By Jean-Marie Jacquet (auth.)
This booklet offers a brand new concurrent good judgment programming language referred to as Conclog and a technique for programming in it. Conclog used to be designed bytaking the fitting good points of common sense programming as reference, instead of via concentrating at once on operational tips to confirm potency. This ends up in fairly effortless declarative programming. quandary for soundness and completeness guarantees that multi-directional and multi-solution right methods could be coded relatively straightforwardly. Assuming compatible hypotheses on their makes use of, they are often remodeled into very effective models due to the advent of applicable keep an eye on info. Correctness-preserving homes are given for that function. The designof Conclog consists of 3 steps. First, a legitimate and whole parallel execution version of Horn clause courses is proposed which reduces subgoals of conjunctions independently and makes use of a calculus for reconciling inconsistent bindings. moment, this is often prolonged to deal with negation as thoroughly as attainable. 3rd, a minimum and fairly whole set of extra-logical positive aspects are outlined for optimization and functional reasons. the second one a part of the e-book offers a technique for developing Conclog in a rigorous approach. This covers the full programming method, from casual requisites through good judgment descriptions to effective courses. The method may be tailored to different languages.
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Additional info for Conclog: A Methodological Approach to Concurrent Logic Programming
Sample text
Next, we execute the fully instrumented translet (instrumented for taint initialization, data-taint propagation, and control-taint propagation) on the faulty input. We use the xsltc command-line API. The execution of the instrumented translet produces an annotated taint log. For a data-taint tag, the taint information contains either a taint mark, or an association to an intermediate variable created and used in the XSL transform. The taint information for a variable tag may itself contain either taint marks, or associations to other intermediate variables.
The failure) is observed. For a model-to-text transform, such a relevant context is appropriate because a transform typically builds the output text in a string buffer b that is printed out to a file at the end of the transformation. If the fault localization were to start at the output statement and the string buffer b as the relevant variable—for example, as the penumbra tool would do—the entire input model would be identified as the fault space. In our approach, the relevant context for fault localization is an error marker.
This case corresponds to path 1 → 3 → 5 in Figure 2. In a large model that contains thousands of elements and attributes, locating such subtle faults can be very difficult. Next, we present our approach that guides the user in locating such input-model faults. 3 Dynamic Taint Analysis for Debugging Figure 4 presents an overview of our approach. Given a transform program P and a failure-inducing input model I, the approach requires the user to identify error markers, which indicate the points in the output string at which a substring is missing or an incorrect substring is generated.