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A Visualization Analytical Framework for Software Fault Localization Metrics

The core of Spectra-Based Fault Localization (SBFL) is suspiciousness metric, expressed as a formula to calculate the fault proneness for each program component. Current analysis works on metrics mainly focus on the comparison of their performances based on algebraic reasoning. However, due to the high complexity of real-life programs, there are still challenges in the practical application of SBFL. This paper emphasizes a further exploration of the mechanism of SBFL metrics. We propose a visualization-based framework for metric analyses, in which metrics are interpreted by curves in the identified spectra space, and their performance can be illustrated by geometric properties. Based on the framework, we design a basic approach for metric analysis following the procedures: visualizing representative SBFL instances → generalizing geometric knowledge → obtaining useful guidance. Due to the advantages of visualization, we can get explainable and essential knowledge about SBFL. In particular, we make a comparative analysis among typical metrics and, compared with algebraic reasoning, obtain not only the comparison results but also the explanation about why a metric can outperform others as well as new theoretical findings such as the optimality of continuous maximal metrics. Finally, we make an extended discussion about the possible way to study the influence of fault interferences on SBFL, which indicates the extensibility of our framework.