A hybrid methodology to detect memory leaks in soft real-time embedded systems software

Abstract

As embedded software becomes complex and time to production needs to be minimized, early fixing of flaws in a software design is important. Memory leaks are the most important memory-related problems commonly occurring in embedded software development. We propose a novel hybrid automated memory leak detection approach for soft real-time embedded system software. Our approach combines static and dynamic methodologies to overcome their individual limitations. The static phase generates potential memory leak warnings with the help of source code annotation and control flow graphs. The dynamic phase involves simulation of abstracted memory behaviour with data collected in an abstract memory model (AMM). Actual leaks are determined from the potential leak warnings generated in the static phase. The dynamic simulation phase makes our approach faster and enables early phase leak detection. Our approach is platform independent and evaluation shows that it is more accurate than existing tools.