English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

An important challenge in dynamic adaptation of a software system is to prevent inconsistencies (failures) and disruptions in its operations during and after change. Several prior techniques have solved this problem with various tradeoffs. All of them, however, assume the availability of detailed component dependency models. This paper presents a complementary technique that solves this problem in settings where such models are either not available, difficult to build, or outdated due to the evolution of the software. Our approach first mines the execution history of a software system to infer a stochastic component dependency model, representing the probabilistic sequence of interactions among the system's components. We then demonstrate how this model could be used at runtime to infer the "best time" for adaptation of the system's components. We have thoroughly evaluated this research on a multi-user real world software system and under varying conditions.

Mining the execution history of a software system to infer the best time for its adaptation