Analyzing error-prone system structure

Using measures of data interaction called data bindings, the authors quantify ratios of coupling and strength in software systems and use the ratios to identify error-prone system structures. A 148000 source line system from a prediction environment was selected for empirical analysis. Software error data were collected from high-level system design through system testing and from field operation of the system. The authors use a set of five tools to calculate the data bindings automatically and use a clustering technique to determine a hierarchical description of each of the system's 77 subsystems. A nonparametric analysis of variance model is used to characterize subsystems and individual routines that had either many or few errors or high or low error correction effort. The empirical results support the effectiveness of the data bindings clustering approach for localizing error-prone system structure.<
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