TY - GEN
T1 - Discovering program's behavioral patterns by inferring graph-grammars from execution traces
AU - Zhao, Chunying
AU - Ates, Keven
AU - Kong, Jun
AU - Zhang, Kang
PY - 2008
Y1 - 2008
N2 - Frequent patterns in program executions represent recurring sequences of events. These patterns can be used to reveal the hidden structures of a program, and ease the comprehension of legacy systems. Existing grammar-induction approaches generally use sequential algorithms to infer formal models from program executions, in which program executions are represented as strings. Software developers, however, often use graphs to illustrate the process of program executions, such as UML diagrams, flowcharts and call graphs. Taking advantage of graphs' expressiveness and intuitiveness for human cognition, we present a graph-grammar induction approach to discovering program's behavioral patterns by analyzing execution traces represented in graphs. Moreover, to improve the efficiency, execution traces are abstracted to filter redundant or unrelated traces. A grammar induction environment called VEGGIE is adopted to facilitate the induction. Evaluation is conducted on an open source project JHotDraw. Experimental results show the applicability of the proposed approach.
AB - Frequent patterns in program executions represent recurring sequences of events. These patterns can be used to reveal the hidden structures of a program, and ease the comprehension of legacy systems. Existing grammar-induction approaches generally use sequential algorithms to infer formal models from program executions, in which program executions are represented as strings. Software developers, however, often use graphs to illustrate the process of program executions, such as UML diagrams, flowcharts and call graphs. Taking advantage of graphs' expressiveness and intuitiveness for human cognition, we present a graph-grammar induction approach to discovering program's behavioral patterns by analyzing execution traces represented in graphs. Moreover, to improve the efficiency, execution traces are abstracted to filter redundant or unrelated traces. A grammar induction environment called VEGGIE is adopted to facilitate the induction. Evaluation is conducted on an open source project JHotDraw. Experimental results show the applicability of the proposed approach.
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000262215500054
UR - https://www.scopus.com/pages/publications/57649187249
U2 - 10.1109/ICTAI.2008.68
DO - 10.1109/ICTAI.2008.68
M3 - Conference Paper published in a book
SN - 9780769534404
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 395
EP - 402
BT - Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
T2 - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Y2 - 3 November 2008 through 5 November 2008
ER -