With the signigicant increase of computer and Internet based crimes, it becomes increasingly important to have techniques that can be applied in a legal setting to assist the court in making judgements about malware, theft of code and computer fraud. To better deal with author identification of software, we propose a semantic approach to identifying authorship through the comparison of program data flows. To do so, we compute program dependences, compute program similarity if detecting theft of code is needed, and thus query about not only the syntactic structure of programs but also the data flow within in order to discriminate authors. The experimental result reveals that our technique is more robust even with some intentional code modifications.
International audienceA typical approach to software fault location is to pinpoint buggy statements by comparing the failing program runs with some successful runs. Most of the research works in this line require a large amount of failing runs and successful runs. Those required execution data inevitably contain a large number of redundant or noisy execution paths, and thus leads to a lower efficiency and accuracy of pinpointing. In this paper, we present an improved fault localization method by statistical analysis of difference between reduced program runs. To do so, we first use a clustering method to eliminate the redundancy in execution paths, next calculate the statistics of difference between the reduced failing runs and successful runs, and then rank the buggy statements in a generated bug report. The experimental results show that our algorithm works many times faster than Wang's, and performs better than competitors in terms of accuracy
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