A tree-based classification model for analysis of a military software system

Tactical military software is required to have high reliability, Each software function is often considered mission-critical, and the lives of military personnel often depend on mission success. This paper presents a tree-based modeling method for identifying fault-prone software modules, which has been, applied to a subsystem of the Joint Surveillance Target Attack Radar System, JSTARS, a large tactical military system. We developed a decision tree model using software product metrics from one iteration of a spiral life cycle to predict whether or not each module in the next iteration would be considered fault-prone. Model results could be used to identify those modules that would probably benefit from extra reviews and testing, and thus, reduce the risk of discovering faults later on. Identifying fault-prone modules early in the development can lead to better reliability. High reliability of each iteration translates into a highly reliable final product. A decision tree also facilitates interpretation of software product metrics to characterize the fault-prone class. The decision tree was constructed using the TREED-ISC algorithm which as a refinement of the CHAID algorithm. This algorithm partitions the ranges of independent variables based on chi-squared tests with the dependent variable. In contrast to algorithms used by previous tree-based studies of software metric data, there is no restriction to binary trees, and statistically significant relationships with the dependent variable are the basis for branching.