  Software comes in releases. An implausible change to software is something
that has never been changed in prior releases. When planning how to reduce
defects, it is better to use plausible changes, i.e., changes with some
precedence in the prior releases.
  To demonstrate these points, this paper compares several defect reduction
planning tools. LIME is a local sensitivity analysis tool that can report the
fewest changes needed to alter the classification of some code module (e.g.,
from "defective" to "non-defective"). TimeLIME is a new tool, introduced in
this paper, that improves LIME by restricting its plans to just those
attributes which change the most within a project.
  In this study, we compared the performance of LIME and TimeLIME and several
other defect reduction planning algorithms. The generated plans were assessed
via (a) the similarity scores between the proposed code changes and the real
code changes made by developers; and (b) the improvement scores seen within
projects that followed the plans. For nine project trails, we found that
TimeLIME outperformed all other algorithms (in 8 out of 9 trials). Hence, we
strongly recommend using past releases as a source of knowledge for computing
fixes for new releases (using TimeLIME).
  Apart from these specific results about planning defect reductions and
TimeLIME, the more general point of this paper is that our community should be
more careful about using off-the-shelf AI tools, without first applying SE
knowledge. In this case study, it was not difficult to augment a standard AI
algorithm with SE knowledge (that past releases are a good source of knowledge
for planning defect reductions). As shown here, once that SE knowledge is
applied, this can result in dramatically better systems.
