Abstract
In large software systems, it is common practice to adopt third-party libraries. Decisions by system maintainers to either update or introduce new third-party libraries can range from trivial to complex. For instance, incompatibility between internal library dependencies may complicate adoption. Therefore, system maintainers especially need adequate assurance of any candidate library release. Using the 'wisdom of the crowd', VerXCombo aims to assist system maintainers by mining popular library dependency patterns of similar systems. Through data interactions, VerXCombo leverages parallel sets to break-down large and complex dataset into distinguishable patterns of 1.) popular and 2.) latest library dependency release combinations. Populating our tool with a maven library dependency dataset from over 4,000 Java Open Source projects, we demonstrate through a case scenario navigation and best fit combinations of the VerXCombo tool. A video highlighting the main features of the tool can be found at: http://goo.gl/wWPylL.
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