Telling the truth about all program behaviors collectively is hard. Can an analysis say something useful and true without making assumptions that are violated by nearly all real programs?
The usage of the term program verification has expanded well beyond its original meaning. As research in this space advances and expands, is it time to reconsider the term?
Although the computer science community successfully harnessed exponential increases in computer performance to drive societal and economic change, the exponential growth in publications is proving harder to accommodate. To gain a deeper understanding of publication growth and inform how the computer science community should handle this growth, we analyze publication practices from several perspectives.
A Checklist Manifesto for Empirical Evaluation: A Preemptive Strike Against a Replication Crisis in Computer Science
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.
Program synthesis addresses an age-old problem in computer science: can a computer program itself? This post surveys the growing evolution of work in this exciting area.
Quantum computing may be more powerful than classical computing but has a radically different programming model. Current languages are in their infancy; future languages are likely to be different. Now is a great time for language designers and implementers to try new ideas.
How should ACM address its contribution to climate change? After two years of discussions and study, the SIGPLAN Climate Committee proposes that (1) all ACM conferences should publicly *account* for the CO2e emitted as a result of putting them on; and that (2) ACM should put a *price* on carbon in conference budgets, to create incentive for organizers to reduce their footprints.