Responsible and Effective Bugfinding

NB: This piece is not about responsible disclosure of security issues. For almost as long as people have written code, we have also worked to create methods for finding software defects. Much more recently, it has become common to treat “external bug finding” — looking for defects in other people’s software — as an activity […]

Alive2 Part 3: Things You Can and Can’t Do with Undef in LLVM

[Also see Part 1 and Part 2 in this series.] Let’s talk about these functions: unsigned add(unsigned x) { return x + x; } unsigned shift(unsigned x) { return x << 1; } From the point of view of the C and C++ abstract machines, their behavior is equivalent: in a program you’re writing, you […]

You Might as Well Be a Great Copy Editor

An early draft of a paper, blog post, grant proposal, or other piece of technical writing typically has many problems. Some of these are high-level issues, such as weak motivation, sections in the wrong order, or a key description that is difficult to understand because it lacks an accompanying figure. These problems need to be […]

Alive2 Part 2: Tracking miscompilations in LLVM using its own unit tests

[This piece is co-authored by Nuno P. Lopes and John Regehr.] Alive2 is a formal verification framework for LLVM optimizations. It includes multiple tools, including a plugin for `opt’ to verify whether the optimizations just run are correct or not. We gave an introduction to Alive2 in a previous post. A few years ago we […]

Alive2 Part 1: Introduction

[This piece is co-authored by Nuno P. Lopes and John Regehr.] Compiler bugs threaten the correctness of almost any computer system that uses compiled code. Translation validation is a path towards reliably correct compilation that works by checking that an individual execution of the compiler did the right thing. We created a tool, Alive2, that […]

Precision Opportunities for Demanded Bits in LLVM

[Although this post was written to stand by itself, it builds on the previous one. It is authored by Jubi Taneja, Zhengyang Liu, and John Regehr.] When designing computer systems, it can be useful to avoid specifying behaviors too tightly. For example, we might specify that a math library function only needs to return a […]

Testing Dataflow Analyses for Precision and Soundness

[This piece is co-authored by Jubi Taneja, Zhengyang Liu, and John Regehr; it’s a summary of some of the findings from a paper that we just recently completed the camera ready copy for, that is going to be published at CGO (Code Generation and Optimization) 2020.] Update from Jan 12 2020: Looks like there’s a […]