Automatically Extracting Bug Reproducing Steps from Android Bug Reports

Yu Zhao; Kye Miller; Tingting Yu; Wei Zheng; Minchao Pu

doi:10.1007/978-3-030-22888-0_8

Automatically Extracting Bug Reproducing Steps from Android Bug Reports

Yu Zhao
, Kye Miller
, Tingting Yu
, Wei Zheng
, Minchao Pu

Computer Science

Producción científica: Conference contribution › revisión exhaustiva

19 Citas (Scopus)

Resumen

Many modern software projects use bug-tracking systems (e.g., Bugzilla, Google Code Issue Tracker) to track software issues and help developers reproduce these issues. There has been recent work on automatically translating the natural language text (i.e., steps to reproduce) of bug reports to reproducing scripts, targeted at Android apps, to facilitate app debugging process. The scripts describe the event sequences leading to the app issues and thus can be reused for testing newer versions of the apps. However, existing techniques require manually providing the text description of steps to reproduce for generating reproducing scripts, which is a non-trivial task because natural language text in bug reports can be complex and contain much information irrelevant for bug reproduction. In this paper, we propose an approach that can automatically extract the text description of steps to reproduce (S2R) from bug reports to advance automated software issue diagnosis and test script reuse. The approach is implemented as a tool, called S2RMiner, which combines HTML parsing, natural language processing, and machine learning techniques. We have evaluated S2RMiner on 1000 original Android bug reports. The results show that S2RMiner can extract S2R with high accuracy.

Idioma original	English
Título de la publicación alojada	Reuse in the Big Data Era - 18th International Conference on Software and Systems Reuse, ICSR 2019, Proceedings
Editores	Xin Peng, Apostolos Ampatzoglou, Tanmay Bhowmik
Páginas	100-111
Número de páginas	12
DOI	https://doi.org/10.1007/978-3-030-22888-0_8
Estado	Published - 2019
Evento	18th International Conference on Software and Systems Reuse, ICSR 2019 - Cincinnati, United States Duración: jun 26 2019 → jun 28 2019

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	11602 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conference

Conference	18th International Conference on Software and Systems Reuse, ICSR 2019
País/Territorio	United States
Ciudad	Cincinnati
Período	6/26/19 → 6/28/19

Nota bibliográfica

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Financiación

in part by the NSF grant CCF-

Financiadores
National Science Foundation (NSF)
California Community Foundation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Acceder al documento

10.1007/978-3-030-22888-0_8

Otros archivos y enlaces

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Zhao, Y., Miller, K., Yu, T., Zheng, W., & Pu, M. (2019). Automatically Extracting Bug Reproducing Steps from Android Bug Reports. En X. Peng, A. Ampatzoglou, & T. Bhowmik (Eds.), Reuse in the Big Data Era - 18th International Conference on Software and Systems Reuse, ICSR 2019, Proceedings (pp. 100-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11602 LNCS). https://doi.org/10.1007/978-3-030-22888-0_8

Zhao, Yu ; Miller, Kye ; Yu, Tingting et al. / Automatically Extracting Bug Reproducing Steps from Android Bug Reports. Reuse in the Big Data Era - 18th International Conference on Software and Systems Reuse, ICSR 2019, Proceedings. editor / Xin Peng ; Apostolos Ampatzoglou ; Tanmay Bhowmik. 2019. pp. 100-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Automatically Extracting Bug Reproducing Steps from Android Bug Reports",

abstract = "Many modern software projects use bug-tracking systems (e.g., Bugzilla, Google Code Issue Tracker) to track software issues and help developers reproduce these issues. There has been recent work on automatically translating the natural language text (i.e., steps to reproduce) of bug reports to reproducing scripts, targeted at Android apps, to facilitate app debugging process. The scripts describe the event sequences leading to the app issues and thus can be reused for testing newer versions of the apps. However, existing techniques require manually providing the text description of steps to reproduce for generating reproducing scripts, which is a non-trivial task because natural language text in bug reports can be complex and contain much information irrelevant for bug reproduction. In this paper, we propose an approach that can automatically extract the text description of steps to reproduce (S2R) from bug reports to advance automated software issue diagnosis and test script reuse. The approach is implemented as a tool, called S2RMiner, which combines HTML parsing, natural language processing, and machine learning techniques. We have evaluated S2RMiner on 1000 original Android bug reports. The results show that S2RMiner can extract S2R with high accuracy.",

