A Hybrid Approach to Fine-grained Automated Fault Localization

Abstract

Fault localization is to identify faulty source code. It could be done on various granularities, e.g., classes, methods, and statements. Most of the automated fault localization (AFL) approaches are coarsegrained because it is challenging to accurately locate fine-grained faulty software elements, e.g., statements. However, some automated approaches, e.g., automated program repair (APR), require fine-grained fault localization. As a result, such APR approaches often leverage the traditional spectrum-based fault localization (SBFL) techniques although more advanced coarse-grained AFL approaches have been proposed recently. SBFL, based on dynamic execution of test cases only, is simple, intuitive, and generic (working on various granularities). However, its accuracy deserves significant improvement. To this end, in this paper, we propose a hybrid fine-grained AFL approach based on both dynamic spectrums and static statement types. The rationale of the approach is that some types of statements are significantly more/less error-prone than others, and thus statement types could be exploited for fault localization. On a crop of faulty programs, we compute the error-proneness for each type of statements, and assign priorities to special statement types that are steadily more/less error-prone than others. For a given faulty program under test, we first leverage traditional spectrum-based fault localization algorithm to identify all suspicious statements and to compute their suspicious scores. For each of the resulting suspicious statements, we retrieve its statement type as well as the special priority associated with the type. The final suspicious score is the product of the SBFL suspicious score and the priority assigned to the statement type. A significant advantage of the approach is that it is simple and intuitive, making it efficient and easy to interpret/implement. We evaluate the proposed approach on widely used benchmark Defects4J. The evaluation results suggest that the proposed approach outperforms widely used SBFL, reducing the absolute waste effort (AWE) by 9.3% on average.