In event-driven systems, separating the reactive part of software (i.e., event-driven control) from the non-reactive part is a common design practice. The reactive part is typically structured according to the states and transitions of a system, whereas the non-reactive part is typically structured according to the concepts of the application domain (e.g., the services provided by the system). In such systems, the non-reactive part of software stimulates the reactive part with event calls . Whenever the non-reactive part is modified (e.g., during evolution), the existing event calls may become invalid, new event calls may become necessary, and the two parts of software may become incompatible . Manually finding and repairing these types of defects is a time-consuming and error-prone maintenance task. In this article, we present a solution that combines source code model checking and aspect-oriented programming techniques, to reduce the time spent by developers and to automatically find defects, while performing the maintenance task mentioned above. In addition, we present controlled experiments showing that the solution can reduce the time by 75%, and enable the prevention of one defect per 140 lines of source code.

MOTIVATION: As the scientific curiosity in genome studies shifts toward identification of functions of the genomes in large scale, data produced about cellular processes at molecular level has been accumulating with an accelerating rate. In this regard, it is essential to be able to store, integrate, access and analyze this data effectively with the help of software tools. Clearly this requires a strong ontology that is intuitive, comprehensive and uncomplicated. RESULTS: We define an ontology for an intuitive, comprehensive and uncomplicated representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information via collaboration, and supports manipulation of the stored data. In addition, it facilitates concurrent modifications to the data while maintaining its validity and consistency. Furthermore, novel structures for representation of multiple levels of abstraction for pathways and homologies is provided. Lastly, our ontology supports efficient querying of large amounts of data. We have also developed a software tool named pathway analysis tool for integration and knowledge acquisition (PATIKA) providing an integrated, multi-user environment for visualizing and manipulating network of cellular events. PATIKA implements the basics of our ontology.