Program evolution: processes of software change
Program evolution: processes of software change
Exploiting hardware performance counters with flow and context sensitive profiling
Proceedings of the ACM SIGPLAN 1997 conference on Programming language design and implementation
Polymetric Views-A Lightweight Visual Approach to Reverse Engineering
IEEE Transactions on Software Engineering
High-Level Polymetric Views of Condensed Run-time Information
CSMR '04 Proceedings of the Eighth Euromicro Working Conference on Software Maintenance and Reengineering (CSMR'04)
Perfdiff: a framework for performance difference analysis in a virtual machine environment
Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization
CCCP: complete calling context profiling in virtual execution environments
Proceedings of the 2009 ACM SIGPLAN workshop on Partial evaluation and program manipulation
Tracking performance across software revisions
PPPJ '09 Proceedings of the 7th International Conference on Principles and Practice of Programming in Java
Exploring large profiles with calling context ring charts
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
Proceedings of the 5th international symposium on Software visualization
Counting messages as a proxy for average execution time in pharo
Proceedings of the 25th European conference on Object-oriented programming
Spy: A flexible code profiling framework
Computer Languages, Systems and Structures
Execution profiling blueprints
Software—Practice & Experience
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Understanding and minimizing the impact of software changes on performance are both challenging and essential when developing software. Unfortunately, current code execution profilers do not offer efficient abstractions and adequate representations to keep track of performance across multiple versions. Consequently, understanding the cause of a slow execution stemming from a software evolution is often realized in an ad hoc fashion. We have designed Rizel, a code profiler that identifies the root of performance variations thanks to an expressive and intuitive visual representation. Rizel highlights variation of executions and time distribution between multiple software versions. Rizel is available for the Pharo programming language under the MIT License.