TY - GEN
T1 - Complex networks and link streams for the empirical analysis of large software
AU - Latapy, Matthieu
AU - Viard, Tiphaine
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Large software may be modeled as graphs in several ways. For instance, nodes may represent modules, objects or functions, and links may encode dependencies between them, calls, heritage, etc. One may then study a large software through such graphs, called complex networks because they have no strong mathematical properties. Studying them sheds much light on the structure of the considered software. If one turns to the analysis of the dynamics of large software, like execution traces, then the considered graphs evolve over time. This raises challenging issues, as there is currently no clear way to study such objects. We develop a new approach consisting in modeling traces as link streams, i.e. series of triplets (t,a,b) meaning that a and b interacted at time t. For instance, such a triplet may model a call between two modules at run time. Analyzing such streams directly turns out to be much easier and powerful than transforming them into dynamic graphs that poorly capture their dynamics. We present our work on this topic, with directions for applications in software analysis.
AB - Large software may be modeled as graphs in several ways. For instance, nodes may represent modules, objects or functions, and links may encode dependencies between them, calls, heritage, etc. One may then study a large software through such graphs, called complex networks because they have no strong mathematical properties. Studying them sheds much light on the structure of the considered software. If one turns to the analysis of the dynamics of large software, like execution traces, then the considered graphs evolve over time. This raises challenging issues, as there is currently no clear way to study such objects. We develop a new approach consisting in modeling traces as link streams, i.e. series of triplets (t,a,b) meaning that a and b interacted at time t. For instance, such a triplet may model a call between two modules at run time. Analyzing such streams directly turns out to be much easier and powerful than transforming them into dynamic graphs that poorly capture their dynamics. We present our work on this topic, with directions for applications in software analysis.
KW - complex networks
KW - dynamic graphs
KW - link streams
KW - software traces
UR - https://www.scopus.com/pages/publications/84904099329
U2 - 10.1007/978-3-319-07734-5_3
DO - 10.1007/978-3-319-07734-5_3
M3 - Conference contribution
AN - SCOPUS:84904099329
SN - 9783319077338
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 40
EP - 50
BT - Application and Theory of Petri Nets and Concurrency - 35th International Conference, PETRI NETS 2014, Proceedings
PB - Springer Verlag
T2 - 35th International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2014
Y2 - 23 June 2014 through 27 June 2014
ER -