An evolved cognitive bias for social norms

Social norms are a widely used concept for explaining human behavior, but there are few studies exploring how we cognitively utilize them. We incorporate here an evolutionary approach to studying social norms, predicting that if norms have been critical to biological fitness, then individuals should have adaptive mechanisms to conform to, and avoid violating, norms. A cognitive bias toward norms is one specific means by which individuals could achieve this. To test this, we assessed whether individuals have greater recall for normative information than for nonnormative information. Three experiments were performed in which participants read a text and were then tested on their recall of behavioral content. The data suggest that individuals have superior recall for normative social information and that performance is not related to rated importance. We discuss how such a cognitive bias may ontogenetically develop and identify possible hypotheses that distinguish between alternative explanatory accounts for social norms

Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required

The Screening Scale for Pedophilic Interests (SSPI): Construct, Predictive, and Incremental Validity

This study of 410 adult male sex offenders against children, using data from the Dynamic Supervision Project (Hanson, Harris, Scott, & Helmus, 2007), examined the construct, predictive, and incremental validity of the Screening Scale for Pedophilic Interests (SSPI; Seto & Lalumière, 2001), a brief proxy measure of phallometrically assessed sexual response to children that is based on sexual victim characteristics. As predicted, the SSPI was significantly related to the Deviant Sexual Interests item on the STABLE-2007 (Hanson et al., 2007), a dynamic risk measure encompassing multiple domains, and with the Deviant Sexual Interests item from its predecessor, the STABLE-2000 (Hanson et al., 2007). The SSPI was unrelated (or more weakly related) to items measuring general antisociality. In addition, the SSPI significantly predicted sexual recidivism, defined as new charges or convictions for sexual offenses, and a broader sexual recidivism outcome that included breaches of community supervision conditions that might involve sexually motivated behavior (e.g., being in the presence of children unsupervised). The SSPI did not add to the predictive accuracy of 2 actuarial risk measures, the Static-99R and Static-200R (Helmus, Thornton, Hanson, & Babchishin, 2012), but it did add to the predictive accuracy of the STABLE-2007. Additional analyses suggest the SSPI can serve as a substitute for the STABLE-2007 Deviant Sexual Interests item, if necessary (e.g., in archival research), when assessing sexual offenders against children

JWalk: a tool for lazy, systematic testing of java classes by design introspection and user interaction

Popular software testing tools, such as JUnit, allow frequent retesting of modified code; yet the manually created test scripts are often seriously incomplete. A unit-testing tool called JWalk has therefore been developed to address the need for systematic unit testing within the context of agile methods. The tool operates directly on the compiled code for Java classes and uses a new lazy method for inducing the changing design of a class on the fly. This is achieved partly through introspection, using Java’s reflection capability, and partly through interaction with the user, constructing and saving test oracles on the fly. Predictive rules reduce the number of oracle values that must be confirmed by the tester. Without human intervention, JWalk performs bounded exhaustive exploration of the class’s method protocols and may be directed to explore the space of algebraic constructions, or the intended design state-space of the tested class. With some human interaction, JWalk performs up to the equivalent of fully automated state-based testing, from a specification that was acquired incrementally

Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort