presented model-based approach is discussed and demonstrated in the context of two case studies, a simulation case study and a web application usage data case study. Beyond system validation activities, the presented approach can also be extended to the field of requirement gathering and regression testing.

ISBN: 9781267902726Subjects--Topical Terms:

1020744
Engineering, General.

A Markov Chain and Likelihood-Based Model Approach for Automated Test Case Generation, Validation and Prioritization: Theory and Application.
LDR:02555nam 2200325 4500 001 1957332
005 20131202131336.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781267902726
035 $a (UMI)AAI3552055
035 $a AAI3552055
040 $a UMI $c UMI
100 1 $a Kashyap, Abhinav. $3 2092210
245 1 2 $a A Markov Chain and Likelihood-Based Model Approach for Automated Test Case Generation, Validation and Prioritization: Theory and Application.
300 $a 106 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-06(E), Section: B.
500 $a Adviser: Thomas A. Mazzuchi.
502 $a Thesis (Ph.D.)--The George Washington University, 2013.
520 $a This research presents a model-based systems engineering approach for the creation of system behavioral models and the automation of test case generation. Although recent advances in systems engineering (SE) approaches for requirement generation have significantly improved system reliability and quality, system validation and verification still remain an arduous task. Manual generation of the test cases is a major barrier to system validation activities. With the growing complexities of modern-world systems, automatic generation and execution of the test case is desirable. In this research, Markov chains and Markov modulated processes are used to model the system behavior. Due to monetary and time constraints faced by the systems engineers and project managers in completing the projects, test case prioritization is another desirable attribute of a test suite. To address this issue we extend the presented model-based approach with a novel likelihood-based prioritization scheme for testing the most used system features and trajectories first. The effectiveness of the presented model-based approach is discussed and demonstrated in the context of two case studies, a simulation case study and a web application usage data case study. Beyond system validation activities, the presented approach can also be extended to the field of requirement gathering and regression testing.
590 $a School code: 0075.
650 4 $a Engineering, General. $3 1020744
650 4 $a Engineering, System Science. $3 1018128
690 $a 0537
690 $a 0790
710 2 $a The George Washington University. $b Engineering Mgt and Systems Engineering. $3 1028210
773 0 $t Dissertation Abstracts International $g 74-06B(E).
790 1 0 $a Mazzuchi, Thomas A., $e advisor
790 1 0 $a Holzer, Thomas $e committee member
790 1 0 $a Sarkani, Shahyar $e committee member
790 1 0 $a Eveleigh, Tim $e committee member
790 1 0 $a Murphree, Edward L. $e committee member
790 $a 0075
791 $a Ph.D.
792 $a 2013
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3552055