jmxtrans is effectively the missing connector between JMX and whatever logging or graphing package that you can dream up. jmxtrans is very powerful tool that reads JSON configuration files specifying servers/ports and JMX domains/attributes and then outputs the data in whatever format you want via special "Writer" objects that you can code up yourself. It does this with a very efficient engine design that will scale to querying literally thousands of machines. The core engine is pretty solid and writers are included for cacti/rrdtool, graphite, and stdout.
SDMetrics Core is a Java library to calculate metrics of structural design properties such as coupling, size, and complexity for UML designs. It also checks design rules to automatically detect incomplete or incorrect design, and adherence to style guidelines such as circular dependencies or naming conventions. The library provides an XMI parser for import of XMI 1.x files with UML1.3/1.4 models, and XMI 2.0/2.1 files with UML 2.x models. Design metrics, rules, and XMI import are highly customizable.
allmon is a generic system for collecting and storing various runtime metrics collections used for system performance, health, quality, and availability monitoring purposes. The system also provides a set of data-mining algorithms useful for further performance analysis. Allmon is designed to harvest different metrics values coming from many areas of monitoring infrastructure. The collected data are based on quantitative and qualitative performance and availability analysis. Allmon collaborates with other analytical tools for OLAP multidimensional analysis and data mining processing. The tool can be used for production as well as for development (profiling) and QA (load testing) purposes.
Metrix++ is a platform to collect and analyze code metrics. It has a plugin-based architecture, so it is easy to add support for new languages, define new metrics, and/or create new pre- and post-processing tools. Every metric has 'turn-on' and other configuration options. There are no predefined thresholds for metrics or rules; you can choose and configure any limit you want. It scales well to large codebases. For example, initial parsing of about 10000 files takes 2-3 minutes on an average PC, and only 10-20 seconds for iterative re-run. Reporting summary results and exceeded limits takes less than 1 - 10 seconds. It can compare results for 2 code snapshots (collections) and differentiate added regions (classes, functions, etc.), modified regions, and unchanged regions. As a result, easy deployment is guaranteed into legacy software, helping you to deal with legacy code efficiently, and either enforce the 'leave it not worse than it was before' rule or motivate re-factoring.
Squale (Software QUALity Enhancement) provides models and associated tools to assess software quality and help improve it over time. Its models and tools know how to aggregate raw quality information (such as metrics) given by third party technologies into high-level factors, offer dashboards which present those factors and allow digging deeply into the code quality, show the evolution of quality over time, and give economical indicators about the return on investment of quality efforts.