A new resummation scheme in scalar field theories

A new resummation scheme in scalar field theories is proposed by combining parquet resummation techniques and flow equations, which is characterized by a hierarchy structure of the Bethe--Salpeter (BS) equations. The new resummation scheme greatly improves on the approximations for the BS kernel. Resummation of the BS kernel in the $t$ and $u$ channels to infinite order is equivalent to truncate the effective action to infinite order. Our approximation approaches ensure that the theory can be renormalized, which is very important for numerical calculations. Two-point function can also be obtained from the four-point one through flow evolution equations resulting from the functional renormalization group. BS equations of different hierarchies and the flow evolution equation for the propagator constitute a closed self-consistent system, which can be solved completely.Comment: 12 pages, 4 figures. v2: title changed, one figure added, final version in Eur. Phys. J.

Revisiting Unsupervised Learning for Defect Prediction

Collecting quality data from software projects can be time-consuming and expensive. Hence, some researchers explore "unsupervised" approaches to quality prediction that does not require labelled data. An alternate technique is to use "supervised" approaches that learn models from project data labelled with, say, "defective" or "not-defective". Most researchers use these supervised models since, it is argued, they can exploit more knowledge of the projects. At FSE'16, Yang et al. reported startling results where unsupervised defect predictors outperformed supervised predictors for effort-aware just-in-time defect prediction. If confirmed, these results would lead to a dramatic simplification of a seemingly complex task (data mining) that is widely explored in the software engineering literature. This paper repeats and refutes those results as follows. (1) There is much variability in the efficacy of the Yang et al. predictors so even with their approach, some supervised data is required to prune weaker predictors away. (2)Their findings were grouped across $N$ projects. When we repeat their analysis on a project-by-project basis, supervised predictors are seen to work better. Even though this paper rejects the specific conclusions of Yang et al., we still endorse their general goal. In our our experiments, supervised predictors did not perform outstandingly better than unsupervised ones for effort-aware just-in-time defect prediction. Hence, they may indeed be some combination of unsupervised learners to achieve comparable performance to supervised ones. We therefore encourage others to work in this promising area.Comment: 11 pages, 5 figures. Accepted at FSE201