Material Detail

Classification of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach

Classification of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach

This video was recorded at 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Paris 2009. Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the difficulty and complexity of software systems, bugs and anomalies are prevalent. Bugs have caused billions of dollars loss, in addition to privacy and security threats. In this work, we address software reliability issues by proposing a novel method to classify software behaviors based on past history or runs. With the technique, it is possible to generalize past known errors and mistakes to capture failures and anomalies. Our technique first mines a set of discriminative features capturing repetitive series of events from program execution... Show More

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Browse...

Disciplines with similar materials as Classification of Software Behaviors for Failure Detection: A Discriminative Pattern Mining Approach

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden