Biotechnological Approaches To Software Health: Applying Bioinformatics And Machine Learning To Predict And Mitigate System Failures

Abstract

Incorporating bioinformatics and AI approaches has provided a fresh concept for addressing and preventing software failure while promoting healthier software systems. One promising field where these algorithms can be utilized is bioinformatics, which, in addition to studying biological data, can also detect patterns to find deviations in software systems. One is the use of AI, especially in artificial neural networks and decision trees, to develop predictive models of potential failures based on historical data so as to take preventative measures. This planned approach can be likened to proactive maintenance, whereby problems are detected and addressed before they cause a breakdown, hence reducing downtime and increasing the reliability of the systems. Hence, bioinformatics integrated with AI shows great potential for sustainable development of SHM approaches that are more reliable, responsive, and efficient to guarantee the reliability of complex software systems ultimately.

Published Date: 23 February 2022