Estimating the probability of failure when testing reveals no failures
Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution; and prior assumptions about the probability of failure of the software. The formulas are not restricted to eq...
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| Veröffentlicht in: | IEEE transactions on software engineering 1992-01, Vol.18 (1), p.33-43 |
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| container_end_page | 43 |
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| container_title | IEEE transactions on software engineering |
| container_volume | 18 |
| creator | Miller, K.W. Morell, L.J. Noonan, R.E. Park, S.K. Nicol, D.M. Murrill, B.W. Voas, M. |
| description | Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution; and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.< > |
| doi_str_mv | 10.1109/32.120314 |
| format | Article |
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These formulas incorporate random testing results, information about the input distribution; and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.< ></description><identifier>ISSN: 0098-5589</identifier><identifier>EISSN: 1939-3520</identifier><identifier>DOI: 10.1109/32.120314</identifier><identifier>CODEN: IESEDJ</identifier><language>eng</language><publisher>Legacy CDMS: IEEE</publisher><subject>Application software ; Applied sciences ; Bayesian analysis ; Bayesian methods ; Computer errors ; Computer programming ; Computer science ; Computer science; control theory; systems ; Estimates ; Exact sciences and technology ; Failure ; Mathematical models ; Methods ; NASA ; Probability ; Probability density function ; Quality Assurance And Reliability ; Software ; Software engineering ; Software reliability ; Software reusability ; Software testing ; System testing ; Testing</subject><ispartof>IEEE transactions on software engineering, 1992-01, Vol.18 (1), p.33-43</ispartof><rights>1993 INIST-CNRS</rights><rights>Copyright Institute of Electrical and Electronics Engineers, Inc. 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These formulas incorporate random testing results, information about the input distribution; and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.< ></description><subject>Application software</subject><subject>Applied sciences</subject><subject>Bayesian analysis</subject><subject>Bayesian methods</subject><subject>Computer errors</subject><subject>Computer programming</subject><subject>Computer science</subject><subject>Computer science; control theory; systems</subject><subject>Estimates</subject><subject>Exact sciences and technology</subject><subject>Failure</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>NASA</subject><subject>Probability</subject><subject>Probability density function</subject><subject>Quality Assurance And Reliability</subject><subject>Software</subject><subject>Software engineering</subject><subject>Software reliability</subject><subject>Software reusability</subject><subject>Software testing</subject><subject>System 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Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Miller, K.W.</au><au>Morell, L.J.</au><au>Noonan, R.E.</au><au>Park, S.K.</au><au>Nicol, D.M.</au><au>Murrill, B.W.</au><au>Voas, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating the probability of failure when testing reveals no failures</atitle><jtitle>IEEE transactions on software engineering</jtitle><stitle>TSE</stitle><date>1992-01</date><risdate>1992</risdate><volume>18</volume><issue>1</issue><spage>33</spage><epage>43</epage><pages>33-43</pages><issn>0098-5589</issn><eissn>1939-3520</eissn><coden>IESEDJ</coden><abstract>Formulas for estimating the probability of failure when testing reveals no errors are introduced. These formulas incorporate random testing results, information about the input distribution; and prior assumptions about the probability of failure of the software. The formulas are not restricted to equally likely input distributions, and the probability of failure estimate can be adjusted when assumptions about the input distribution change. The formulas are based on a discrete sample space statistical model of software and include Bayesian prior assumptions. Reusable software and software in life-critical applications are particularly appropriate candidates for this type of analysis.< ></abstract><cop>Legacy CDMS</cop><pub>IEEE</pub><doi>10.1109/32.120314</doi><tpages>11</tpages></addata></record> |
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| identifier | ISSN: 0098-5589 |
| ispartof | IEEE transactions on software engineering, 1992-01, Vol.18 (1), p.33-43 |
| issn | 0098-5589 1939-3520 |
| language | eng |
| recordid | cdi_proquest_journals_195563898 |
| source | IEEE Electronic Library (IEL) |
| subjects | Application software Applied sciences Bayesian analysis Bayesian methods Computer errors Computer programming Computer science Computer science control theory systems Estimates Exact sciences and technology Failure Mathematical models Methods NASA Probability Probability density function Quality Assurance And Reliability Software Software engineering Software reliability Software reusability Software testing System testing Testing |
| title | Estimating the probability of failure when testing reveals no failures |
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