Multifactor Algorithm for Test Case Selection and Ordering
Regression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent m...
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University of Baghdad, College of Science for Women
2021-06-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5517 |
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| author | Atulya Gupta Rajendra Prasad Mahapatra |
| author_facet | Atulya Gupta Rajendra Prasad Mahapatra |
| author_sort | Atulya Gupta |
| collection | DOAJ |
| description | Regression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent mechanism to attain optimality while reducing or prioritizing test cases. Nevertheless, those studies were deprived of systematic procedures to manage tied test cases issue. Moreover, evolutionary algorithms such as the genetic process often help in depleting test cases, together with a concurrent decrease in computational runtime. However, when examining the fault detection capacity along with other parameters, is required, the method falls short. The current research is motivated by this concept and proposes a multifactor algorithm incorporated with genetic operators and powerful features. A factor-based prioritizer is introduced for proper handling of tied test cases that emerged while implementing re-ordering. Besides this, a Cost-based Fine Tuner (CFT) is embedded in the study to reveal the stable test cases for processing. The effectiveness of the outcome procured through the proposed minimization approach is anatomized and compared with a specific heuristic method (rule-based) and standard genetic methodology. Intra-validation for the result achieved from the reduction procedure is performed graphically. This study contrasts randomly generated sequences with procured re-ordered test sequence for over '10' benchmark codes for the proposed prioritization scheme. Experimental analysis divulged that the proposed system significantly managed to achieve a reduction of 35-40% in testing effort by identifying and executing stable and coverage efficacious test cases at an earlier phase. |
| format | Article |
| id | doaj-art-ae51e5b9eaa24d99b2d997d620d5e3ae |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-ae51e5b9eaa24d99b2d997d620d5e3ae2025-08-20T03:58:50ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862021-06-01182(Suppl.)10.21123/bsj.2021.18.2(Suppl.).1056Multifactor Algorithm for Test Case Selection and OrderingAtulya Gupta0Rajendra Prasad Mahapatra1Department of Computer Science and Engineering, SRMIST -201204, Delhi-NCR Campus, Ghaziabad, U.P., IndiaDepartment of Computer Science and Engineering, SRMIST -201204, Delhi-NCR Campus, Ghaziabad, U.P., IndiaRegression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent mechanism to attain optimality while reducing or prioritizing test cases. Nevertheless, those studies were deprived of systematic procedures to manage tied test cases issue. Moreover, evolutionary algorithms such as the genetic process often help in depleting test cases, together with a concurrent decrease in computational runtime. However, when examining the fault detection capacity along with other parameters, is required, the method falls short. The current research is motivated by this concept and proposes a multifactor algorithm incorporated with genetic operators and powerful features. A factor-based prioritizer is introduced for proper handling of tied test cases that emerged while implementing re-ordering. Besides this, a Cost-based Fine Tuner (CFT) is embedded in the study to reveal the stable test cases for processing. The effectiveness of the outcome procured through the proposed minimization approach is anatomized and compared with a specific heuristic method (rule-based) and standard genetic methodology. Intra-validation for the result achieved from the reduction procedure is performed graphically. This study contrasts randomly generated sequences with procured re-ordered test sequence for over '10' benchmark codes for the proposed prioritization scheme. Experimental analysis divulged that the proposed system significantly managed to achieve a reduction of 35-40% in testing effort by identifying and executing stable and coverage efficacious test cases at an earlier phase.https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5517GA, Regression testing, Test cases, Test case minimization, Test case prioritization |
| spellingShingle | Atulya Gupta Rajendra Prasad Mahapatra Multifactor Algorithm for Test Case Selection and Ordering مجلة بغداد للعلوم GA, Regression testing, Test cases, Test case minimization, Test case prioritization |
| title | Multifactor Algorithm for Test Case Selection and Ordering |
| title_full | Multifactor Algorithm for Test Case Selection and Ordering |
| title_fullStr | Multifactor Algorithm for Test Case Selection and Ordering |
| title_full_unstemmed | Multifactor Algorithm for Test Case Selection and Ordering |
| title_short | Multifactor Algorithm for Test Case Selection and Ordering |
| title_sort | multifactor algorithm for test case selection and ordering |
| topic | GA, Regression testing, Test cases, Test case minimization, Test case prioritization |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5517 |
| work_keys_str_mv | AT atulyagupta multifactoralgorithmfortestcaseselectionandordering AT rajendraprasadmahapatra multifactoralgorithmfortestcaseselectionandordering |