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Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA)
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Debugging with Probabilistic Event Structures
Get PDFDebugging is a search process to find, understand and fix the root cause of software defects. Can debugging benefit from probabilistic information? We hypothesize that debugging activities can benefit from probabilistic information that capture the statistical dependence of program features and the minor variations of program behavior. This probabilistic information helps to guide the search for the root cause of the bug and provides detailed diagnostic information (such as failure-inducing inputs and method calls leading to the fault). To realize our hypothesis, we propose to improve debugging activities by guiding bug diagnosis using both probabilistic reasoning and program analysis. The main idea is to mine probabilistic information from program executions, then apply these information to construct probabilistic event structures (e.g. probabilistic call graphs) that guides debugging activities such as fault localization and comprehension. The resulting probabilistic model will guide bug diagnosis towards the most likely paths to the root cause of bugs and provide contextual diagnostic information
Offline Equivalence: A Non-preemptive Scheduling Technique for Resource-Constrained Embedded Real-Time Systems
No full textWe consider the problem of scheduling a set of nonpreemptive periodic tasks in an embedded system with a limited amount of memory. On the one hand, due to the memory limitations, a table-based scheduling approach might not be applicable, and on the other hand, the existing online non-preemptive scheduling algorithms are either not efficient in terms of the schedulability ratio, or suffer from considerable runtime overhead. To arrive at a compromise, this paper proposes an online policy that is equivalent to a given offline table to combine some of the advantages of both online and offline scheduling: we first consider a low-overhead online scheduling algorithm as a baseline, and then identify any irregular situations where a given offline table differs from the schedule generated by the online algorithm. We store any such irregularities in tables for use by the online scheduling algorithm, which then can recreate the table at runtime. To generate suitable tables, we provide an offline scheduling algorithm for nonpreemptive tasks, and a table-transformation algorithm to reduce the number of irregularities that must be stored. In an evaluation using an Arduino board and synthetic task sets, we have observed the technique to result in a substantial reduction of scheduling overhead compared toCW-EDF, the online scheduler that achieves the highest schedulability ratio, while having to store on average only a few dozen to a few hundreds of bytes of the static schedule
