Predicting access to persistent objects through static code analysis
Document typeConference report
Rights accessOpen Access
European Commission's projectBigStorage - BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data (EC-H2020-642963)
In this paper, we present a fully-automatic, high-accuracy approach to predict access to persistent objects through static code analysis of object-oriented applications. The most widely-used previous technique uses a simple heuristic to make the predictions while approaches that offer higher accuracy are based on monitoring application execution. These approaches add a non-negligible overhead to the application’s execution time and/or consume a considerable amount of memory. By contrast, we demonstrate in our experimental study that our proposed approach offers better accuracy than the most common technique used to predict access to persistent objects, and makes the predictions farther in advance, without performing any analysis during application execution
CitationTouma, R., Queralt, A., Cortés, A., Pérez, M. Predicting access to persistent objects through static code analysis. A: Conference on Advances in Databases and Information Systems. "New Trends in Databases and Information Systems: ADBIS 2017: short papers and workshops, AMSD, BigNovelTI, DAS, SW4CH, DC: Nicosia, Cyprus, September 24-27, 2017: proceedings". Springer, 2017, p. 54-62.
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