BACKGROUND: Rule-based modeling (RBM) is a powerful and increasingly popular approach to modeling cell signaling networks. However, novel visual tools are needed in order to make RBM accessible to a broad range of users, to make specification of models less error prone, and to improve workflows. RESULTS: We introduce RuleBender, a novel visualization system for the integrated visualization, modeling and simulation of rule-based intracellular biochemistry. We present the user requirements, visual paradigms, algorithms and design decisions behind RuleBender, with emphasis on visual global/local model exploration and integrated execution of simulations. The support of RBM creation, debugging, and interactive visualization expedites the RBM learning process and reduces model construction time; while built-in model simulation and results with multiple linked views streamline the execution and analysis of newly created models and generated networks. CONCLUSION: RuleBender has been adopted as both an educational and a research tool and is available as a free open source tool at http://www.rulebender.org. A development cycle that includes close interaction with expert users allows RuleBender to better serve the needs of the systems biology community.

Abstract Summary: Rule-based modeling (RBM) is a powerful and increasingly popular approach to modeling intracellular biochemistry. Current interfaces for RBM are predominantly text-based and command-line driven. Better visual tools are needed to make RBM accessible to a broad range of users, to make specification of models less error prone and to improve workflows. We present RULEBENDER, an open-source visual interface that facilitates interactive debugging, simulation and analysis of RBMs. Availability: RULEBENDER is freely available for Mac, Windows and Linux at http://rulebender.org. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

This paper presents a tool that (i) constructs tree-based models of a program's behavior during testing and (ii) employs these trees while reordering and reducing a test suite. Using either a dynamic call tree or a calling context tree, the test reduction component identifies a subset of the original tests that covers the same call tree paths. The prioritization technique reorders a test suite so that it covers the call tree paths more rapidly than the initial test ordering. In support of program and test suite understanding, the tool also visualizes the call trees and the coverage relationships. For a chosen case study application, the experimental results show that call tree construction only increases testing time by 13%. In comparison to the original test suite, the experiments show that (i) a prioritized suite achieves coverage much faster and (ii) a reduced test suite contains 45% fewer tests and consumes 82% less time