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.

All the World's a Stage: Reaching English Language Learners through Drama

The use of drama with language instruction has long been considered a legitimate practice. A recent survey of the literature shows that English Language Learners (ELLs) may have even more to benefit from the use of drama in the classroom. Studies showed an increase in language and problem-solving ability, as well as student self-efficacy that was transferrable across activities. Following an analysis of the literature is a proposed curriculum based on the findings of the researchers cited. This unit is centered on dramatic activities that make use of all four language domains and includes the examination of plays in writing, on the stage, and as a playwright. In the end, students will be asked to combine all of their skills to put on a completely student-created production. An analysis of the factors surrounding the implementation of such a unit follows the unit itself.