Fine-grained Code Coverage Measurement in Automated Black-box Android Testing
Today, there are millions of third-party Android applications. Some of these applications are buggy or even malicious. To identify such applications, novel frameworks for automated black-box testing and dynamic analysis are being developed by the Android community, including Google. Code coverage is...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Pilgun, Aleksandr Gadyatskaya, Olga Dashevskyi, Stanislav Zhauniarovich, Yury Kushniarou, Artsiom |
| description | Today, there are millions of third-party Android applications. Some of these
applications are buggy or even malicious. To identify such applications, novel
frameworks for automated black-box testing and dynamic analysis are being
developed by the Android community, including Google. Code coverage is one of
the most common metrics for evaluating effectiveness of these frameworks.
Furthermore, code coverage is used as a fitness function for guiding
evolutionary and fuzzy testing techniques. However, there are no reliable tools
for measuring fine-grained code coverage in black-box Android app testing.
We present the Android Code coVerage Tool, ACVTool for short, that
instruments Android apps and measures the code coverage in the black-box
setting at the class, method and instruction granularities. ACVTool has
successfully instrumented 96.9% of apps in our experiments. It introduces a
negligible instrumentation time overhead, and its runtime overhead is
acceptable for automated testing tools. We show in a large-scale experiment
with Sapienz, a state-of-art testing tool, that the fine-grained
instruction-level code coverage provided by ACVTool helps to uncover a larger
amount of faults than coarser-grained code coverage metrics. |
| doi_str_mv | 10.48550/arxiv.1812.10729 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1812_10729</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1812_10729</sourcerecordid><originalsourceid>FETCH-LOGICAL-a679-77c5bc66679436ff8df860f8a2253211b158640807ff8b1a4e5fdb5f5753377d3</originalsourceid><addsrcrecordid>eNotj0tOwzAYhL1hgQoHYIUv4OBH_OgyRBSQWrHJPvod25FF4yAnrcrtMYXNzEgzGulD6IHRqjZS0ifIl3iumGG8YlTz7S067GLyZMxQzOF2dr7I2WcYPT54WE7ZTz6tOCbcnNZ5grXMno8wfBI7X3CTXJ6jw51f1pjGO3QT4Lj4-3_foG730rVvZP_x-t42ewJKb4nWg7SDUiXXQoVgXDCKBgOcS8EZs0waVVNDdeksg9rL4KwMUkshtHZigx7_bq88_VeOE-Tv_perv3KJH7swR1w</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Fine-grained Code Coverage Measurement in Automated Black-box Android Testing</title><source>arXiv.org</source><creator>Pilgun, Aleksandr ; Gadyatskaya, Olga ; Dashevskyi, Stanislav ; Zhauniarovich, Yury ; Kushniarou, Artsiom</creator><creatorcontrib>Pilgun, Aleksandr ; Gadyatskaya, Olga ; Dashevskyi, Stanislav ; Zhauniarovich, Yury ; Kushniarou, Artsiom</creatorcontrib><description>Today, there are millions of third-party Android applications. Some of these
applications are buggy or even malicious. To identify such applications, novel
frameworks for automated black-box testing and dynamic analysis are being
developed by the Android community, including Google. Code coverage is one of
the most common metrics for evaluating effectiveness of these frameworks.
Furthermore, code coverage is used as a fitness function for guiding
evolutionary and fuzzy testing techniques. However, there are no reliable tools
for measuring fine-grained code coverage in black-box Android app testing.
We present the Android Code coVerage Tool, ACVTool for short, that
instruments Android apps and measures the code coverage in the black-box
setting at the class, method and instruction granularities. ACVTool has
successfully instrumented 96.9% of apps in our experiments. It introduces a
negligible instrumentation time overhead, and its runtime overhead is
acceptable for automated testing tools. We show in a large-scale experiment
with Sapienz, a state-of-art testing tool, that the fine-grained
instruction-level code coverage provided by ACVTool helps to uncover a larger
amount of faults than coarser-grained code coverage metrics.</description><identifier>DOI: 10.48550/arxiv.1812.10729</identifier><language>eng</language><subject>Computer Science - Cryptography and Security ; Computer Science - Software Engineering</subject><creationdate>2018-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1812.10729$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1812.10729$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Pilgun, Aleksandr</creatorcontrib><creatorcontrib>Gadyatskaya, Olga</creatorcontrib><creatorcontrib>Dashevskyi, Stanislav</creatorcontrib><creatorcontrib>Zhauniarovich, Yury</creatorcontrib><creatorcontrib>Kushniarou, Artsiom</creatorcontrib><title>Fine-grained Code Coverage Measurement in Automated Black-box Android Testing</title><description>Today, there are millions of third-party Android applications. Some of these
applications are buggy or even malicious. To identify such applications, novel
frameworks for automated black-box testing and dynamic analysis are being
developed by the Android community, including Google. Code coverage is one of
the most common metrics for evaluating effectiveness of these frameworks.
