Software has become a central part of nearly all sectors of economic activity, and our daily lives have become increasingly dependent on complex software-intensive systems and services. Failures in these systems can affect thousands or even millions of people and lead to massive damages. Despite significant investments in software verification and validation (V&V), the software industry is still plagued by failures. One reason is that conventional V&V can only target anticipated faults: it can only check that the conditions corresponding to known or expected problems do not occur. However, the complexity of modern software makes it impossible to anticipate all problems that could be encountered. The overall goal of this project is to devise novel methods and techniques to create self-healing software-intensive systems, i.e. systems that support autonomous detection, diagnosis, and containment of unanticipated faults during execution, thereby significantly increasing their dependability, robustness, and resilience. We reach this goal by building on the concept of an artificial immune system to achieve three scientific break-throughs: (1) Autonomic techniques that can detect unanticipated faults by distinguishing between normal behavior and anomalies in runtime observational data. (2) Adaptive learning techniques that make it easier to recognize faults that are similar to the ones that have seen before. (3) Cost-effective techniques to diagnose the root causes of a fault and to contain its impact, both inside and outside the system. Timeliness: Recent advances in machine learning together with the PI's new results on automatically learning patterns in high volume data and generalizing them using rule aggregation [23 in project description] make that now is the best time to start this research. These failures need to be addressed, and the global state-of-the-art was not at the required level to start this ambitious research undertaking until just recently.
Project leader: Leon Moonen
Category: Øvrige forskningsinstitutter
Institution: SIMULA RESEARCH LABORATORY AS