A novel failure indexing approach with run-time values of program variables

TL;DR

This paper introduces a new failure indexing method that uses run-time program variable values to better distinguish failure causes, significantly improving fault estimation and clustering accuracy over existing techniques.

Contribution

The paper proposes a novel failure proximity based on program variable values and a corresponding indexing approach, outperforming current methods in effectiveness.

Findings

Achieves 44.12% and 27.59% improvements in faults number estimation.

Achieves 47.30% and 26.93% improvements in clustering effectiveness.

Demonstrates effectiveness in both simulated and real-world environments.

Abstract

Failures with different root causes can disturb multi-fault localization significantly, therefore, dividing failures into distinct groups according to the responsible faults is highly important. In such a failure indexing task, the crux lies in the failure proximity, which involves two points, i.e., how to effectively represent failures (e.g., extract the signature of failures) and how to properly measure the distance between the proxies for those failures. Existing studies have proposed a variety of failure proximities. The prevalent of them extract signatures of failures from execution coverage or suspiciousness ranking lists, and accordingly employ the Euclid or the Kendall tau distances. However, such strategies may not properly reflect the essential characteristics of failures, thus resulting in unsatisfactory effectiveness. In this paper, we propose a new failure proximity, namely, program variable-based failure proximity, and based on which present a novel failure indexing approach. Specifically, the proposed approach utilizes the run-time values of program variables to represent failures, and designs a set of rules to measure the similarity between them. Experimental results demonstrate the competitiveness of the proposed approach: it can achieve 44.12% and 27.59% improvements in faults number estimation, as well as 47.30% and 26.93% improvements in clustering effectiveness, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively.