AGA: An Accelerated Greedy Additional Algorithm for Test Case Prioritization

In recent years, many test case prioritization (TCP) techniques have been proposed to speed up the process of fault detection. However, little work has taken the efficiency problem of these techniques into account. In this paper, we target the Greedy Additional (GA) algorithm, which has been widely...

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Veröffentlicht in:IEEE transactions on software engineering Jg. 48; H. 12; S. 5102 - 5119
Hauptverfasser: Li, Feng, Zhou, Jianyi, Li, Yinzhu, Hao, Dan, Zhang, Lu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2022
IEEE Computer Society
Schlagworte:
acceleration
additional strategy
Algorithms
Codes
Data structures
Effectiveness
Efficiency
Fault detection
Genetic algorithms
Greedy algorithms
Iterative methods
Software algorithms
Software engineering
Test case prioritization
Testing
Time complexity
Web and internet services
ISSN:0098-5589, 1939-3520
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Abstract In recent years, many test case prioritization (TCP) techniques have been proposed to speed up the process of fault detection. However, little work has taken the efficiency problem of these techniques into account. In this paper, we target the Greedy Additional (GA) algorithm, which has been widely recognized to be effective but less efficient, and try to improve its efficiency while preserving effectiveness. In our Accelerated GA (AGA) algorithm, we use some extra data structures to reduce redundant data accesses in the GA algorithm and thus the time complexity is reduced from <inline-formula><tex-math notation="LaTeX">\mathcal {O}(m^2n)</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>m</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="hao-ieq1-3137929.gif"/> </inline-formula> to <inline-formula><tex-math notation="LaTeX">\mathcal {O}(kmn)</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mi>k</mml:mi><mml:mi>m</mml:mi><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="hao-ieq2-3137929.gif"/> </inline-formula> when <inline-formula><tex-math notation="LaTeX">n > m</tex-math> <mml:math><mml:mrow><mml:mi>n</mml:mi><mml:mo>></mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="hao-ieq3-3137929.gif"/> </inline-formula>, where <inline-formula><tex-math notation="LaTeX">m</tex-math> <mml:math><mml:mi>m</mml:mi></mml:math><inline-graphic xlink:href="hao-ieq4-3137929.gif"/> </inline-formula> is the number of test cases, <inline-formula><tex-math notation="LaTeX">n</tex-math> <mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="hao-ieq5-3137929.gif"/> </inline-formula> is the number of program elements, and <inline-formula><tex-math notation="LaTeX">k</tex-math> <mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="hao-ieq6-3137929.gif"/> </inline-formula> is the iteration number. Moreover, we observe the impact of iteration numbers on prioritization efficiency on our dataset and propose to use a specific iteration number in the AGA algorithm to further improve the efficiency. We conducted experiments on 55 open-source subjects. In particular, we implemented each TCP algorithm with two kinds of widely-used input formats, adjacency matrix and adjacency list. Since a TCP algorithm with adjacency matrix is less efficient than the algorithm with adjacency list, the result analysis is mainly conducted based on TCP algorithms with adjacency list. The results show that AGA achieves 5.95X speedup ratio over GA on average, while it achieves the same average effectiveness as GA in terms of Average Percentage of Fault Detected (APFD). Moreover, we conducted an industrial case study on 22 subjects, collected from Baidu, and find that the average speedup ratio of AGA over GA is 44.27X, which indicates the practical usage of AGA in real-world scenarios.
