ECOOP 2010 – Object-Oriented Programming: 24th European by Doug Lea (auth.), Theo D’Hondt (eds.)

By Doug Lea (auth.), Theo D’Hondt (eds.)

This ebook constitutes the refereed lawsuits of the twenty fourth eu convention on Object-Oriented Programming, ECOOP 2010, held in Maribor, Slovenia, in June 2010. The 24 revised complete papers, provided including one prolonged summary have been rigorously reviewed and chosen from a complete of 108 submissions. The papers disguise subject matters akin to programming environments and instruments, theoretical foundations of programming languages, formal tools, concurrency types in Java, empirical equipment, variety structures, language layout and implementation, concurrency abstractions and studies.

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Extra info for ECOOP 2010 – Object-Oriented Programming: 24th European Conference, Maribor, Slovenia, June 21-25, 2010. Proceedings

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.

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