Preparing measurements of legacy software for predicting operational faults

Abstract

Software quality modeling can be used by a software maintenance project to identify a limited set of software modules that probably need improvement. A model's goal is to recommend a set of modules to receive special treatment. The purpose of the paper is to report our experiences modeling software quality with classification trees, including necessary preprocessing of data. We conducted a case study on two releases of a very large legacy telecommunications system. A module was considered fault-prone if any faults were discovered by customers, and not fault-prone otherwise. Software product, process, and execution metrics were the basis for predictors. The TREEDISC algorithm for building classification trees was investigated, because it emphasizes statistical significance. Numeric data, such as software metrics, are not suitable for TREEDISC. Consequently, we transformed measurements into discrete ordinal predictors by grouping. This case study investigated the sensitivity of modeling results to various groupings. We found that robustness, accuracy, and parsimony of the models were influenced by the maximum number of groups. Models based on two sets of candidate predictors had similar sensitivity.