AbstractList In recent years, many test case prioritization (TCP) techniques have been proposed to speed up the process of fault detection. However, little work has taken the efficiency problem of these techniques into account. In this paper, we target the Greedy Additional (GA) algorithm, which has been widely recognized to be effective but less efficient, and try to improve its efficiency while preserving effectiveness. In our Accelerated GA (AGA) algorithm, we use some extra data structures to reduce redundant data accesses in the GA algorithm and thus the time complexity is reduced from <inline-formula><tex-math notation="LaTeX">\mathcal {O}(m^2n)</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>m</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="hao-ieq1-3137929.gif"/> </inline-formula> to <inline-formula><tex-math notation="LaTeX">\mathcal {O}(kmn)</tex-math> <mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mi>k</mml:mi><mml:mi>m</mml:mi><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="hao-ieq2-3137929.gif"/> </inline-formula> when <inline-formula><tex-math notation="LaTeX">n > m</tex-math> <mml:math><mml:mrow><mml:mi>n</mml:mi><mml:mo>></mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="hao-ieq3-3137929.gif"/> </inline-formula>, where <inline-formula><tex-math notation="LaTeX">m</tex-math> <mml:math><mml:mi>m</mml:mi></mml:math><inline-graphic xlink:href="hao-ieq4-3137929.gif"/> </inline-formula> is the number of test cases, <inline-formula><tex-math notation="LaTeX">n</tex-math> <mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="hao-ieq5-3137929.gif"/> </inline-formula> is the number of program elements, and <inline-formula><tex-math notation="LaTeX">k</tex-math> <mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="hao-ieq6-3137929.gif"/> </inline-formula> is the iteration number. Moreover, we observe the impact of iteration numbers on prioritization efficiency on our dataset and propose to use a specific iteration number in the AGA algorithm to further improve the efficiency. We conducted experiments on 55 open-source subjects. In particular, we implemented each TCP algorithm with two kinds of widely-used input formats, adjacency matrix and adjacency list. Since a TCP algorithm with adjacency matrix is less efficient than the algorithm with adjacency list, the result analysis is mainly conducted based on TCP algorithms with adjacency list. The results show that AGA achieves 5.95X speedup ratio over GA on average, while it achieves the same average effectiveness as GA in terms of Average Percentage of Fault Detected (APFD). Moreover, we conducted an industrial case study on 22 subjects, collected from Baidu, and find that the average speedup ratio of AGA over GA is 44.27X, which indicates the practical usage of AGA in real-world scenarios.
In recent years, many test case prioritization (TCP) techniques have been proposed to speed up the process of fault detection. However, little work has taken the efficiency problem of these techniques into account. In this paper, we target the Greedy Additional (GA) algorithm, which has been widely recognized to be effective but less efficient, and try to improve its efficiency while preserving effectiveness. In our Accelerated GA (AGA) algorithm, we use some extra data structures to reduce redundant data accesses in the GA algorithm and thus the time complexity is reduced from [Formula Omitted] to [Formula Omitted] when [Formula Omitted], where [Formula Omitted] is the number of test cases, [Formula Omitted] is the number of program elements, and [Formula Omitted] is the iteration number. Moreover, we observe the impact of iteration numbers on prioritization efficiency on our dataset and propose to use a specific iteration number in the AGA algorithm to further improve the efficiency. We conducted experiments on 55 open-source subjects. In particular, we implemented each TCP algorithm with two kinds of widely-used input formats, adjacency matrix and adjacency list. Since a TCP algorithm with adjacency matrix is less efficient than the algorithm with adjacency list, the result analysis is mainly conducted based on TCP algorithms with adjacency list. The results show that AGA achieves 5.95X speedup ratio over GA on average, while it achieves the same average effectiveness as GA in terms of Average Percentage of Fault Detected (APFD). Moreover, we conducted an industrial case study on 22 subjects, collected from Baidu, and find that the average speedup ratio of AGA over GA is 44.27X, which indicates the practical usage of AGA in real-world scenarios.
Author Li, Feng
Hao, Dan
Zhang, Lu
Zhou, Jianyi
Li, Yinzhu
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crossref_primary_10_1002_smr_2708
crossref_primary_10_1007_s11390_024_2967_1
crossref_primary_10_1016_j_jss_2022_111419
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Snippet In recent years, many test case prioritization (TCP) techniques have been proposed to speed up the process of fault detection. However, little work has taken...
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SubjectTerms acceleration
additional strategy
Algorithms
Codes
Data structures
Effectiveness
Efficiency
Fault detection
Genetic algorithms
Greedy algorithms
Iterative methods
Software algorithms
Software engineering
Test case prioritization
Testing
Time complexity
Web and internet services
Title AGA: An Accelerated Greedy Additional Algorithm for Test Case Prioritization
URI https://ieeexplore.ieee.org/document/9662236
https://www.proquest.com/docview/2748564162
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