Long-lived software products commonly have a large number of reported defects, some of which may not be fixed for a lengthy period of time, if ever. These so-called languishing bugs can incur various costs to project teams, such as wasted time in release planning and in defect analysis and inspection. They also result in an unrealistic view of the number of bugs still to be fixed at a given time. The goal of this work is to help software practitioners mitigate their costs from languishing bugs by providing a technique to predict and pre-emptively close them. We analyze defect fix times from an ABB program and the Apache HTTP server, and find that both contain a substantial number of languishing bugs. We also train decision tree classification models to predict whether a given bug will be fixed within a desired time period. We propose that an organization could use such a model to form a "grim reaper" policy, whereby bugs that are predicted to become languishing will be pre-emptively closed. However, initial results are mixed, with models for the ABB program achieving F-scores of 63-95%, while the Apache program has F-scores of 21-59%.
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