Much of the previous work on preparing undergraduates for industry focusses on software engineering and the skills needed to design and to implement new software systems. There has been relatively little attention given to the skills needed to maintain, to modify, and to repair systems already in use. These skills are captured in the emerging discipline of site reliability engineering, a relative of software reliability engineering. Site reliability engineers use a distinct set of skills, tools, and techniques for managing complex production systems. More importantly, they have a mindset that prioritizes high performance and reliability while attempting to minimize repetitive tasks done by human operators. In this paper we describe an upper-division elective that was designed to introduce students to site reliability engineering through hands-on assignments requiring teams to deploy, maintain, and scale a working software system, done alongside readings and discussion of high-stakes episodes from the broader history of complex systems. We discuss the design of the class and reflect on what worked well and not so well in the initial offerings.

Broadening participation in Computer Science requires supporting individuals by helping them feel a sense of belonging to a larger community. In this BOF, we invite participants to brainstorm, to share strategies, and to explore possibilities for building and supporting intentional communities, particularly for underrepresented or marginalized groups. Whether these communities are spaces created by faculty for faculty, by students for students, or by faculty for students, we need to be thoughtful and deliberate about the ways in which we set up, build, and maintain supportive relationships. Indeed, we now increasingly need techniques and strategies that will work both virtually and in person and that can be sustained by both live and asynchronous discussions. During this virtual session, we invite you to grab a snack and a cup of tea and join us as we setup our own 'SIGCSE CommuniTEA' with the intention of supporting each other as we collectively work towards helping all individuals truly feel that CS is for them and towards increasing that sense of belonging to both smaller and larger computer science communities.

As departments search for meaningful ways to incorporate material on ethics into the computer science curriculum, effective assessment is critical. Motivated by a framework proposed by the ImpactCS project, we describe simple open-ended questions that could provide insight into learners' progress from awareness through evaluation to responsible behavior. In a study of 22 graduating seniors at a liberal arts college we found more complete answers to our open-ended questions tended to correlate with students' perception of personal growth in ethical awareness since arriving at college.

Network modeling of biological systems is a powerful tool for analysis of high-throughput datasets by computational systems biologists. Integration of networks to form a heterogeneous model requires that each network be as noise-free as possible while still containing relevant biological information. In earlier work, we have shown that the graph theoretic properties of gene correlation networks can be used to highlight and maintain important structures such as high degree nodes, clusters, and critical links between sparse network branches while reducing noise. In this paper, we propose the design of advanced network filters using structurally related graph theoretic properties. While spanning trees and chordal subgraphs provide filters with special advantages, we hypothesize that a hybrid subgraph sampling method will allow for the design of a more effective filter preserving key properties in biological networks. That the proposed approach allows us to optimize a number of parameters associated with the filtering process which in turn improves upon the identification of essential genes in mouse aging networks.

This study examines the extent to which novice computing students with minimal computer science coursework and no training in user interface (UI) evaluation consider UI concepts such as usability, user experience, and the context in which software will be used when evaluating an interface. In analyzing the responses of 149 novice computer science students who were asked to evaluate two interfaces for converting temperatures between Fahrenheit and Celsius, we observed that students generally considered usability and user experience factors, but were less likely to consider context. For educators, this exact task could be given to a class in order to initiate discussion of user-centered design; the study also provides a framework for structuring the discussion. More generally, the results of this study provide insight into some opportunities and challenges in teaching good interface design and evaluation skills.

This paper investigates what students understand about algorithm efficiency before receiving any formal instruction on the topic. We gave students a challenging search problem and two solutions, then asked them to identify the more efficient solution and to justify their choice. Many students did not use the standard worst-case analysis of algorithms; rather they chose other metrics, including average-case, better for more cases, better in all cases, one algorithm being more correct, and better for real-world scenarios. Students were much more likely to choose the correct algorithm when they were asked to trace the algorithms on specific examples; this was true even if they traced the algorithms incorrectly.

We examine students' commonsense understanding of computer science concepts before they receive any formal instruction in the field. For this study, we asked students on the first day of a CS1 class to describe in English how they would arrange a set of numbers in ascending, sorted order; we then repeated the experiment asking students to sort a list of dates (in mm/dd/yyyy format).We found that a majority of students described a coherent algorithm; some described versions of insertion or selection sort, but many gave unexpected algorithms. We also found significant differences between responses given for sorting numbers versus dates. Based on our analysis of the data we suggest that beginning-programming instructors more explicitly discuss data types, begin loop instruction with post-test loops, assist students in recognizing implicit conditional and iteration use in natural language solutions to probls, and recognize that novices and experts focus on different aspects of the probl in even basic probl solving tasks.

We examine students' commonsense understanding of computer science concepts before they receive any formal instruction in the field. Specifically, we asked students on the first day of a CS1 class to describe in English how they would arrange a set of numbers in ascending, sorted order. We repeated the experiment with students in an introductory economics course, and again with a sub-population of the CS1 students after ten weeks of Java instruction.We found that a majority of beginning computing students could describe a coherent algorithm to correctly sort a list of numbers, while less than a third of general college students could do so. Many students gave versions of selection or insertion sort, but the most common algorithm treated numbers as strings and manipulated them digit by digit. Students who used iteration strongly preferred post-test loops. Finally, some aspects of student performance became worse after ten weeks of CS1 instruction.

We are studying what students know about computer science-related topics before they take formal coursework at the university level. Preliminary results suggest that entering students have a fairly sophisticated understanding of algorithms. We are exploring other central computing topics for similar shared commonsense understanding.

What measurable effect do the language and paradigm used in early programming classes have on novice programmers' ability to do design? This work investigates the question by using data collected from 136 first competency students as part of a multi-national, multi-institutional study of students' approach to and attitudes toward design. Analysis of a number of surface characteristics of their designs found strikingly few differences between designs produced by students at schools that teach using objects-early, imperative-early, and functional-early paradigms. A similar lack of difference was found between students at C++-first and Java-first schools. While statistically significant differences are found for three characteristic comparisons across language and paradigm, these results seem to have little meaning for teaching given the complexity of the null hypotheses tested in those three cases. In particular, for the following design characteristics no statistically significant differences across language or paradigm of early instruction were found: attempt to address requirements, type of design produced, number of parts in design, recognition of ambiguity in design, and connectedness of design.

We study how the relative values placed on a variety of software design criteria differs between beginning students, advanced students, and educators. We also consider how these values change depending on the specific design situation. Statistical analysis of data collectedfrom over 300 subjects reveals relatively small differences between the two student populations and significant differences between educators and students. In addition to often valuing different criteria, educators also value criteria more consistently across the design situations.