A model to predict anti-regressive effort in Open Source Software

Abstract

Accumulated changes on a software system are not uniformly distributed: some elements are changed more often than others. For optimal impact, the limited time and effort for complexity control, called anti-regressive work, should be applied to the elements of the system which are frequently changed and are complex. Based on this, we propose a maintenance guidance model (MGM) which is tested against real-world data. MGM takes into account several dimensions of complexity: size, structural complexity and coupling. Results show that maintainers of the eight open source systems studied tend, in general, to prioritize their anti-regressive work in line with the predictions given by our MGM, even though, divergences also exist. MGM offers a history-based alternative to existing approaches to the identification of elements for anti-regressive work, most of which use static code characteristics only.