Detection of software modules with high debug code churn in a very large legacy system

Abstract

Society has become so dependent on reliable telecommunications, that failures can risk loss of emergency service, business disruptions, or isolation from friends. Consequently, telecommunications software is required to have high reliability. Many previous studies define the classification fault prone in terms of fault counts. This study defines fault prone as exceeding a threshold of debug code churn, defined as the number of lines added or changed due to bug fixes. Previous studies have characterized reuse history with simple categories. This study quantified new functionality with lines of code. The paper analyzes two consecutive releases of a large legacy software system for telecommunications. We applied discriminant analysis to identify fault prone modules based on 16 static software product metrics and the amount of code changed during development. Modules from one release were used as a fit data set and modules from the subsequent release were used as a test data set. In contrast, comparable prior studies of legacy systems split the data to simulate two releases. We validated the model with a realistic simulation of utilization of the fitted model with the test data set. Model results could be used to give extra attention to fault prone modules and thus, reduce the risk of unexpected problems.