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Zhao, Y, Miller, K, Yu, T, Zheng, W & Pu, M 2019, Automatically Extracting Bug Reproducing Steps from Android Bug Reports. En X Peng, A Ampatzoglou & T Bhowmik (eds.), Reuse in the Big Data Era - 18th International Conference on Software and Systems Reuse, ICSR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11602 LNCS, pp. 100-111, 18th International Conference on Software and Systems Reuse, ICSR 2019, Cincinnati, United States, 6/26/19. https://doi.org/10.1007/978-3-030-22888-0_8

Automatically Extracting Bug Reproducing Steps from Android Bug Reports. / Zhao, Yu; Miller, Kye; Yu, Tingting et al.
Reuse in the Big Data Era - 18th International Conference on Software and Systems Reuse, ICSR 2019, Proceedings. ed. / Xin Peng; Apostolos Ampatzoglou; Tanmay Bhowmik. 2019. p. 100-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11602 LNCS).

Producción científica: Conference contribution › revisión exhaustiva

TY - GEN

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AU - Miller, Kye

AU - Yu, Tingting

AU - Zheng, Wei

AU - Pu, Minchao

PY - 2019

Y1 - 2019

N2 - Many modern software projects use bug-tracking systems (e.g., Bugzilla, Google Code Issue Tracker) to track software issues and help developers reproduce these issues. There has been recent work on automatically translating the natural language text (i.e., steps to reproduce) of bug reports to reproducing scripts, targeted at Android apps, to facilitate app debugging process. The scripts describe the event sequences leading to the app issues and thus can be reused for testing newer versions of the apps. However, existing techniques require manually providing the text description of steps to reproduce for generating reproducing scripts, which is a non-trivial task because natural language text in bug reports can be complex and contain much information irrelevant for bug reproduction. In this paper, we propose an approach that can automatically extract the text description of steps to reproduce (S2R) from bug reports to advance automated software issue diagnosis and test script reuse. The approach is implemented as a tool, called S2RMiner, which combines HTML parsing, natural language processing, and machine learning techniques. We have evaluated S2RMiner on 1000 original Android bug reports. The results show that S2RMiner can extract S2R with high accuracy.

AB - Many modern software projects use bug-tracking systems (e.g., Bugzilla, Google Code Issue Tracker) to track software issues and help developers reproduce these issues. There has been recent work on automatically translating the natural language text (i.e., steps to reproduce) of bug reports to reproducing scripts, targeted at Android apps, to facilitate app debugging process. The scripts describe the event sequences leading to the app issues and thus can be reused for testing newer versions of the apps. However, existing techniques require manually providing the text description of steps to reproduce for generating reproducing scripts, which is a non-trivial task because natural language text in bug reports can be complex and contain much information irrelevant for bug reproduction. In this paper, we propose an approach that can automatically extract the text description of steps to reproduce (S2R) from bug reports to advance automated software issue diagnosis and test script reuse. The approach is implemented as a tool, called S2RMiner, which combines HTML parsing, natural language processing, and machine learning techniques. We have evaluated S2RMiner on 1000 original Android bug reports. The results show that S2RMiner can extract S2R with high accuracy.

KW - Android apps

KW - Bug reports

KW - Steps to reproduce

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Zhao Y, Miller K, Yu T, Zheng W, Pu M. Automatically Extracting Bug Reproducing Steps from Android Bug Reports. En Peng X, Ampatzoglou A, Bhowmik T, editores, Reuse in the Big Data Era - 18th International Conference on Software and Systems Reuse, ICSR 2019, Proceedings. 2019. p. 100-111. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-22888-0_8

Automatically Extracting Bug Reproducing Steps from Android Bug Reports

Resumen

Serie de la publicación

Conference

Nota bibliográfica

Financiación

ASJC Scopus subject areas

Acceder al documento

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