To avoid an empirical replication crisis in programming languages research, PL researchers should employ the best scientific practices for empirical evaluation. A SIGPLAN empirical evaluation committee has assembled a checklist to help.
The wealth of code now available on-line is fertile ground to enable machine learning to be applied to programming tasks. This post discusses the promise of and some progress on the problem “deep code.” It is the first in a series.
Journals broaden the impact of PL. One way to make journals a more attractive publication vehicle is to allow presentations of journal papers at PL conferences, as TOPLAS does.
The purpose of a program analysis is to infer whether a certain property of a program execution can be observed at runtime. The notion of an analysis’ soundness defines how much confidence one should put in its results. The notion is not uniform and is determined by whether the analysis is intended to be used as a testing or as a verification tool.
