Coverity has released its Coverity Software Readiness Manager for Java, a tool that lets development and release managers assess the release readiness of critical code by combining essential data from multiple sources including Prevent, Coverity's static-analysis tool. Software Readiness Manager helps development teams deliver high-integrity code that successfully meets quality standards to align product development with business goals.
Coverity Software Readiness Manager analyzes data from multiple sources and tools, so managers can proactively determine the risk of post-release failure. In addition, Software Readiness Manager helps identify, prioritize and direct the repair of code branches that pose the greatest risk of failure. It provides detailed intelligence about key readiness factors such as code complexity, violation of best practices, architectural integrity, interdependencies, and test coverage. Software Readiness Manager provides high-level dashboards as well as deep, drill-down capabilities that development managers can use to objectively assess the quality and maintainability of their software prior to release. The tool can also provide immediate visibility into code of unknown quality that may be outsourced, open sourced, acquired or otherwise reused.
"When it comes to delivering superior software, development organizations are faced with a number of challenges -- code complexity, pace of change, and constrained resources to name just a few," said Ben Chelf, chief technology officer at Coverity. "Software Readiness Manager provides development managers working in today's fast-paced development environments with actionable data they need to help their teams deliver software that is fully prepared to perform in the field before it is released."
Key features of Coverity Software Readiness Manager include:
- Automatic identification of failure-prone (high-risk) code across large and complex software systems
- Translation of large amounts of data from multiple tools into actionable, prioritized recommendations for improving code
- Correlation of test coverage data with high-risk code to determine if failure-prone areas are being sufficiently tested
- Elimination of issues due to poor coding practices earlier in the software development lifecycle, before they negatively impact development
- Creation of quality and risk benchmarks to identify code appropriate for reuse
