A Plot Twist, Part II: In Praise of Scatter Plots
Drawing on numerous examples from papers published in PLDI 2019, I argue that scatter plots can be easier to understand than bar charts.
A Plot Twist: Drawing Better Graphs in PL Papers
Drawing on numerous examples from papers published in PLDI 2019, I argue that normalised data should usually be plotted on a logarithmic scale.
Understanding the World with Program Synthesis
Most applications of program synthesis are concerned with the engineering of software. However, because programming languages can be used to model the physical world, program synthesis can also offer a way of discovering and validating new hypotheses in the natural sciences. In this post, I elaborate on how.
How to Have Real-World Impact: Five Easy Pieces
Five easy pieces of advice to getting your work to have real-world impact: not guaranteed to get your work adopted, but will definitely increase the odds of luck being on your side.
What Type Soundness Theorem Do You Really Want to Prove?
For decades, researchers have syntactically proved type soundness to establish their type system works. This post shows why a semantic notion of type soundness is more appropriate, for covering abstraction and unsafe features.
How to Design Co-Programs
We accept that data structure determines program structure. But we should not forget that it is not just the input data that may be structured: output data may be structured too, and both may determine program structure.
Machine Learning and Approximate Computing
There’s a new ecosystem of deep-learning-driven applications, occasionally titled Software 2.0, that integrates neural networks into a variety of computational tasks. Such applications include image recognition, natural language processing, and other traditional...
Highlighting PL Research to the CS Community
This post showcases three papers published in SIGPLAN venues which will appear as Research Highlights in upcoming issues of the Communications of the ACM.
Sound Analysis: Can We Tell the Truth About Programs?
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?
“Program Verification”: Has it lost its punch?
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?
