ContiPerf is a lightweight testing utility to easily leverage JUnit 4 test cases as performance tests, e.g. for continuous performance testing. It is inspired by JUnit 4's easy test configuration with annotations, and by JUnitPerf's idea of wrapping Unit tests for performance testing, but is more powerful and easier to use. It uses Java annotations for defining test execution characteristics and performance requirements. You can mark a test to run a certain number of times, or to be repeatedly executed for a certain amount of time. Performance requirements can be maximum, average, medium, or any percentile execution time. You can run tests in two different modes, using them as simple unit tests or performance tests. Easy integration with Eclipse and Maven. Export of an execution summary to a CSV file. A small library without external dependencies (only JUnit).
BitNami Spree Stack greatly simplifies the deployment of Spree and its required dependencies. It can be deployed using a native installer, as a virtual machine, in the cloud, or as a module over an already installed infrastructure stack. Spree is a very powerful and flexible e-commerce platform written for the Ruby on Rails framework.
Embedded Profiler is low-overhead C++ profiler based on automatic instrumentation of functions done by the compiler (GCC, MinGW, or MSVC). Profiling can be done either automatically or manually. Automatic profiling generates a complete call tree and needs no modification of code. Manual profiling requires using the EProfiler API to specify the parts of code to be profiled. The resulting log can be opened in Performance Analyzer, a GUI application with several views designed for comfortable log analysis.
libLunchbox facilitates the development and deployment of multi-threaded applications. It provides OS Abstraction, using utility classes abstracting common operating system features (such as threads, locks, memory maps, shared library loading, and condition variables), high-performance primitives (including thread-safe utilities tuned for performance, such as atomic variables, spin locks, and lock-free containers), and utility classes (including helper primitives which are not in the standard library, such as logging, pools, and random number generation).
Django-live-profiler is a low-overhead data access and code profiler for Django-based applications. Profiling Web applications on a development environment often produces misleading results due to different patterns in the data, different patterns in user behavior, and differences in infrastructure. All existing DB access profiling solutions for Django seem to focus on a single request, but in the real world certain queries might be negligible in a single request while still putting a considerable strain the database across all requests. Django-live-profiler aims to solve these issues by collecting database usage data from a live application.