scholarly journals Potpourri: An Epistasis Test Prioritization Algorithm via Diverse SNP Selection

2020 ◽  
Author(s):  
Gizem Caylak ◽  
Oznur Tastan ◽  
A. Ercument Cicek
Keyword(s):  
Test Prioritization

scholarly journals Molecular testing in lung cancer: Fine-needle aspiration specimen adequacy and test prioritization prior to the CAP/IASLC/AMP Molecular Testing Guideline publication

2014 ◽  
Vol 122 (6) ◽  
pp. 454-458 ◽  
Author(s):  
Oana C. Rafael ◽  
Mohamed Aziz ◽  
Harry Raftopoulos ◽  
Oana E. Vele ◽  
Weisheng Xu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fine Needle Aspiration ◽  
Needle Aspiration ◽  
Molecular Testing ◽  
Fine Needle ◽  
Test Prioritization

scholarly journals PRIORITIZATION OF COMBINATORIAL TEST CASES BY INCREMENTAL INTERACTION COVERAGE

2013 ◽  
Vol 23 (10) ◽  
pp. 1427-1457 ◽  
Author(s):  
RUBING HUANG ◽  
XIAODONG XIE ◽  
DAVE TOWEY ◽  
TSONG YUEH CHEN ◽  
YANSHENG LU ◽  
...  
Keyword(s):  
Test Case ◽  
Test Cases ◽  
New Strategy ◽  
Testing Cost ◽  
Test Prioritization ◽  
Interaction Testing ◽  
Parameter Interactions ◽  
Combinatorial Interaction Testing ◽  
Alternative Assumption ◽  
Better Than

Combinatorial interaction testing is a well-recognized testing method, and has been widely applied in practice, often with the assumption that all test cases in a combinatorial test suite have the same fault detection capability. However, when testing resources are limited, an alternative assumption may be that some test cases are more likely to reveal failure, thus making the order of executing the test cases critical. To improve testing cost-effectiveness, prioritization of combinatorial test cases is employed. The most popular approach is based on interaction coverage, which prioritizes combinatorial test cases by repeatedly choosing an unexecuted test case that covers the largest number of uncovered parameter value combinations of a given strength (level of interaction among parameters). However, this approach suffers from some drawbacks. Based on previous observations that the majority of faults in practical systems can usually be triggered with parameter interactions of small strengths, we propose a new strategy of prioritizing combinatorial test cases by incrementally adjusting the strength values. Experimental results show that our method performs better than the random prioritization technique and the technique of prioritizing combinatorial test suites according to test case generation order, and has better performance than the interaction-coverage-based test prioritization technique in most cases.

Parallel Test Prioritization

2022 ◽  
Vol 31 (1) ◽  
pp. 1-50
Author(s):  
Jianyi Zhou ◽  
Junjie Chen ◽  
Dan Hao
Keyword(s):  
Extensive Study ◽  
Good Choice ◽  
Regression Testing ◽  
Sequential Test ◽  
Testing Time ◽  
Parallel Test ◽  
Parallel Testing ◽  
Test Execution ◽  
Coverage Criterion ◽  
Test Prioritization

Although regression testing is important to guarantee the software quality in software evolution, it suffers from the widely known cost problem. To address this problem, existing researchers made dedicated efforts on test prioritization, which optimizes the execution order of tests to detect faults earlier; while practitioners in industry leveraged more computing resources to save the time cost of regression testing. By combining these two orthogonal solutions, in this article, we define the problem of parallel test prioritization, which is to conduct test prioritization in the scenario of parallel test execution to reduce the cost of regression testing. Different from traditional sequential test prioritization, parallel test prioritization aims at generating a set of test sequences, each of which is allocated in an individual computing resource and executed in parallel. In particular, we propose eight parallel test prioritization techniques by adapting the existing four sequential test prioritization techniques, by including and excluding testing time in prioritization. To investigate the performance of the eight parallel test prioritization techniques, we conducted an extensive study on 54 open-source projects and a case study on 16 commercial projects from Baidu , a famous search service provider with 600M monthly active users. According to the two studies, parallel test prioritization does improve the efficiency of regression testing, and cost-aware additional parallel test prioritization technique significantly outperforms the other techniques, indicating that this technique is a good choice for practical parallel testing. Besides, we also investigated the influence of two external factors, the number of computing resources and time allowed for parallel testing, and find that more computing resources indeed improve the performance of parallel test prioritization. In addition, we investigated the influence of two more factors, test granularity and coverage criterion, and find that parallel test prioritization can still accelerate regression testing in parallel scenario. Moreover, we investigated the benefit of parallel test prioritization on the regression testing process of continuous integration, considering both the cumulative acceleration performance and the overhead of prioritization techniques, and the results demonstrate the superiority of parallel test prioritization.

scholarly journals Test prioritization in continuous integration environments

2018 ◽  
Vol 146 ◽  
pp. 80-98 ◽  
Author(s):  
Alireza Haghighatkhah ◽  
Mika Mäntylä ◽  
Markku Oivo ◽  
Pasi Kuvaja
Keyword(s):  
Continuous Integration ◽  
Test Prioritization

Hansie: Hybrid and consensus regression test prioritization

2021 ◽  
Vol 172 ◽  
pp. 110850
Author(s):  
Shouvick Mondal ◽  
Rupesh Nasre
Keyword(s):  
Regression Test ◽  
Test Prioritization

Automated System-Level Regression Test Prioritization in a Nutshell

IEEE Software ◽  
2017 ◽  
Vol 34 (4) ◽  
pp. 30-37 ◽  
Author(s):  
Per Erik Strandberg ◽  
Wasif Afzal ◽  
Thomas J. Ostrand ◽  
Elaine J. Weyuker ◽  
Daniel Sundmark
Keyword(s):  
Automated System ◽  
System Level ◽  
Regression Test ◽  
Test Prioritization

scholarly journals Requirements based test prioritization using risk factors: An industrial study

2016 ◽  
Vol 69 ◽  
pp. 71-83 ◽  
Author(s):  
Hema Srikanth ◽  
Charitha Hettiarachchi ◽  
Hyunsook Do
Keyword(s):  
Risk Factors ◽  
Test Prioritization
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