Spectra-based fault localization (SFL) techniques have brought encouraging results and a variety of program spectra have been proposed to locate faults. Different types of abnormal behaviors may be revealed by different kinds of spectra. Compared to techniques using single spectra type, techniques combining multiple types of spectra try to leverage the strengths of the constituent types. However, in the presence of multiple kinds of spectra, how to select adequate spectra type and build appropriate models need further investigation.In this paper, we propose an SFL technique LOUPE, which uses multiple spectra-specific models. Both control and data dependences are introduced to capture unusual behaviors of faults. In the stage of suspiciousness modeling, in contrast with previous studies, we build different models to evaluate the suspiciousness of statements for each spectra type respectively. Finally, since the fault type is unknown in advance, suspiciousness scores are calculated based on the two models. We evaluate LOUPE on the Siemens benchmark and experimental results show that our technique is promising.
Proximity query is an integral part of any motion planning algorithm and takes up the majority of planning time. Due to performance issues, most existing planners perform queries at fixed sampled configurations, sometimes resulting in missed collisions. Moreover, randomly determining collision-free configurations makes it difficult to obtain samples close to, or on, the surface of C-obstacles in the configuration space. In this paper, we present an efficient and practical local planning method in contact space which uses "continuous collision detection" (CCD). We show how, using the precise contact information provided by a CCD algorithm, a randomized planner can be enhanced by efficiently sampling the contact space, as well as by constraining the sampling when the roadmap is expanded. We have included our contact-space planning methods in a freely available stateof-the-art planning library-the Stanford MPK library. We have been able to observe that in complex scenarios involving cluttered and narrow passages, which are typically difficult for randomized planners, the enhanced planner offers up to 70 times performance improvement when our contact-space sampling and constrained sampling methods are enabled.
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