Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
Evaluating Ordering Heuristics for Dynamic Partial-Order Reduction Techniques
verfasst von : Steven Lauterburg, Rajesh K. Karmani, Darko Marinov, Gul Agha
Erschienen in: Fundamental Approaches to Software Engineering
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Actor programs consist of a number of concurrent objects called actors, which communicate by exchanging messages. Nondeterminism in actors results from the different possible orders in which available messages are processed. Systematic testing of actor programs explores various feasible message processing schedules. Dynamic partial-order reduction (DPOR) techniques speed up systematic testing by pruning parts of the exploration space. Based on the exploration of a schedule, a DPOR algorithm may find that it need not explore some other schedules. However, the potential pruning that can be achieved using DPOR is highly dependent on the order in which messages are considered for processing. This paper evaluates a number of heuristics for choosing the order in which messages are explored for actor programs, and summarizes their advantages and disadvantages.