8 entries tagged with #Libadalang
Originally, constraint solving in Libadalang was handled by several ad-hoc solvers built specifically for different parts of the type system. These implementations were tailored and somewhat fragile. A couple of years ago, a newer approach replaced these with a backend based on satisfiability solvers, AdaSAT. In this blog post, I will use AdaSAT to implement a Sudoku solver.
How we're using Libadalang to create an automated Ada code reducer.
Martyn’s recent blog post showed small programs based on Libadalang to find uses of access types in Ada sources. Albeit short, these programs need to take care of all the tedious logistics around processing Ada sources: find the files to work on, create a Libadalang analysis context, use it to read the source files, etc. Besides, they are not very convenient to run:
This is part #2 of the SPARKZumo series of blog posts. This post covers the build system that was used to build the SPARKZumo project and how to automate the process in GPS.
Libadalang has come a long way since the last time we blogged about it. In the past 6 months, we have been working tirelessly on name resolution, a pretty complicated topic in Ada, and it is finally ready enough that we feel ready to blog about it, and encourage people to try it out.
While we are working very hard on semantic analysis in Libadalang, it is already possible to leverage its lexical and syntactic analyzers. A useful example for this is a syntax highlighter.
We reported in a previous post our initial experiments to create lightweight checkers for Ada source code, based on the new Libadalang technology. The two checkers we described discovered 12 issues in the codebase of the tools we develop at AdaCore. In this post, we are reporting on 6 more lightweight checkers, which have discovered 114 new issues in our codebase. This is definitely showing that these kind of checkers are worth integrating in static analysis tools, and we look forward to integrating these and more in our static analyzer CodePeer for Ada programs.
After we created lightweight checkers based on the recent Libadalang technology developed at AdaCore, a colleague gave us the challenge of creating a copy-paste detector based on Libadalang. It turned out to be both easier than anticipated, and much more efficient and effective than we could have hoped for. In the end, we hope to use this new detector to refactor the codebase of some of our tools, and we expect to integrate it in our IDEs.