Software systems are often released with missing functionality, errors, or incompatibilities that may result in failures in the field, inferior performances, or, more generally, user dissatisfaction. In previous work, some of the authors presented the gamma approach, whose goal is to improve software quality by augmenting software-engineering tasks with dynamic information collected from deployed software. The gamma approach enables analyses that (1) rely on actual field data instead of synthetic in-house data and (2) leverage the vast and heterogeneous resources of an entire user community instead of limited, and often homogeneous, in-house resources. When monitoring a large number of deployed instances of a software product, however, a significant amount of data is collected. Such raw data are useless in the absence of suitable datamining and visualization techniques that support exploration and understanding of the data. In this paper, we present a new technique for collecting, storing, and visualizing program-execution data gathered from deployed instances of a software product. We also present a prototype toolset, Gammatella, that implements the technique. Finally, we show how the visualization capabilities of Gammatella facilitate effective investigation of several kinds of execution-related information in an interactive fashion, and discuss our initial experience with a semi-public display of Gammatella.
