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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

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.

Original language	English
Title of host publication	Reuse in the Big Data Era - 18th International Conference on Software and Systems Reuse, ICSR 2019, Proceedings
Editors	Xin Peng, Apostolos Ampatzoglou, Tanmay Bhowmik
Pages	100-111
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-030-22888-0_8
State	Published - 2019
Event	18th International Conference on Software and Systems Reuse, ICSR 2019 - Cincinnati, United States Duration: Jun 26 2019 → Jun 28 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11602 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Software and Systems Reuse, ICSR 2019
Country/Territory	United States
City	Cincinnati
Period	6/26/19 → 6/28/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Funding

in part by the NSF grant CCF-

Funders	Funder number
National Science Foundation (NSF)
California Community Foundation

Keywords

Android apps
Bug reports
Steps to reproduce

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

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

Cite this

Zhao, Y., Miller, K., Yu, T., Zheng, W., & Pu, M. (2019). Automatically Extracting Bug Reproducing Steps from Android Bug Reports. In 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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author = "Yu Zhao and Kye Miller and Tingting Yu and Wei Zheng and Minchao Pu",

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Zhao, Y, Miller, K, Yu, T, Zheng, W & Pu, M 2019, Automatically Extracting Bug Reproducing Steps from Android Bug Reports. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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Zhao Y, Miller K, Yu T, Zheng W, Pu M. Automatically Extracting Bug Reproducing Steps from Android Bug Reports. In Peng X, Ampatzoglou A, Bhowmik T, editors, 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

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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