Reduce the risk of migrating algorithms to a new software environment
New software versions are a reality so why not be ready for them
VersionBay will report out on compatibility, test coverage, performance and memory usage of a new software version
VersionBay will write regression tests and prove a safe migration
If you would like to minimize the risk in migrating algorithms to new software environments
Some examples of what we do
inv(a) pinv(a) rank(a) a\b [U,S,V] = svd(a) chol(a) [V,D] = eig(a)
linalg.inv(a) linalg.pinv(a) linalg.matrix_rank(a) linalg.solve(a,b) U, S, Vh = linalg.svd(a) linalg.cholesky(a).T D,V = linalg.eig(a)
Convert MATLAB algorithms to Python proving numerical equivalence
Migrate code to a newer version of MATLAB maintaining numerical accuracy
Notice the Live Editor Task. It also generates MATLAB code.
load('patients.mat') tbl = table(Diastolic,Smoker,Systolic); p = parallelplot(tbl)
This examples shows a new plotting function for tables that was released in R2019a: parallelplot.
Train your team and leverage latest features when migrating to newer software environments
Write regression tests to report on compatibility between software environments
Quantify the impact of execution time and memory usage of your algorithms
Write tests to achieve your coverage testing goal
Notice in the image the usage of a subsystem reference – controller block with the 2 triangles in the corners – that was released in R2019b.