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. - STAMPA. - 7921:(2013), pp. 124-134. (Intervento presentato al convegno 9th Conference on Computability in Europe tenutosi a Milan, Italy nel 1 July 2013 through 5 July 2013) [10.1007/978-3-642-39053-1_15].
Software streams: Big data challenges in dynamic program analysis
FINOCCHI, Irene
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.