Visualizing bug-prone code via version control metadata

Abstract: Software being developed today can have years worth of history and hundreds if notthousands of files involved in a single project. When trying to determine what parts ofthe code need maintenance or updating it can be difficult to determine what will beproblematic in the future. Hours spent on code that will not cause problems in thefuture could be better used in other areas. Before changes are made to a codebase, themost error-prone parts of the code should be identified. In this thesis a method forcomparing what factors contribute to future bugs is developed, as well as testing severalfactors extracted from version control metadata using this method. In addition, avisualization was made using tree maps to show the most problematic files in a readablemanner, effectively using the produced data in an application to predict future bugs. Itwas determined that Age of newest change, Changes with age reducing importance andPrevious bugfixes with age reducing importance were all the most impactful factors forpredicting future bugs but that different repositories worked best with differentcombinations of the mentioned factors.