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Defect detection at commit check-in time prevents the introduction of defectsinto software systems. Current defect detection approaches rely on metric-basedmodels which are not very accurate and whose results are not directly usefulfor developers. We propose a method to detect bug-inducing commits by comparingthe incoming changes with all past commits in the project, considering boththose that introduced defects and those that did not. Our method considersindividual changes in the commit separately, at the method-level granularity.Doing so helps developers as they are informed of specific methods that needfurther attention instead of being told that the entire commit is problematic.Our approach represents source code as abstract syntax trees and uses treekernels to estimate the similarity of the code with previous commits. Weexperiment with subtree kernels (STK), subset tree kernels (SSTK), or partialtree kernels (PTK). An incoming change is then classified using a K-NNclassifier on the past changes. We evaluate our approach on the BigCloneBenchbenchmark and on the Technical Debt dataset, using the NiCad clone detector asthe baseline. Our experiments with the BigCloneBench benchmark show that thetree kernel approach can detect clones with a comparable MAP to that of NiCad.Also, on defect detection with the Technical Debt dataset, tree kernels areleast as effective as NiCad with MRR, F-score, and Accuracy of 0.87, 0.80, and0.82 respectively.

An empirical evaluation of the usefulness of Tree Kernels for Commit-time Defect Detection in large software systems