Program input grammars (i.e., grammars encoding the language of valid program inputs) facilitate a wide range of applications in software engineering such as symbolic execution and delta debugging. Grammars synthesized by existing approaches can cover only a small part of the valid input space mainly due to unanalyzable code (e.g., native code) in programs and lacking high-quality and high-variety seed inputs. To address these challenges, we present REINAM, a reinforcement-learning approach for synthesizing probabilistic context-free program input grammars without any seed inputs. REINAM uses an industrial symbolic execution engine to generate an initial set of inputs for the given target program, and then uses an iterative process of grammar generalization to proactively generate additional inputs to infer grammars generalized from these initial seed inputs. To efficiently search for target generalizations in a huge search space of candidate generalization operators, REINAM includes a novel formulation of the search problem as a reinforcement learning problem. Our evaluation on 11 real-world benchmarks shows that REINAM outperforms an existing state-of-the-art approach on precision and recall of synthesized grammars, and fuzz testing based on REINAM substantially increases the coverage of the space of valid inputs. REINAM is able to synthesize a grammar covering the entire valid input space for some benchmarks without decreasing the accuracy of the grammar.
Background The symbiotic interactions that occur between humans and organisms in our environment have a tremendous impact on our health. Recently, there has been a surge in interest in understanding the complex relationships between the microbiome and human health and host immunity against microbial pathogens, among other things. To collect and manage data about these interactions and their complexity, scientists will need ontologies that represent symbiotic interactions as they occur in reality. Methods We began with two papers that reviewed the usage of ‘symbiosis’ and related terms in the biology and ecology literature and prominent textbooks. We then analyzed several prominent standard terminologies and ontologies that contain representations of symbiotic interactions, to determine if they appropriately defined ‘symbiosis’ and related terms according to current scientific usage as identified by the review papers. In the process, we identified several subtypes of symbiotic interactions, as well as the characteristics that differentiate them, which we used to propose textual and axiomatic definitions for each subtype of interaction. To both illustrate how to use the ontological representations and definitions we created and provide additional quality assurance on key definitions, we carried out a referent tracking analysis and representation of three scenarios involving symbiotic interactions among organisms. Results We found one definition of ‘symbiosis’ in an existing ontology that was consistent with the vast preponderance of scientific usage in biology and ecology. However, that ontology changed its definition during the course of our work, and discussions are ongoing. We present a new definition that we have proposed. We also define 34 subtypes of symbiosis. Our referent tracking analysis showed that it is necessary to define symbiotic interactions at the level of the individual, rather than at the species level, due to the complex nature in which organisms can go from participating in one type of symbiosis with one organism to participating in another type of symbiosis with a different organism. Conclusion As a result of our efforts here, we have developed a robust representation of symbiotic interactions using a realism-based approach, which fills a gap in existing biomedical ontologies.
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