Runtime monitoring is a technique usable in all phases of the software development cycle, from initial testing, to debugging, to actually maintaining proper function in production code. Of particular importance are parametric monitoring systems, which allow the specification of properties that relate objects in a program, rather than only global properties. In the past decade, a number of parametric runtime monitoring systems have been developed. Here we give a demonstration of our system, JavaMOP. It is the only parametric monitoring system that allows multiple differing logical formalisms. It is also the most efficient in terms of runtime overhead, and very competitive with respect to memory usage.

Specifications carrying formal parameters that are bound to concrete data at runtime can effectively and elegantly capture multi-object behaviors or protocols. Unfortunately, parametric specifications are not easy to formulate by nonexperts and, consequently, are rarely available. This paper presents a general approach for mining parametric specifications from program executions, based on a strict separation of concerns: (1) a trace slicer first extracts sets of independent interactions from parametric execution traces; and (2) the resulting non-parametric trace slices are then passed to any conventional non-parametric property learner. The presented technique has been implemented in jMiner, which has been used to automatically mine many meaningful and non-trivial parametric properties of OpenJDK 6.

Lack of standard metadata and file formats make it challenging to archive and distribute remote sensing data. Although the Common Data Model (CDM) developed by Unidata has the potential of providing easy access to data stored in a variety of file formats, it would still be necessary to maintain each I/O library ever used to create a data product. Moreover, due to the nature of these data structures, I/O libraries are often complicated and expensive to maintain. As a consequence, the long-term archival of data stored in multiple formats becomes an expensive and challenging task. In this paper, we demonstrate an alternative solution, namely migration of data to a single standards-based archive format. Data from NASA's Earth Observing System (EOS) Data Centers formatted in Hierarchical Data Format (HDF) are used in the demonstration.