Furthermore, code coverage is used as a fitness function for guiding
evolutionary and fuzzy testing techniques. However, there are no reliable tools
for measuring fine-grained code coverage in black-box Android app testing.
We present the Android Code coVerage Tool, ACVTool for short, that
instruments Android apps and measures the code coverage in the black-box
setting at the class, method and instruction granularities. ACVTool has
successfully instrumented 96.9% of apps in our experiments. It introduces a
negligible instrumentation time overhead, and its runtime overhead is
acceptable for automated testing tools. We show in a large-scale experiment
with Sapienz, a state-of-art testing tool, that the fine-grained
instruction-level code coverage provided by ACVTool helps to uncover a larger
amount of faults than coarser-grained code coverage metrics.</description><subject>Computer Science - Cryptography and Security</subject><subject>Computer Science - Software Engineering</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj0tOwzAYhL1hgQoHYIUv4OBH_OgyRBSQWrHJPvod25FF4yAnrcrtMYXNzEgzGulD6IHRqjZS0ifIl3iumGG8YlTz7S067GLyZMxQzOF2dr7I2WcYPT54WE7ZTz6tOCbcnNZ5grXMno8wfBI7X3CTXJ6jw51f1pjGO3QT4Lj4-3_foG730rVvZP_x-t42ewJKb4nWg7SDUiXXQoVgXDCKBgOcS8EZs0waVVNDdeksg9rL4KwMUkshtHZigx7_bq88_VeOE-Tv_perv3KJH7swR1w</recordid><startdate>20181227</startdate><enddate>20181227</enddate><creator>Pilgun, Aleksandr</creator><creator>Gadyatskaya, Olga</creator><creator>Dashevskyi, Stanislav</creator><creator>Zhauniarovich, Yury</creator><creator>Kushniarou, Artsiom</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20181227</creationdate><title>Fine-grained Code Coverage Measurement in Automated Black-box Android Testing</title><author>Pilgun, Aleksandr ; Gadyatskaya, Olga ; Dashevskyi, Stanislav ; Zhauniarovich, Yury ; Kushniarou, Artsiom</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-77c5bc66679436ff8df860f8a2253211b158640807ff8b1a4e5fdb5f5753377d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Computer Science - Cryptography and Security</topic><topic>Computer Science - Software Engineering</topic><toplevel>online_resources</toplevel><creatorcontrib>Pilgun, Aleksandr</creatorcontrib><creatorcontrib>Gadyatskaya, Olga</creatorcontrib><creatorcontrib>Dashevskyi, Stanislav</creatorcontrib><creatorcontrib>Zhauniarovich, Yury</creatorcontrib><creatorcontrib>Kushniarou, Artsiom</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pilgun, Aleksandr</au><au>Gadyatskaya, Olga</au><au>Dashevskyi, Stanislav</au><au>Zhauniarovich, Yury</au><au>Kushniarou, Artsiom</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fine-grained Code Coverage Measurement in Automated Black-box Android Testing</atitle><date>2018-12-27</date><risdate>2018</risdate><abstract>Today, there are millions of third-party Android applications. Some of these
applications are buggy or even malicious. To identify such applications, novel
frameworks for automated black-box testing and dynamic analysis are being
developed by the Android community, including Google. Code coverage is one of
the most common metrics for evaluating effectiveness of these frameworks.
Furthermore, code coverage is used as a fitness function for guiding
evolutionary and fuzzy testing techniques. However, there are no reliable tools
for measuring fine-grained code coverage in black-box Android app testing.
We present the Android Code coVerage Tool, ACVTool for short, that
instruments Android apps and measures the code coverage in the black-box
setting at the class, method and instruction granularities. ACVTool has
successfully instrumented 96.9% of apps in our experiments. It introduces a
negligible instrumentation time overhead, and its runtime overhead is
acceptable for automated testing tools. We show in a large-scale experiment
with Sapienz, a state-of-art testing tool, that the fine-grained
instruction-level code coverage provided by ACVTool helps to uncover a larger
amount of faults than coarser-grained code coverage metrics.</abstract><doi>10.48550/arxiv.1812.10729</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.1812.10729 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_1812_10729 |
| source | arXiv.org |
| subjects | Computer Science - Cryptography and Security Computer Science - Software Engineering |
| title | Fine-grained Code Coverage Measurement in Automated Black-box Android Testing |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T21%3A08%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fine-grained%20Code%20Coverage%20Measurement%20in%20Automated%20Black-box%20Android%20Testing&rft.au=Pilgun,%20Aleksandr&rft.date=2018-12-27&rft_id=info:doi/10.48550/arxiv.1812.10729&rft_dat=%3Carxiv_GOX%3E1812_10729%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |