Interconnectivity analysis techniques for error localization in large systems

Abstract Software systems contain multiple types of interrelations among components — data, control, and sequencing, among others. We are developing interconnectivity analysis techniques that derive multiple views of the structure of large-scale software systems. These techniques calculate interconnections among components and then recursively group the components into sets according to their degree of interconnection. These techniques are especially suited to large-scale systems (e.g., > 100,000 lines) since numerous types of interconnections can be determined automatically in a tractable manner. Interconnectivity analysis techniques produce visualizations of system structure and can be used to document systems, model their evolution over time, compare system structure, guide regression testing, or localize error-prone structure. This article summarizes two studies using interconnectivity analysis. In earlier work, one such technique was applied effectively in a feasibility study to characterize the error-prone components in a large-scale system from a production environment. Routines with the highest coupling/cohesion ratios had 8.1 times more errors per 1,000 source lines of code that did routines with the lowest coupling /cohesion ratios. A second validation study is currently underway. In this study, we are applying interconnectivity analysis techniques to the design specification of a large distributed command and control system. Tools supporting interconnectivity analysis will be integrated into the Amadeus measurement-driven analysis and feedback system.