Abstract-The dependability properties of a software system are usually assessed and refined towards the end of the software development lifecycle. Problems pertaining to software dependability may necessitate costly system redesign. Hence, early insights into the potential for error propagation within a software system would be beneficial. Further, the refinement of the dependability properties of software involves the design and location of dependability components called detectors and correctors. Recently, a metric, called spatial impact, has been proposed to capture the extent of error propagation in a software system, providing insights into the location of detectors and correctors. However, the metric only provides insights towards the end of the software development life cycle. In this paper, our objective is to investigate whether spatial impact can enable the early identification of locations for detectors. To achieve this we first hypothesise that spatial impact is correlated with module coupling, a metric that can be evaluated early in the software development life cycle, and show this relationship to hold. We then evaluate module coupling for the modules of a complex software system, identifying modules with high coupling values as potential locations for detectors. We then enhanced these modules with detectors and perform fault-injection analysis to determine the suitability of these locations. The results presented demonstrate that our approach can permit the early identification of possible detector locations.
A module coupling has been used to propose a spatial impact metric to capture the extent of error propagation in a software system by identifying the location of dependability components called detectors and correctors at early stage of software system development REF .
The Early Identification of Detector Locations in Dependable Software
2011 IEEE 22nd International Symposium on Software Reliability Engineering
2011 IEEE 22nd International Symposium on Software Reliability Engineering
Computer Science