Software quality assurance becomes especially critical if bioinformatics tools are to be used in a translational medical setting, such as analysis and interpretation of WGS data. We must ensure that only validated algorithms are used, and that they are implemented correctly in the analysis pipeline – and not disrupted by hardware or software failure. Recently it has been shown that theconcordance of multiple widely used variant-calling pipelines is very low (<60% in SNP calling, and <30% in indel calling). Considering there is only one ground truth, the high level of discrepancy is troubling, and is telling us that even the most popular bioinformatics tools to date can generate results with a non-negligible false positives or false negative rate. In this talk, we will give examples on our recent experience in implementing QA in commonly used bioinformatics programs in genomic medicine and systems biology. We will discuss explore how concepts and tools in the software testing field can be adapted to perform quality assurance in genomic medicine and systems biology.