Strengthening the Synapse between Outpatient Neurological Care and Inpatient Referral

Objective The goal of this project is to investigate if established neurology patients are appropriately referred to the emergency room. We suspect that there are patients that could be more effectively triaged to prevent unnecessary visits to the emergency department. If this is the case, implementing an intervention such as offering expedited visits or contingency plan, may reduce non-emergent inpatient consultative services5. This would also improve outpatient communication and decrease utilization of both ER and patient resources

DLint: dynamically checking bad coding practices in JavaScript

JavaScript has become one of the most popular programming languages, yet it is known for its suboptimal design. To effectively use JavaScript despite its design flaws, developers try to follow informal code quality rules that help avoid correctness, maintainability, performance, and security problems. Lightweight static analyses, implemented in "lint-like" tools, are widely used to find violations of these rules, but are of limited use because of the language's dynamic nature. This paper presents DLint, a dynamic analysis approach to check code quality rules in JavaScript. DLint consists of a generic framework and an extensible set of checkers that each addresses a particular rule. We formally describe and implement 28 checkers that address problems missed by state-of-the-art static approaches. Applying the approach in a comprehensive empirical study on over 200 popular web sites shows that static and dynamic checking complement each other. On average per web site, DLint detects 49 problems that are missed statically, including visible bugs on the web sites of IKEA, Hilton, eBay, and CNBC

Study of integrating random and symbolic testing for object-oriented software

Testing is currently the main technique adopted by the industry for improving the quality, reliability, and security of software. In order to lower the cost of manual testing, automatic testing techniques have been devised, such as random and symbolic testing, with their respective trade-offs. For example, random testing excels at fast global exploration of software, while it plateaus when faced with hard-to-hit numerically-intensive execution paths. On the other hand, symbolic testing excels at exploring such paths, while it struggles when faced with complex heap class structures. In this paper, we describe an approach for automatic unit testing of object-oriented software that integrates the two techniques. We leverage feedback-directed unit testing to generate meaningful sequences of constructor+method invocations that create rich heap structures, and we in turn further explore these sequences using dynamic symbolic execution. We implement this approach in a tool called JDoop, which we augment with several parameters for fine-tuning its heuristics; such “knobs” allow for a detailed exploration of the various trade-offs that the proposed integration offers. Using JDoop, we perform an extensive empirical exploration of this space, and we describe lessons learned and guidelines for future research efforts in this area