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.