Generic and Robust Localization of Multidimensional Root Causes Operators of online software services periodically collect various measures with many attributes. When a measure becomes abnormal, indicating service problems such as reliability degrade, operators would like to rapidly and accurately localize the root cause attribute combinations within a huge multidimensional search space. Unfortunately, previous approaches are not generic or robust in that they all suffer from impractical root cause assumptions, handling only directly collected measures but not derived ones, handling only anomalies with signicant magnitudes but not those insignicant but important ones, requiring manual parameter netuning, or being too slow. This paper proposes a generic and robust multidimensional root cause localization approach, Squeeze, that overcomes all above limitations, the first in the literature. Through our novel bottomup then topdown searching strategy and the techniques based on our proposed generalized ripple effect and generalized potential score, Squeeze is able to reach a good trade off between search speed and accuracy in a generic and robust manner. Case studies in several banks and an Internet company show that Squeeze can localize root causes much more rapidly and accurately than the traditional manual analysis. Furthermore, our extensive experiments on semisynthetic datasets show that the F1score of Squeeze outperforms previous approaches by 0.4 on average, while its localization time is only about 10 seconds

Combining SpectrumBased Fault Localization and Statistical Debugging: An Empirical Study Program debugging is a timeconsuming task, and researchers have proposed different kinds of automatic fault localization techniques to mitigate the burden of manual debugging. Among these techniques, two popular families are spectrumbased fault localization (SBFL) and statistical debugging (SD), both localizing faults by collecting statistical information at runtime. Though the ideas are similar, the two families have been developed independently and their combinations have not been systematically explored. In this paper we perform a systematical empirical study on the combination of SBFL and SD. We first build a unified model of the two techniques, and systematically explore four types of variations, different predicates, different risk evaluation formulas, different granularities of data collection, and different methods of combining suspicious scores. Our study leads to several findings. First, most of the effectiveness of the combined approach contributed by a simple type of predicates: branch conditions. Second, the risk evaluation formulas of SBFL significantly outperform that of SD. Third, finegrained data collection significantly outperforms coarsegrained data collection with a little extra execution overhead. Fourth, a linear combination of SBFL and SD predicates outperforms both individual approaches. According to our empirical study, we propose a new fault localization approach, PREDFL (Predicatebased Fault Localization), with the best configuration for each dimension under the unified model. Then, we explore its complementarity to existing techniques by integrating PREDFL with a stateoftheart fault localization framework. The experimental results show that PREDFL can further improve the effectiveness of stateoftheart fault localization techniques. More concretely, integrating PREDFL results in an up to 20.8% improvement w.r.t the faults successfully located at Top1, which reveals that PREDFL complements existing techniques.