Non-semantics-preserving transformations for higher-coverage test generation using symbolic execution

Symbolic execution is a well-studied method that can produce high-quality test suites for programs. However, scaling it to real-world applications is a significant challenge, as it depends on the expensive process of solving constraints on program inputs. Our insight is that non-semantics-preserving program transformations can reduce the cost of symbolic execution and the tests generated for the transformed programs can still serve as quality suites for the original program. We present several such transformations that are designed to improve test input generation and/or provide faster symbolic execution. We evaluated these optimizations using a suite of small examples and a substantial subset of Unix's Coreutils. In more than 50% of cases, our approach reduces the test generation time and increases the code coverage.Electrical and Computer Engineerin

Piecing Together the American Voting Puzzle: How Votersâ Personalities and Judgments of Issue Importance Mattered in the 2016 Presidential Election

In the wake of the 2016 election, which surprised pundits and voters on both the left and the right, there has been renewed interest in understanding what predicts American votersâ choices. In this article, we investigate the roles of personality and issue importance in how people voted in the 2016 U.S. election. In this longitudinal study of 403 MTurk workers who voted in the election, we assessed the relations between personality (openness, social dominance orientation, and national identity importance) and issue importance (group rights and social justice, economic rights, and individual and national rights), and voting for Clinton or Trump. Our results indicate that both individual differences and issue importance as measured in July 2016 predicted votes in November. We also found that the links between personality and voting were mediated by issue importance. Implications for political psychology and the study of personality, campaign issues, and voting behavior are discussed.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146841/1/asap12157.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146841/2/asap12157_am.pd

Bridging the enduring gender gap in political interest in Europe: the relevance of promoting gender equality

Notwithstanding the improvement in gender equality in political power and resources in European democracies, this study shows that on average declared interest in politics is 16% lower for women than for men in Europe. This gap remains even after controlling for differences in men’s and women’s educational attainment, material, and cognitive resources. Drawing on the newly developed European Institute for Gender Equality’s (EIGE) Gender Equality Index (GEI) and on the European Social Survey (ESS)-fifth wave, we show that promoting gender equality contributes towards narrowing the magnitude of the differences in political interest between men and women. However, this effect appears to be conditioned by the age of citizens. More specifically, findings show that in Europe gender-friendly policies contribute to bridging the gender gap in political engagement only during adulthood, suggesting that childhood socialisation is more strongly affected by traditional family values than by policies promoting gender equality. In contrast, feminising social citizenship does make a difference by reducing the situational disadvantages traditionally faced by women within the family and in society for middle-aged people and above

Powering reasoning about complex software systems through heuristic methods

Today's real-world software systems are often too complex to reason about formally, which can cause expensive failures which could be avoided with improved analysis in the process of their creation. We here seek to demonstrate that heuristic methods can improve the techniques used to enable and enhance explainability and reasoning for these systems, such as symbolic execution and model checking, thus making the systems they support easier to design, develop, and debug. To this end, we propose a set of new tools for a diverse set of traditionally difficult-to-analyze systems, including neural networks and symbolic execution engines. These tools and techniques use approximation-based insights to show the power of this idea. Experimental evaluation shows that these techniques and tools can improve both explanability and analyzability.Electrical and Computer Engineerin

A Video Game-Integrated Electromyography Biofeedback Device for Use in Physical Therapy

We present a novel system to be used in the rehabilitation of patients with forearm injuries. The system uses surface electromyography (sEMG) recordings from a wireless sleeve to control video games designed to provide engaging biofeedback to the user. An integrated hardware/software system uses a neural net to classify the signals from a user’s muscles as they perform one of a number of common forearm physical therapy exercises. These classifications are used as input for a suite of video games that have been custom-designed to hold the patient’s attention and decrease the risk of noncompliance with the physical therapy regimen necessary to regain full function in the injured limb. The data is transmitted wirelessly from the on-sleeve board to a laptop computer using a custom-designed signal-processing algorithm that filters and compresses the data prior to transmission. We believe that this system has the potential to significantly improve the patient experience and efficacy of physical therapy using biofeedback that leverages the compelling nature of video games