Mutation-based fault localization (MBFL) is an automated fault localization method that has been extensively studied in recent years.The intuition behind MBFL is based on the assumption that mutation operations can correct faults in a program.However, this assumption has only been experimented and validated on simulated datasets, and whether it truly holds in the real world has never been investigated.Fault types in simulated datasets are simple and differ significantly from the complex and diverse faults found in realworld programs.Therefore, to investigate whether MBFL works in the real world, it is necessary to validate its intuition in the real world.The goal of this study is to analyze whether the intuition of MBFL still holds in the real world.We quantified the MBFL intuition by establishing an algorithm, which eliminated the interference of factors unrelated to MBFL itself, allowing us to directly validate the intuition of MBFL.Based on this algorithm, we conducted extensive experiments on both real-world programs and programs in simulated datasets.The results revealed an interesting trend: due to the complexity of faults in real-world programs compared to those in simulated datasets, MBFL's intuition probably cannot hold in the real world.This indicates that MBFL's intuition is difficult to hold in the real world.Consequently, we focused on analyzing the real-world faulty versions and summarized a set of mutation operators that perform better in the real world by studying the types and effects of each mutant, providing guidance for the application of MBFL.

Failures with different root causes can greatly disrupt multi-fault localization, therefore, categorizing failures into distinct groups according to the culprit fault is highly important. In such a failure indexing task, the crux lies in the failure proximity, which comprises two points, i.e., how to effectively represent failures (e.g., extract the signature of failures) and how to properly measure the distance between those proxies for failures. Existing research has proposed a variety of failure proximities. The majority of them extract signatures of failures from execution coverage or suspiciousness ranking lists, and accordingly employ the Euclid or the Kendall tau distances, etc. 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, the program variable-based failure proximity, and further present a novel failure indexing approach, ReClues. Specifically, ReClues 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 ReClues: it can achieve 44.12% and 27.59% improvements in faults number estimation, as well as 47.56% and 26.27% improvements in clustering effectiveness, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively.

Existing fault localization techniques typically analyze static information and run-time profiles of faulty software programs, and subsequently calculate suspiciousness values for each program entity. Such strategies typically have overbroad information to be analyzed and lead to unsatisfactory results. Exception is a widely-used programming language feature. It is closely related to the execution status during the execution of programs, and thus can be incorporated into automatic fault localization techniques for better effectiveness. Based on this intuition, we propose EXPECT, a novel fault localization technique that makes use of exception information, a valuable source of data for fault localization while being often ignored in previous research. Specifically, EXPECT first constructs exception trigger streams (including exception trigger information and execution traces), and then localizes faults by tracing bifurcation points between different exception trigger streams. Moreover, the tie-breaking problem can be also benefited from the use of exception trigger streams. Experimental results demonstrate the advantages of EXPECT: it achieves as high as 38.26% improvements in localizing faults regarding the Exam metric in comparison to the state-of-the-art fault localization technique, and it reduces the scales of ties in existing FL methods by up to 99.08%.