Software builds are arguably the most critical steps in the development cycles of the vast majority of the
software products, at least because technically they output the artifacts eventually qualified as the products
Building software is a significant portion of the daily grind for many software developers, QA communities needs the built artifacts to do their jobs, DevOps personnel cross their fingers for the automated CI builds to complete successfully, Release teams hold their breath during production builds hoping they'll meet the deployment planned date.
The software build performance remains a hot topic driving many products and technologies, showing up on many software development costs-related slides and backing many IT departments budget requests.
BuildIn is a cloud-based application designed to aid in high-level performance analysis of complex/massive
software builds. It provides a visual correlation between a build's long-lasting process tree and various
build server's operating parameters.
These are just a few potential uses for such high-level performance analysis:
- lower build computing resources costs by making better use of existing resources, better ROI evaluation for new resource expenses, etc.
- develop "low hanging fruit" build performance improvement strategies and measure their effectiveness
- pinpoint build areas warranting further more detailed performance analysis using tools impractical/impossible to use at the entire build level
And here are some examples of questions that BuildIn can provide answers for:
- is the build fully utilizing a server's resources?
- is the build overloading the server?
- does the build include serialized/inefficient/suspect sequences warranting more detailed analysis?
- how are overlapping builds on the same server affecting each-other?
- how much better/worse is a particular server configuration compared to another for particular classes of builds?
- what is the actual/measured effect of a particular build performance improvement initiative?
BuildIn is implemented using the Google App Engine PaaS
infrastructure for its performance, scalability and security features.