Assessing Needs for Computer Pest Management Software in Nebraska Agriculture

A mail survey was conducted to assess current computer hardware use and perceived needs of potential users for software related to crop pest management in Nebraska. Surveys were sent to University of Nebraska-Lincoln agricultural extension agents, agribusiness personnel (including independent crop consultants), and crop producers identified by extension agents as computer users. There were no differences between the groups in several aspects of computer hardware use (percentage computer use, percentage IBM-compatible computer, amount of RAM memory, percentage with hard drive, hard drive size, or monitor graphics capability). Responses were similar among the three groups in several areas that are important to crop pest management (pest identification, pest biology, treatment decision making, control options, and pesticide selection), and a majority of each group expressed the need for additional sources of such information about insects, diseases, and weeds. However, agents mentioned vertebrate pest management information as a need more often than the other two groups. Also, majorities of each group expressed an interest in using computer software, if available, to obtain information in these areas. Appropriate software to address these needs should find an audience among all three groups.

Predicting Accurate and Actionable Static Analysis Warnings: An Experimental Approach

Static analysis tools report software defects that may or may not be detected by other verification methods. Two challenges complicating the adoption of these tools are spurious false positive warnings and legitimate warnings that are not acted on. This paper reports automated support to help address these challenges using logistic regression models that predict the foregoing types of warnings from signals in the warnings and implicated code. Because examining many potential signaling factors in large software development settings can be expensive, we use a screening methodology to quickly discard factors with low predictive power and cost-effectively build predictive models. Our empirical evaluation indicates that these models can achieve high accuracy in predicting accurate and actionable static analysis warnings, and suggests that the models are competitive with alternative models built without screening.

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.