Graph-based seed object synthesis for search-based unit testing
Search-based software testing (SBST) generates tests using search algorithms guided by measurements gauging how far a test case is away from exercising a coverage goal. The effectiveness of SBST largely depends on the continuity and monotonicity of the fitness landscape decided by these measurements...
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sg-smu-ink.sis_research-72152021-10-14T06:03:06Z Graph-based seed object synthesis for search-based unit testing LIN, Yun ONG, You Seng SUN, Jun FRASER, Gordon DONG, Jin Song Search-based software testing (SBST) generates tests using search algorithms guided by measurements gauging how far a test case is away from exercising a coverage goal. The effectiveness of SBST largely depends on the continuity and monotonicity of the fitness landscape decided by these measurements and the search operators. Unfortunately, the fitness landscape is challenging when the function under test takes object inputs, as classical measurements hardly provide guidance for constructing legitimate object inputs. To overcome this problem, we propose test seeds, i.e., test code skeletons of legitimate objects which enable the use of classical measurements. Given a target branch in a function under test, we first statically analyze the function to build an object construction graph that captures the relation between the operands of the target method and the states of their relevant object inputs. Based on the graph, we synthesize test template code where each “slot” is a mutation point for the search algorithm. This approach can be seamlessly integrated with existing SBST algorithms, and we implemented EvoObj on top of the well-known EvoSuite unit test generation tool. Our experiments show that EvoObj outperforms EvoSuite with statistical significance on 2,750 methods taken from 103 open source Java projects using state-of-the-art SBST algorithms. 2021-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6212 info:doi/10.1145/3468264.3468619 https://ink.library.smu.edu.sg/context/sis_research/article/7215/viewcontent/fse21.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University object oriented software testing search-based code synthesis Software Engineering |
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object oriented software testing search-based code synthesis Software Engineering LIN, Yun ONG, You Seng SUN, Jun FRASER, Gordon DONG, Jin Song Graph-based seed object synthesis for search-based unit testing |
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Search-based software testing (SBST) generates tests using search algorithms guided by measurements gauging how far a test case is away from exercising a coverage goal. The effectiveness of SBST largely depends on the continuity and monotonicity of the fitness landscape decided by these measurements and the search operators. Unfortunately, the fitness landscape is challenging when the function under test takes object inputs, as classical measurements hardly provide guidance for constructing legitimate object inputs. To overcome this problem, we propose test seeds, i.e., test code skeletons of legitimate objects which enable the use of classical measurements. Given a target branch in a function under test, we first statically analyze the function to build an object construction graph that captures the relation between the operands of the target method and the states of their relevant object inputs. Based on the graph, we synthesize test template code where each “slot” is a mutation point for the search algorithm. This approach can be seamlessly integrated with existing SBST algorithms, and we implemented EvoObj on top of the well-known EvoSuite unit test generation tool. Our experiments show that EvoObj outperforms EvoSuite with statistical significance on 2,750 methods taken from 103 open source Java projects using state-of-the-art SBST algorithms. |
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LIN, Yun ONG, You Seng SUN, Jun FRASER, Gordon DONG, Jin Song |
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LIN, Yun ONG, You Seng SUN, Jun FRASER, Gordon DONG, Jin Song |
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LIN, Yun |
title |
Graph-based seed object synthesis for search-based unit testing |
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Graph-based seed object synthesis for search-based unit testing |
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Graph-based seed object synthesis for search-based unit testing |
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Graph-based seed object synthesis for search-based unit testing |
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Graph-based seed object synthesis for search-based unit testing |
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graph-based seed object synthesis for search-based unit testing |
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Institutional Knowledge at Singapore Management University |
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2021 |
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https://ink.library.smu.edu.sg/sis_research/6212 https://ink.library.smu.edu.sg/context/sis_research/article/7215/viewcontent/fse21.pdf |
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