The SE community needs to be more careful about using off-the-shelf AI tools, without first applying SE knowledge (e.g., those past releases are a good source of knowledge for planning defect reductions). Once that SE knowledge is applied, this can result in dramatically better reasoning.

For example, here is a property of software that is not usually exploited in standard AI tools:

• Software comes in releases and
• Implausible change to software is something that has never been changed in prior releases.

Our TimeLIME exploits this knowledge as follows. 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 this point, 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 trials, 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).