Using code quality features to predict bugs in procedural software systems.

A wide range of metrics have been used as features to build bug (or fault) predictors. However, most of the existing predictors focus mostly on object-oriented (OO) systems, either because they rely on OO metrics or were evaluated mainly with OO systems. Procedural software systems (PSS), less addressed in bug prediction research, often suffer from maintainability problems because they typically consist of low-level applications, using for example preprocessors to cope with variability. Previous work evaluated sets of features (composed of static code metrics) proposed in existing approaches in the PSS context. However, explored metrics are limited to those that are part of traditional metric suites, being often associated with structural code properties. A type of information explored to a smaller extent in this context is the output of code quality tools that statically analyse source code, providing hints of code problems. In this paper, we investigate the use of information collected from quality tools to build bug predictors dedicated to PSS. We specify four features derived from code quality tools or associated with poor programming practices and evaluate the effectiveness of these features. Our evaluation shows that our proposed features improve bug predictors in our investigated context.