Wildlife Master Volunteers: A Multi-County Approach to Resolving Human-Wildlife Conflicts

The Wildlife Master (WM) Program in Colorado was modeled after the highly successful Master Gardener volunteer program. In 10 highly populated suburban counties with large rural areas surrounding the Denver Metro Area, Colorado State University (CSU) Cooperative Extension Natural Resources agents train, supervise and manage these volunteers in the identification, referral, and resolution of wildlife damage issues. High quality, research-based training is provided by university faculty and other professionals in public health, animal damage control, wildlife management and animal behavior. Inquiries are responded to mainly via telephone. Calls by concerned residents are forwarded to WMs who provide general information about human-wildlife conflicts and possible ways to resolve complaints. Each volunteer serves a minimum of 14 days on phone duty annually, calling in from a remote location to a voice mail system from which phone messages can be conveniently retrieved. Response time per call is generally less than 24 hours. During 2004, more than 2,000 phone calls, e-mail messages and walk-in requests for assistance were fielded by 100 cooperative extension WMs. Calls fielded by volunteers in one county increased five-fold during the past five years, from 100 calls to over 500 calls annually. Valued at the rate of approximately $18.00 per volunteer hour, the leveraged value of each WM was about $450 in 2005, based on 25 hours of service and training. The estimated value of the program to Colorado in 2004 was over $45,000 of in-kind service, or about one full-time equivalent faculty member. This paper describes components of Colorado’s WM Program, with guides to the set-up of similar programs in other states.

SimSight: A Virtual Machine Based Dynamic Call Graph Generator

One problem with using component-based software development approach is that once software modules are reused over generations of products, they form legacy structures that can be challenging to understand, making validating these systems difficult. Therefore, tools and methodologies that enable engineers to see interactions of these software modules will enhance their ability to make these software systems more dependable. To address this need, we propose SimSight, a framework to capture dynamic call graphs in Simics, a widely adopted commercial full-system simulator. Simics is a software system that simulates complete computer systems. Thus, it performs nearly identical tasks to a real system but at a much lower speed while providing greater execution observability. We have implemented SimSight to generate dynamic call graphs of statically and dynamically linked functions in x86/Linux environment. A case study illustrates how we can use SimSight to identify sources of software errors. We then evaluate its performance using 12 integer programs from SPEC CPU2006 benchmark suite.

JVM-based Techniques for Improving Java Observability

Observability measures the support of computer systems to accurately capture, analyze, and present (collectively observe) the internal information about the systems. Observability frameworks play important roles for program understanding, troubleshooting, performance diagnosis, and optimizations. However, traditional solutions are either expensive or coarse-grained, consequently compromising their utility in accommodating today’s increasingly complex software systems. New solutions are emerging for VM-based languages due to the full control language VMs have over program executions. Existing such solutions, nonetheless, still lack flexibility, have high overhead, or provide limited context information for developing powerful dynamic analyses. In this thesis, we present a VM-based infrastructure, called marker tracing framework (MTF), to address the deficiencies in the existing solutions for providing better observability for VM-based languages. MTF serves as a solid foundation for implementing fine-grained low-overhead program instrumentation. Specifically, MTF allows analysis clients to: 1) define custom events with rich semantics ; 2) specify precisely the program locations where the events should trigger; and 3) adaptively enable/disable the instrumentation at runtime. In addition, MTF-based analysis clients are more powerful by having access to all information available to the VM. To demonstrate the utility and effectiveness of MTF, we present two analysis clients: 1) dynamic typestate analysis with adaptive online program analysis (AOPA); and 2) selective probabilistic calling context analysis (SPCC). In addition, we evaluate the runtime performance of MTF and the typestate client with the DaCapo benchmarks. The results show that: 1) MTF has acceptable runtime overhead when tracing moderate numbers of marker events; and 2) AOPA is highly effective in reducing the event frequency for the dynamic typestate analysis; and 3) language VMs can be exploited to offer greater observability.