Hacia la docencia “cloud-first” en la ingeniería de software

Resumen de ponencia invitadaEn 2007, no existían ni la computación en la nube como hoy la conocemos, ni los smartphones cuyo arquetipo es el iPhone de Apple. Apenas una década más adelante, Amazon Web Services controla la tercera parte de un mercado valorado en 20 mil millones de dólares, la penetración mundial de smartphones está en 30% y en todos tipos de móviles en 60%, y el porcentaje de visitas a sitios web desde los smartphones ha superado en porcentaje al de los ordenadores. En Berkeley en 2007, ya veíamos que había que reenfocar la docencia de ingeniería de software con una filosofía de “Cloud first.” En la primera parte de esta charla contaré como intentamos hacerlo, que hemos aprendido, cuáles aspectos del enfoque nos han servido bien y cuáles todavía faltan por perfeccionar. Ahora en 2018, con la consolidación de las arquitecturas orientadas a servicios y la proliferación de dispositivos móviles, también nos toca reenfocar hacia una aproximación “mobile first.” . En este nuevo escenario, se puede observar directamente que “mobile first” + “cloud first” se puede materializar en un enfoque “API first” dado que la arquitectura que domina este ecosistema consiste de servicios conectados tanto entre sí como con sus clientes móviles a través de interfaces ligeros estilo REST. En la segunda parte de la charla exploraremos qué puede significar esto para la docencia de ingeniería de software y qué planes estamos desarrollando para avanzar la enseñanza en buena dirección, contando con colaboraciones a nivel mundial y desde una perspectiva de docencia tanto digital como presencial.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Red de Ciencia e Ingeniería de Servicios (RCIS), Master en Ingeniería Informática por la Universidad de Málaga y Departamento de Lenguajes y Ciencias de la Computació

GASPACHO : a generic automatic solver using proximal algorithms for convex Huge optimization problems

Many inverse problems (e.g., demosaicking, deblurring, denoising, image fusion, HDR synthesis) share various similarities: degradation operators are often modeled by a specific data fitting function while image prior knowledge (e.g., sparsity) is incorporated by additional regularization terms. In this paper, we investigate automatic algorithmic techniques for evaluating proximal operators. These algorithmic techniques also enable efficient calculation of adjoints from linear operators in a general matrix-free setting. In particular, we study the simultaneous-direction method of multipliers (SDMM) and the parallel proximal algorithm (PPXA) solvers and show that the automatically derived implementations are well suited for both single-GPU and multi-GPU processing. We demonstrate this approach for an Electron Microscopy (EM) deconvolution problem

OmniFill: Domain-Agnostic Form Filling Suggestions Using Multi-Faceted Context

Predictive suggestion systems offer contextually-relevant text entry completions. Existing approaches, like autofill, often excel in narrowly-defined domains but fail to generalize to arbitrary workflows. We introduce a conceptual framework to analyze the compound demands of a particular suggestion context, yielding unique opportunities for large language models (LLMs) to infer suggestions for a wide range of domain-agnostic form-filling tasks that were out of reach with prior approaches. We explore these opportunities in OmniFill, a prototype that collects multi-faceted context including browsing and text entry activity to construct an LLM prompt that offers suggestions in situ for arbitrary structured text entry interfaces. Through a user study with 18 participants, we found that OmniFill offered valuable suggestions and we identified four themes that characterize users' behavior and attitudes: an "opportunistic scrapbooking" approach; a trust placed in the system; value in partial success; and a need for visibility into prompt context.Comment: 14 pages, 5 figure

What Agile processes should we use in software engineering course projects?

Producción CientíficaWhile project-based software engineering courses aim to provide learning opportunities grounded in professional processes, it is not always possible to replicate every process in classrooms due to course constraints. Previous studies observed how students react to various processes and gave retroactive recommendations. In this study, we instead combine a field study on professional Agile (eXtreme Programming, XP) teams and an established team process taxonomy to proactively select team processes to incorporate in a project-based software engineering course. With collected knowledge from the field study, we choose three XP processes to augment the design of a mature software engineering project course. We choose processes that are 1) considered important by professionals, and 2) complete with respect to coverage of the taxonomy's main categories. We then compare the augmented course design with the original design in a case study. Our results suggest that 1) even without extra resources, adding these new processes does not interfere with learning opportunities for XP processes previously existing in the course design; 2) student teams experience similar benefits from these new processes as professional teams do, and students appreciate the usefulness and value of the processes. In other words, our approach allows instructors to make conscious choices of XP processes that improve student learning outcomes while exposing students to a more complete set of processes and thus preparing them better for professional careers. Course designers with limited resources are encouraged to use our methodology to evaluate and improve the designs of their own project-based courses.Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R)Junta de Castilla y León (project VA257P18) by the European Commission under project grant 588438-EPP-1-2017-1-EL-EPPKA2- K

The Importance of Computing Education Research

Interest in computer science is growing. As a result, computer science (CS) and related departments are experiencing an explosive increase in undergraduate enrollments and unprecedented demand from other disciplines for learning computing. According to the 2014 CRA Taulbee Survey, the number of undergraduates declaring a computing major at Ph.D. granting departments in the US has increased 60% from 2011-2014 and the number of degrees granted has increased by 34% from 2008-2013

The Importance of Computing Education Research

Interest in computer science is growing. As a result, computer science (CS) and related departments are experiencing an explosive increase in undergraduate enrollments and unprecedented demand from other disciplines for learning computing. According to the 2014 CRA Taulbee Survey, the number of undergraduates declaring a computing major at Ph.D. granting departments in the US has increased 60% from 2011-2014 and the number of degrees granted has increased by 34% from 2008-2013

The Importance of Computing Education Research

Interest in computer science is growing. As a result, computer science (CS) and related departments are experiencing an explosive increase in undergraduate enrollments and unprecedented demand from other disciplines for learning computing. According to the 2014 CRA Taulbee Survey, the number of undergraduates declaring a computing major at Ph.D. granting departments in the US has increased 60% from 2011-2014 and the number of degrees granted has increased by 34% from 2008-2013. However, this growth is not limited to higher education. New York City, San Francisco and Oakland public schools will soon be offering computer science to all students at all schools from preschool to 12th grade, although it will be an elective for high school students. This unprecedented demand means that CS departments are likely to teach not only more students in the coming decades, but more diverse students, with more varied backgrounds, motivations, preparations, and abilities. This growth is an unparalleled opportunity to expand the reach of computing education. However, this growth is also a unique research challenge, as we know very little about how best to teach our current students, let alone the students soon to arrive. The burgeoning field of Computing Education Research (CER) is positioned to address this challenge by answering research questions such as, how should we teach computer science, from programming to advanced principles, to a broader and more diverse audience? We argue that computer science departments should lead the way in establishing CER as a foundational research area of computer science, discovering the best ways to teach CS, and inventing the best technologies with which to teach it. This white paper provides a snapshot of the current state of CER and makes actionable recommendations for academic leaders to grow CER as a successful research area in their departments.Comment: A Computing Community Consortium (CCC) white paper, 12 page