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However, these bug reports indeed contain high-quality search keywords in their texts even though they might not contain explicit hints for localizing bugs (e.g., stack traces). We confirmed that the state-of-the-art query construction approaches are indeed not sufficient for constructing appropriate queries (for bug localization) from certain natural language-only bug reports. In particular, we use a dataset of 2,320 bug reports, employ ten existing approaches from the literature, exploit the Genetic Algorithm-based approach to construct optimal, near-optimal search queries from these bug reports, and then answer three research questions. In this article, we attempted to clear the sky on this aspect by conducting an in-depth empirical study that critically examines the state-of-the-art query selection practices in IR-based bug localization. On the other hand, they cast serious doubt on the query selection practices in the IR-based bug localization. On one hand, these findings suggest that natural language-only bug reports might be a sufficient source for good query keywords. On the other hand, there is a piece of recent evidence that suggests that even these natural language-only reports contain enough good keywords that could help localize the bugs successfully. According to existing studies, IR-based approaches cannot perform well when they use these bug reports as search queries. A significant number of bug reports contain only plain natural language texts. However, the accuracy of these approaches heavily depends on their used bug reports.

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Bayesian Belief Network analysis (BBN) was used to model the omnidirectional relationships of sleep duration and physical activity to history of stroke.Being light-weight and cost-effective, IR-based approaches for bug localization have shown promise in finding software bugs. Method: Analysis was based on the 2004-2013 National Health Interview Survey (n=288,888). Such a model allowed us to develop a predictive classification model of stroke which can be used in post-stroke risk stratification and developing targeted stroke rehabilitation care based on an individualвЂ™s profile. The current study utilized Bayesian Belief Network analysis, a type of machine learning analysis, to develop profiles of physical activity (duration, intensity, and frequency) and sleep duration associated with or no history of stroke, given the influence of multiple stroke predictors and correlates. Though the interrelationships between PA, sleep, and other stroke risk factors have been studied, we are unclear about the associations of different types, frequency and duration of PA, sleep behavioral patterns (short, average and long sleep durations), within the context of stroke-related clinical, behavioral, and socio-demographic risk factors. Introduction: Physical activity (PA) and sleep are associated with cerebrovascular disease and events like stroke. Our work gives a theoretical explanation for the widespread empirical phenomenon of persistency for this LP relaxation. We design an algorithm to find stable blocks, and find that these real instances have large stable regions. We complement our theoretical results with an empirical evaluation of real-world MAP inference instances from computer vision. Under this block stability condition, we prove that the pairwise LP relaxation is persistent on the stable blocks.

In this paper, we introduce a significantly more relaxed condition that only requires blocks (portions) of an input instance to be stable. Unfortunately, this strong condition does not seem to be satisfied in practice. The simplest stability condition assumes that the MAP solution does not change at all when some of the pairwise potentials are (adversarially) perturbed. To understand the empirical success of approximate MAP inference, recent work (Lang et al., 2018) has shown that some popular approximation algorithms perform very well when the input instance is stable.

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