Requirements engineering (RE) is a critical set of activities in the software development life cycle (SDLC). Without effective requirements elicitation, organization, communication, and understanding software engineers cannot build quality soft-ware. Thus, it is necessary for software stakeholders to facilitate the SDLC by following best practices and utilizing software tools as needed to ensure requirements are well understood. One area where RE still faces issues, despite stakeholders' best efforts, is the communication of requirements amongst the various stakeholders. Software stakeholders consist of the customers, developers, managers, end users, and others with a vested interest in the software, and they typically all have different skillsets, backgrounds, vernaculars, and understanding of the requirements. These differences naturally lead to miscommunications which can lead to redundant, missing, or conflicting requirements, especially when customer and end user domains include complex vocabularies developers may not be accustomed to, and vice versa, e.g., biology, physics, and medicine. One approach in recent works to address this challenge has been to bridge the communication gap between stakeholders by constructing domain-specific ontologies using natural language processing (NLP) and Wikipedia [1]. With these ontologies, stakeholders have a convenient tool they can use to translate and understand specific requirements in the terminologies they're accustomed to. These techniques have shown promising potential, however there are computational challenges associated with efficiently handling a large dataset like Wikipedia. In particular, parsing internal links from Wikipedia article metadata can be a bottleneck in such ontology-construction systems. In this work we address this issue by implementing a program for memory-efficient parallel internal link extraction from Wikipedia articles. This builds on the work of Rodriguez et al. [2] by optimizing additional phases in the knowledge acquisition process.

In this work, we consider distance-based clustering of partial lexicographic preference trees (PLP-trees), intuitive and compact graphical representations of user preferences over multi-valued attributes. To compute distances between PLP-trees, we propose a polynomial time algorithm that computes Kendall's Tau distance directly from the trees and show its efficacy compared to the brute-force algorithm. To this end, we implement several clustering methods (i.e., spectral clustering, affinity propagation, and agglomerative nesting) augmented by our distance algorithm, experiment with clustering of up to 10,000 PLP-trees, and show the effectiveness of the clustering methods and visualizations of their results.

In this paper, we describe a large-scale, long-term study of a National Science Foundation undergraduate research program via the Computer and Information Science and Engineering (CISE) Research Experiences for Undergraduates (REU) Evaluation Toolkit project. The overarching research questions are: what attitudinal shifts occur for undergraduate research experience student participants, and do experience perceptions differ based upon student demographics? Results across two cohort years showed statistically significant gains for several key program level outcomes. However, intent to go to graduate school decreased in both cohorts. Gender and ethnicity differences were observed. Women and underrepresented minority group students had significantly higher scores for several factors. These findings are discussed in context of culturally relevant pedagogy for undergraduate research programs. This study is unique in its scale and scope, providing a depiction of student outcomes across multiple cohorts of a national sample including over 58 computing research sites and more than 900 undergraduate students.