Risky Module Estimation in Safety-Critical Software
Software used in safety-critical system must have high dependability. Software testing and V&V (Verification and Validation) activities are very important for assuring high software quality. If we can predict the risky modules in safety-critical software, testing activities and regulation activi...
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| creator | Young-Mi Kim Choong-Heui Jeong A-Rang Jeong Hyeon Soo Kim |
| description | Software used in safety-critical system must have high dependability. Software testing and V&V (Verification and Validation) activities are very important for assuring high software quality. If we can predict the risky modules in safety-critical software, testing activities and regulation activities can be applied to them more intensively. In this paper, we classify the estimated risk classes which can be used for deep testing and V&V. We predict the risk class for each module using support vector machines. We can consider that the modules classified to risk class 5 or 4 are more risky than others relatively. For all classification error rates, we expect that the results can be useful and practical for software testing, V&V, and activities for regulatory reviews. In the future works, to improve the practicality, we will have to investigate other machine learning algorithms and datasets. |
| doi_str_mv | 10.1109/ICIS.2009.83 |
| format | Conference Proceeding |
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Software testing and V&V (Verification and Validation) activities are very important for assuring high software quality. If we can predict the risky modules in safety-critical software, testing activities and regulation activities can be applied to them more intensively. In this paper, we classify the estimated risk classes which can be used for deep testing and V&V. We predict the risk class for each module using support vector machines. We can consider that the modules classified to risk class 5 or 4 are more risky than others relatively. For all classification error rates, we expect that the results can be useful and practical for software testing, V&V, and activities for regulatory reviews. In the future works, to improve the practicality, we will have to investigate other machine learning algorithms and datasets.</description><identifier>ISBN: 0769536417</identifier><identifier>ISBN: 9780769536415</identifier><identifier>DOI: 10.1109/ICIS.2009.83</identifier><language>eng</language><publisher>IEEE</publisher><subject>Classification tree analysis ; Kernel ; Machine learning ; Safety-Critical Software ; Software metrics ; Software quality ; Software safety ; Software testing ; Software V&V ; Space technology ; Support vector machine classification ; Support vector machines ; SVM</subject><ispartof>2009 Eighth IEEE/ACIS International Conference on Computer and Information Science, 2009, p.967-970</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5223201$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5223201$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Young-Mi Kim</creatorcontrib><creatorcontrib>Choong-Heui Jeong</creatorcontrib><creatorcontrib>A-Rang Jeong</creatorcontrib><creatorcontrib>Hyeon Soo Kim</creatorcontrib><title>Risky Module Estimation in Safety-Critical Software</title><title>2009 Eighth IEEE/ACIS International Conference on Computer and Information Science</title><addtitle>ICIS</addtitle><description>Software used in safety-critical system must have high dependability. Software testing and V&V (Verification and Validation) activities are very important for assuring high software quality. If we can predict the risky modules in safety-critical software, testing activities and regulation activities can be applied to them more intensively. In this paper, we classify the estimated risk classes which can be used for deep testing and V&V. We predict the risk class for each module using support vector machines. We can consider that the modules classified to risk class 5 or 4 are more risky than others relatively. For all classification error rates, we expect that the results can be useful and practical for software testing, V&V, and activities for regulatory reviews. In the future works, to improve the practicality, we will have to investigate other machine learning algorithms and datasets.</description><subject>Classification tree analysis</subject><subject>Kernel</subject><subject>Machine learning</subject><subject>Safety-Critical Software</subject><subject>Software metrics</subject><subject>Software quality</subject><subject>Software safety</subject><subject>Software testing</subject><subject>Software V&V</subject><subject>Space technology</subject><subject>Support vector machine classification</subject><subject>Support vector machines</subject><subject>SVM</subject><isbn>0769536417</isbn><isbn>9780769536415</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjk9LwzAcQAMiqNtu3rzkC7TmlzT_jlKmFibCqueRJr9AtK7SRKTf3oF7l3d7PEJugdUAzN53bdfXnDFbG3FBbphWVgrVgL4im5w_2IlGMmnkNRH7lD8X-jKFnxHpNpf05UqajjQdae8ilqVq51SSdyPtp1h-3YxrchndmHFz9oq8P27f2udq9_rUtQ-7KoGWpVLOBu8GY5Tllnvr_KA0oJWIYoiCNSBBcEQGUQ4YAGJwuglcumi80kasyN1_NyHi4Xs-rc3LQXIuOAPxB8WPQiM</recordid><startdate>200906</startdate><enddate>200906</enddate><creator>Young-Mi Kim</creator><creator>Choong-Heui Jeong</creator><creator>A-Rang Jeong</creator><creator>Hyeon Soo Kim</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200906</creationdate><title>Risky Module Estimation in Safety-Critical Software</title><author>Young-Mi Kim ; Choong-Heui Jeong ; A-Rang Jeong ; Hyeon Soo Kim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-6a9dcab8869292c9acb671e95ee3bf30415132ee01f5bed11fda74d25af8c6783</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Classification tree analysis</topic><topic>Kernel</topic><topic>Machine learning</topic><topic>Safety-Critical Software</topic><topic>Software metrics</topic><topic>Software quality</topic><topic>Software safety</topic><topic>Software testing</topic><topic>Software V&V</topic><topic>Space technology</topic><topic>Support vector machine classification</topic><topic>Support vector machines</topic><topic>SVM</topic><toplevel>online_resources</toplevel><creatorcontrib>Young-Mi Kim</creatorcontrib><creatorcontrib>Choong-Heui Jeong</creatorcontrib><creatorcontrib>A-Rang Jeong</creatorcontrib><creatorcontrib>Hyeon Soo Kim</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Young-Mi Kim</au><au>Choong-Heui Jeong</au><au>A-Rang Jeong</au><au>Hyeon Soo Kim</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Risky Module Estimation in Safety-Critical Software</atitle><btitle>2009 Eighth IEEE/ACIS International Conference on Computer and Information Science</btitle><stitle>ICIS</stitle><date>2009-06</date><risdate>2009</risdate><spage>967</spage><epage>970</epage><pages>967-970</pages><isbn>0769536417</isbn><isbn>9780769536415</isbn><abstract>Software used in safety-critical system must have high dependability. Software testing and V&V (Verification and Validation) activities are very important for assuring high software quality. If we can predict the risky modules in safety-critical software, testing activities and regulation activities can be applied to them more intensively. In this paper, we classify the estimated risk classes which can be used for deep testing and V&V. We predict the risk class for each module using support vector machines. We can consider that the modules classified to risk class 5 or 4 are more risky than others relatively. For all classification error rates, we expect that the results can be useful and practical for software testing, V&V, and activities for regulatory reviews. In the future works, to improve the practicality, we will have to investigate other machine learning algorithms and datasets.</abstract><pub>IEEE</pub><doi>10.1109/ICIS.2009.83</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Classification tree analysis Kernel Machine learning Safety-Critical Software Software metrics Software quality Software safety Software testing Software V&V Space technology Support vector machine classification Support vector machines SVM |
| title | Risky Module Estimation in Safety-Critical Software |
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