A framework for the discovery of predictive fix-time models

Fix-time prediction is a key task in bug tracking systems, which has been recently faced through the definition of inductive learning methods, trained to estimate the time needed to solve a case at the moment when it is reported. And yet, the actions performed on a bug along its life can help refine the prediction of its (remaining) fix time, possibly with the help of Process Mining techniques. However, typical bug-tracking systems lack any task-oriented description of the resolution process, and store fine-grain records, just capturing bug attributes' updates. Moreover, no general approach has been proposed to support the definition of derived data, which can help improve considerably fix-time predictions. A new methodological framework for the analysis of bug repositories is presented here, along with an associated toolkit, leveraging two kinds of tools: (i) a combination of modular and flexible data-transformation mechanisms, for producing an enhanced process-oriented view of log data, and (ii) a series of ad-hoc induction techniques, for extracting a prediction model out of such a view. Preliminary results on the bug repository of a real project confirm the validity of our proposal and, in particular, of our log transformation methods.