Building Confidence in Scientific Computing Software Via Assurance Cases
This work addresses the need for rigorous confidence-building methodologies in scientific computing software, particularly for safety-critical applications like medical imaging, though it is incremental as it applies an existing assurance case approach to a new domain.
The paper tackles the problem of ensuring correctness in scientific computing software by proposing assurance cases, demonstrating their value through an analysis of the 3dfim+ medical imaging application, which revealed ambiguities and omissions in existing documentation and a serious concern about missing warnings for data-model mismatches.
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.