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Automatic Test Smell Detection using Information Retrieval Techniques. / Palomba, Fabio; Zaidman, Andy; De Lucia, Andrea.

Proceedings of the International Conference on Software Maintenance and Evolution (ICSME). Piscataway, NJ : IEEE, 2018. p. 311-322.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Palomba, F, Zaidman, A & De Lucia, A 2018, Automatic Test Smell Detection using Information Retrieval Techniques. in Proceedings of the International Conference on Software Maintenance and Evolution (ICSME). IEEE, Piscataway, NJ, pp. 311-322. https://doi.org/10.1109/ICSME.2018.00040

APA

Palomba, F., Zaidman, A., & De Lucia, A. (2018). Automatic Test Smell Detection using Information Retrieval Techniques. In Proceedings of the International Conference on Software Maintenance and Evolution (ICSME) (pp. 311-322). Piscataway, NJ: IEEE. https://doi.org/10.1109/ICSME.2018.00040

Vancouver

Palomba F, Zaidman A, De Lucia A. Automatic Test Smell Detection using Information Retrieval Techniques. In Proceedings of the International Conference on Software Maintenance and Evolution (ICSME). Piscataway, NJ: IEEE. 2018. p. 311-322 https://doi.org/10.1109/ICSME.2018.00040

Author

Palomba, Fabio ; Zaidman, Andy ; De Lucia, Andrea. / Automatic Test Smell Detection using Information Retrieval Techniques. Proceedings of the International Conference on Software Maintenance and Evolution (ICSME). Piscataway, NJ : IEEE, 2018. pp. 311-322

BibTeX

@inproceedings{68e82e648a044d7fbbe95f79c4c0cc7a,
title = "Automatic Test Smell Detection using Information Retrieval Techniques",
abstract = "Software testing is a key activity to control the reliability of production code. Unfortunately, the effectiveness of test cases can be threatened by the presence of faults. Recent work showed that static indicators can be exploited to identify testrelated issues. In particular test smells, i.e., sub-optimal design choices applied by developers when implementing test cases, have been shown to be related to test case effectiveness. While some approaches for the automatic detection of test smells have been proposed so far, they generally suffer of poor performance: as a consequence, current detectors cannot properly provide support to developers when diagnosing the quality of test cases. In this paper, we aim at making a step ahead toward the automated detection of test smells by devising a novel textual-based detector, coined TASTE (Textual AnalySis for Test smEll detection), with the aim of evaluating the usefulness of textual analysis fordetecting three test smell types, General Fixture, Eager Test, and Lack of Cohesion of Methods. We evaluate TASTE in an empirical study that involves a manually-built dataset composed of 494 test smell instances belonging to 12 software projects, comparing the capabilities of our detector with those of two code metrics-based techniques proposed by Van Rompaey et al. and Greiler et al.Our results show that the structural-based detection applied by existing approaches cannot identify most of the test smells in our dataset, while TASTE is up to 44{\%} more effective. Finally, we find that textual and structural approaches can identify different sets of test smells, thereby indicating complementarity.",
keywords = "Test smells, Empirical Studies, Mining Software, Repositories",
author = "Fabio Palomba and Andy Zaidman and {De Lucia}, Andrea",
year = "2018",
doi = "10.1109/ICSME.2018.00040",
language = "English",
pages = "311--322",
booktitle = "Proceedings of the International Conference on Software Maintenance and Evolution (ICSME)",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - Automatic Test Smell Detection using Information Retrieval Techniques

AU - Palomba, Fabio

AU - Zaidman, Andy

AU - De Lucia, Andrea

PY - 2018

Y1 - 2018

N2 - Software testing is a key activity to control the reliability of production code. Unfortunately, the effectiveness of test cases can be threatened by the presence of faults. Recent work showed that static indicators can be exploited to identify testrelated issues. In particular test smells, i.e., sub-optimal design choices applied by developers when implementing test cases, have been shown to be related to test case effectiveness. While some approaches for the automatic detection of test smells have been proposed so far, they generally suffer of poor performance: as a consequence, current detectors cannot properly provide support to developers when diagnosing the quality of test cases. In this paper, we aim at making a step ahead toward the automated detection of test smells by devising a novel textual-based detector, coined TASTE (Textual AnalySis for Test smEll detection), with the aim of evaluating the usefulness of textual analysis fordetecting three test smell types, General Fixture, Eager Test, and Lack of Cohesion of Methods. We evaluate TASTE in an empirical study that involves a manually-built dataset composed of 494 test smell instances belonging to 12 software projects, comparing the capabilities of our detector with those of two code metrics-based techniques proposed by Van Rompaey et al. and Greiler et al.Our results show that the structural-based detection applied by existing approaches cannot identify most of the test smells in our dataset, while TASTE is up to 44% more effective. Finally, we find that textual and structural approaches can identify different sets of test smells, thereby indicating complementarity.

AB - Software testing is a key activity to control the reliability of production code. Unfortunately, the effectiveness of test cases can be threatened by the presence of faults. Recent work showed that static indicators can be exploited to identify testrelated issues. In particular test smells, i.e., sub-optimal design choices applied by developers when implementing test cases, have been shown to be related to test case effectiveness. While some approaches for the automatic detection of test smells have been proposed so far, they generally suffer of poor performance: as a consequence, current detectors cannot properly provide support to developers when diagnosing the quality of test cases. In this paper, we aim at making a step ahead toward the automated detection of test smells by devising a novel textual-based detector, coined TASTE (Textual AnalySis for Test smEll detection), with the aim of evaluating the usefulness of textual analysis fordetecting three test smell types, General Fixture, Eager Test, and Lack of Cohesion of Methods. We evaluate TASTE in an empirical study that involves a manually-built dataset composed of 494 test smell instances belonging to 12 software projects, comparing the capabilities of our detector with those of two code metrics-based techniques proposed by Van Rompaey et al. and Greiler et al.Our results show that the structural-based detection applied by existing approaches cannot identify most of the test smells in our dataset, while TASTE is up to 44% more effective. Finally, we find that textual and structural approaches can identify different sets of test smells, thereby indicating complementarity.

KW - Test smells

KW - Empirical Studies

KW - Mining Software

KW - Repositories

U2 - 10.1109/ICSME.2018.00040

DO - 10.1109/ICSME.2018.00040

M3 - Conference contribution

SP - 311

EP - 322

BT - Proceedings of the International Conference on Software Maintenance and Evolution (ICSME)

PB - IEEE

CY - Piscataway, NJ

ER -

ID: 46728571