Rust is an emerging programming language that has gained popularity in many academic and industrial projects due to its memory safety features. However, despite its safety advantages, there are still many serious bugs in existing Rust projects. The lack of suitable fuzz targets has been a major challenge in Rust fuzz testing, as existing fuzzing tools often rely on manual efforts to write test targets, which is time-consuming and cannot be directly applied to library function interfaces. While the previous work has made significant progress in generating fuzz targets for Rust, it still cannot effectively test special functions with generics and polymorphism.To address this issue, this paper proposes an automated approach for generating fuzz targets for Rust library functions, called RVFuzz. RVFuzz addresses the challenges by performing static analysis through variable instantiation, ensuring that special functions in Rust, such as those with generics and polymorphism, are covered in the interface sequence analysis. It then traverses the library function dependency graph to generate a set of reasonable and non-redundant target function sequences for fuzz testing. Any compilation errors encountered during the process are used to refine the sequence generation. RVFuzz successfully generated fuzz targets for 10 popular Rust projects, achieving an impressive interface coverage rate of 92.3%.

MOTIVATION: Understanding gene regulation and expression is the key to the advancement of biology. EST sequence assembly and analysis provide unique benefits in this regard. We have developed a standalone application, dCAS (Desktop cDNA Annotation System), which performs automated EST cleaning, clustering, assembly and annotation on a desktop computer. Compared with other available tools, dCAS provides a more convenient and user-friendly solution to biologists for extracting biological meaning from sequence data. AVAILABILITY: The dCAS package is distributed freely. A cross-platform installer and associated sequence databases can be downloaded at: http://exon.niaid.nih.gov/applications.html.

BACKGROUND: Due to the complex and distributed nature of biological research, our current biological knowledge is spread over many redundant annotation databases maintained by many independent groups. Analysts usually need to visit many of these bioinformatics databases in order to integrate comprehensive annotation information for their genes, which becomes one of the bottlenecks, particularly for the analytic task associated with a large gene list. Thus, a highly centralized and ready-to-use gene-annotation knowledgebase is in demand for high throughput gene functional analysis. DESCRIPTION: The DAVID Knowledgebase is built around the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of gene/protein identifiers from a variety of public genomic resources into DAVID gene clusters. The grouping of such identifiers improves the cross-reference capability, particularly across NCBI and UniProt systems, enabling more than 40 publicly available functional annotation sources to be comprehensively integrated and centralized by the DAVID gene clusters. The simple, pair-wise, text format files which make up the DAVID Knowledgebase are freely downloadable for various data analysis uses. In addition, a well organized web interface allows users to query different types of heterogeneous annotations in a high-throughput manner. CONCLUSION: The DAVID Knowledgebase is designed to facilitate high throughput gene functional analysis. For a given gene list, it not only provides the quick accessibility to a wide range of heterogeneous annotation data in a centralized location, but also enriches the level of biological information for an individual gene. Moreover, the entire DAVID Knowledgebase is freely downloadable or searchable at http://david.abcc.ncifcrf.gov/knowledgebase/.