Bug prediction is helpful for facilitating bug fixes
and improving the efficiency in software development and maintenance.
In the past decades, researchers have proposed numerous
studies on bug prediction by using code metrics. However, most
of the existing studies use syntax-based metrics, there exists little
work building bug prediction models with semantic metrics from
source code. In this paper, we propose a new model, semantic
dependency graph (SDG), to represent semantic relationships
among source files. Based on the SDG, we formally define a
suite of semantic metrics reflecting semantic characteristics of
a project’s source files. Moreover, we create a tool to automate
the generation of our proposed SDG-based metrics. Through our
experimental studies, we have demonstrated that the SDG-based
semantic metrics are effective for building bug prediction models,
and the SDG-based metrics outperform traditional syntactic
metrics on bug prediction. In addition, models using the SDGbased
metrics could achieve a better prediction performance
than two state-of-the-art models that learn semantic features
automatically. Finally, we have also presented that our approach
is applicable in practice in terms of execution time and space.

Formal Definition and Automatic Generation of Semantic Metrics: An Empirical Study on Bug Prediction

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