Dynamic program analysis encompasses the development of techniques and tools for analyzing computer software by exploiting information gathered from a program at runtime. The impressive amounts of data collected by dynamic analysis tools require efficient indexing and compression schemes, as well as on-line algorithmic techniques for mining relevant information on-the-fly in order to identify frequent events, hidden software patterns, or undesirable behaviors corresponding to bugs, malware, or intrusions. The paper explores how recent results in algorithmic theory for data-intensive scenarios can be applied to the design and implementation of dynamic program analysis tools, focusing on two important techniques: sampling and streaming.

Software streams: Big data challenges in dynamic program analysis / Finocchi, Irene. - Proc. Computability in Europe (CiE 2013), (2013), pp. 124-134. (9th Conference on Computability in Europe, Milan, Italy, July 1 -5, 2013). [10.1007/978-3-642-39053-1_15].

Software streams: Big data challenges in dynamic program analysis

Irene Finocchi
2013

Abstract

Dynamic program analysis encompasses the development of techniques and tools for analyzing computer software by exploiting information gathered from a program at runtime. The impressive amounts of data collected by dynamic analysis tools require efficient indexing and compression schemes, as well as on-line algorithmic techniques for mining relevant information on-the-fly in order to identify frequent events, hidden software patterns, or undesirable behaviors corresponding to bugs, malware, or intrusions. The paper explores how recent results in algorithmic theory for data-intensive scenarios can be applied to the design and implementation of dynamic program analysis tools, focusing on two important techniques: sampling and streaming.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11385/192665
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