1 research outputs found
Building Confidence in Scientific Computing Software Via Assurance Cases
Assurance cases provide an organized and explicit argument for correctness.
They can dramatically improve the certification of Scientific Computing
Software (SCS). Assurance cases have already been effectively used for safety
cases for real time systems. Their advantages for SCS include engaging domain
experts, producing only necessary documentation, and providing evidence that
can be verified/replicated. This paper illustrates assurance cases for SCS
through the correctness case for 3dfim+, an existing Medical Imaging
Application (MIA) for analyzing activity in the brain. This example was partly
chosen because of recent concerns about the validity of fMRI (Functional
Magnetic Resonance Imaging) studies. The example justifies the value of
assurance cases for SCS, since the existing documentation is shown to have
ambiguities and omissions, such as an incompletely defined ranking function and
missing details on the coordinate system. A serious concern for 3dfim+ is
identified: running the software does not produce any warning about the
necessity of using data that matches the parametric statistical model employed
for the correlation calculations. Raising the bar for SCS in general, and MIA
in particular, is both feasible and necessary - when software impacts safety,
an assurance case methodology (or an equivalently rigorous confidence building
methodology) should be employed.Comment: 17 pages, 9 figures, submitted to Reliability Engineering & Safety
System