Little-JIL/Juliette: a process definition language and interpreter

  • Authors:

  • Aaron G. Cass;Barbara Staudt Lerner;Stanley M. Sutton, Jr.;Eric K. McCall;Alexander Wise;Leon J. Osterweil

  • Affiliations:

  • Laboratory for Advanced Software Engineering Research, Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, Williams College, Williamstown, MA and Laboratory for Advanced Software Engineering Research, Department of Computer Science, University of Massachusetts, Amherst, ...;IBM TJ Watson Research Center, Hawthorne, NY and Laboratory for Advanced Software Engineering Research, Department of Computer Science, University of Massachusetts, Amherst, MA;HP Laboratories, Palo Alto, CA and Laboratory for Advanced Software Engineering Research, Department of Computer Science, University of Massachusetts, Amherst, MA;Laboratory for Advanced Software Engineering Research, Department of Computer Science, University of Massachusetts, Amherst, MA;Laboratory for Advanced Software Engineering Research, Department of Computer Science, University of Massachusetts, Amherst, MA

  • Venue:
  • Proceedings of the 22nd international conference on Software engineering
  • Year:
  • 2000

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Abstract

Little-JIL, a language for programming coordination in processes is an executable, high-level language with a formal (yet graphical) syntax and rigorously defined operational semantics. The central abstraction in Little-JIL is the “step,” which is the focal point for coordination, providing a scoping mechanism for control, data, and exception flow and for agent and resource assignment. Steps are organized into a static hierarchy, but can have a highly dynamic execution structure including the possibility of recursion and concurrency.Little-JIL is based on two main hypotheses. The first is that coordination structure is separable from other process language issues. Little-JIL provides rich control structures while relying on separate systems for resource, artifact, and agenda management. The second hypothesis is that processes are executed by agents that know how to perform their tasks but benefit from coordination support. Accordingly, each Little-JIL step has an execution agent (human or automated) that is responsible for performing the work of the step.This approach has proven effective in supporting the clear and concise expression of agent coordination for a wide variety of software, workflow, and other processes.