Accuracy Graphs of Spectrum-based Fault Localization Formulas

Abstract:The effectiveness of spectrum-based fault localization techniques primarily relies on the accuracy of their fault localiz¬ation formulas. Theoretical studies prove the relative accuracy orders of selected formulas under certain assumptions, forming a graph of their theoretical accuracy relations. However, it is not entirely clear to what extent these relations still hold in practice when some assumptions are invalid. On the other hand, empirical studies may measure the actual accuracy of any formula in controlled settings that more closely approximate practical scenarios. In this paper, we propose an empirical framework of accuracy graphs and their construction that reveal the relative accuracy of formulas. Our work not only evaluates the association between certain assumptions and the theoretical relations among formulas, but also expands our knowledge to reveal new potential accuracy relationships of other formulas which have not been theoretically analyzed. Using our proposed framework, we identified a list of formula pairs in which a formula is statistically consistently more accurate than or similar to another, enlightening directions for further theoretical analysis. 
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The effectiveness of spectrum-based fault localization techniques primarily relies on the accuracy of their fault localiz¬ation formulas. Theoretical studies prove the relative accuracy orders of selected formulas under certain assumptions, forming a graph of their theoretical accuracy relations. However, it is not entirely clear to what extent these relations still hold in practice when some assumptions are invalid. On the other hand, empirical studies may measure the actual accuracy of any formula in controlled settings that more closely approximate practical scenarios. In this paper, we propose an empirical framework of accuracy graphs and their construction that reveal the relative accuracy of formulas. Our work not only evaluates the association between certain assumptions and the theoretical relations among formulas, but also expands our knowledge to reveal new potential accuracy relationships of other formulas which have not been theoretically analyzed. Using our proposed framework, we identified a list of formula pairs in which a formula is statistically consistently more accurate than or similar to another, enlightening directions for further theoretical analysis. 

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