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DTSTART:20210314T030000
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DESCRIPTION:FREE Webinar\n\nTraditional software reliability growth models 
 enable quantitative assessment of the software testing process by characte
 rizing defect detection in terms of testing time or effort. However\, the 
 majority of these parametric models do not identify specific testing activ
 ities underlying defect discovery and thus can only provide general guidan
 ce on how to incrementally allocate effort. This talk presents a non-homog
 eneous Poisson process software reliability growth model incorporating cov
 ariates based on the discrete Cox proportional hazards model\, which expli
 citly links test activities to defect discovery. Efficient and stable expe
 ctation conditional maximization algorithms are derived to estimate the nu
 merical parameters of a model that best characterize the failure data coll
 ected during testing. An optimal test activity allocation problem is formu
 lated to maximize defects discovered\, so that they can be corrected prior
  to release. An overview of the Covariate Software Failure and Reliability
  Assessment Tool (C-SFRAT) will also be provided.\n\nSpeaker(s): Lance Fio
 ndella\, \n\nAgenda: \n11:00 AM Technical Presentation\n\n12:00 PM Adjourn
 ment\n\nVirtual: https://events.vtools.ieee.org/m/247948
LOCATION:Virtual: https://events.vtools.ieee.org/m/247948
ORGANIZER:michael.bannan@ieee.org
SEQUENCE:4
SUMMARY:Webinar - Covariate Software Reliability Models and Applications
URL;VALUE=URI:https://events.vtools.ieee.org/m/247948
X-ALT-DESC:Description: <br /><p><strong><em>FREE Webinar</em></strong></p>
 \n<p>Traditional software reliability growth models enable quantitative as
 sessment of the software testing process by characterizing defect detectio
 n in terms of testing time or effort. However\, the majority of these para
 metric models do not identify specific testing activities underlying defec
 t discovery and thus can only provide general guidance on how to increment
 ally allocate effort. This talk presents a non-homogeneous Poisson process
  software reliability growth model incorporating covariates based on the d
 iscrete Cox proportional hazards model\, which explicitly links test activ
 ities to defect discovery. Efficient and stable expectation conditional ma
 ximization algorithms are derived to estimate the numerical parameters of 
 a model that best characterize the failure data collected during testing. 
 An optimal test activity allocation problem is formulated to maximize defe
 cts discovered\, so that they can be corrected prior to release. An overvi
 ew of the Covariate Software Failure and Reliability Assessment Tool (C-SF
 RAT) will also be provided.</p><br /><br />Agenda: <br /><p>1<strong>1:00 
 AM</strong>&nbsp\;&nbsp\;&nbsp\;Technical Presentation</p>\n<p><strong>12:
 00 PM</strong>&nbsp\;&nbsp\;&nbsp\;Adjournment</p>
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