How to Improve AI Tools (by Adding in SE Knowledge): Experiments with the TimeLIME Defect Reduction Tool
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AI algorithms are being used with increased frequency in SE research and practice. Such algorithms are usually commissioned and certified using data from outside the SE domain. Can we assume that such algorithms can be used ”off-the-shelf” (i.e. with no modifications)? To say that another way, are there special features of SE problems that suggest a different and better way to use AI tools? To answer these questions, this paper reports experiments with TimeLIME, a variant of the LIME explanation algorithm from KDD'16. LIME can offer recommendations on how to change static code attributes in order to reduce the number of defects in the next software release. That version of LIME used an internal weighting tool to decide what attributes to include/exclude in those recommendations. TimeLIME improves on that weighting scheme using the following SE knowledge: software comes in releases; an implausible change to software is something that has never been changed in prior releases; so it is better to use plausible changes, i.e. changes with some precedent in the prior releases. By restricting recommendations to just the frequently changed attributes, TimeLIME can produce (a) dramatically better explanations of what causes defects and (b) much better recommendations on how to fix buggy code. Apart from these specific results about defect reduction 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. As shown here, it may not be a complex matter to apply that knowledge. Further, once that SE knowledge is applied, this can result in dramatically better systems.
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