Software Engineering Institute | Carnegie Mellon University
Software Engineering Institute | Carnegie Mellon University

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Showing 1 - 10 of 63 results for the Type - Conference Paper

Conference Paper | November 2016 - Conference Paper Static Analysis Alert Audits: Lexicon & Rules By David Svoboda, Lori Flynn, William Snavely

In this paper, the authors provide a suggested set of auditing rules and a lexicon for auditing static analysis alerts.

Conference Paper | November 2016 - Conference Paper Automated Code Repair Based on Inferred Specifications By William Klieber, William Snavely

In this paper, the authors describe automated repairs for three types of bugs: integer overflows, missing array bounds checks, and missing authorization checks.

Conference Paper | July 2016 - Conference Paper Verbalization: Narration of Autonomous Robot Experience By Stephanie Rosenthal, Sai P. Selvaraj (Carnegie Mellon University), Manuela Veloso (Carnegie Mellon University)

This work addresses narrations of autonomous mobile robot navigation, contributing the concept of verbalization as a parallel to the concept of visualization.

Conference Paper | July 2016 - Conference Paper Verbalization: Narration of Autonomous Robot Experience By Stephanie Rosenthal, Sai P. Selvaraj (Carnegie Mellon University), Manuela Veloso (Carnegie Mellon University)

In this work, we address the generation of narrations of autonomous mobile robot navigation experiences.

Conference Paper | May 2016 - Conference Paper A Reference Architecture for Big Data Systems in the National ... By John Klein, Ross Buglak (Data to Decisions Cooperative Research Centre), David Blockow (Data to Decisions Cooperative Research Centre), Troy Wuttke (Data to Decisions Cooperative Research Centre), Brenton Cooper (Data to Decisions Cooperative Research Centre)

This paper presents a reference architecture for big data systems that is focused on addressing typical national defense requirements and that is vendor-neutral.

Conference Paper | May 2016 - Conference Paper Got Technical Debt? Surfacing Elusive Technical Debt in ... By Stephany Bellomo, Robert Nord, Ipek Ozkaya, Mary Popeck

This paper reports on a study of issues from issue trackers to identify technical debt and present an approach for reporting technical debt in issue trackers.

Conference Paper | April 2016 - Conference Paper Managing Technical Debt in Software Engineering By Paris Avgeriou (University of Groningen - The Netherlands)), Philippe Kruchten, Ipek Ozkaya, Carolyn Seaman (University of Maryland Baltimore County)

This report documents the program and outcomes of Dagstuhl Seminar 16162, ,Managing Technical Debt in Software Engineering.Š We summarize the goals and format of the seminar.

Conference Paper | April 2016 - Conference Paper Model-Driven Observability for Big Data Storage By John Klein, Ian Gorton (Northeastern University), Laila Alhmoud (Carnegie Mellon University), Joel Gao (Carnegie Mellon University), Caglayan Gemici (Carnegie Mellon University), Rajat Kapoor (Carnegie Mellon University), Prasanth Nair (Carnegie Mellon University), Varun Saravagi (Carnegie Mellon University)

This paper presents an architecture that automates metric collection processes for big data systems using a distributed runtime observability framework.

Conference Paper | April 2016 - Conference Paper Missed Architectural Dependencies: The Elephant in the ... By Robert Nord, Raghvinder Sangwan, Julien Delange, Peter H. Feiler, Luke Thomas (Indiana University‹Purdue University), Ipek Ozkaya

This paper presents an in-depth study of a safety-critical system that underwent major changes as a result of missed architectural dependencies.

Conference Paper | December 2015 - Conference Paper Dynamic Parallelism for Simple and Efficient GPU Graph ... By Peter Zhang (Indiana University), Eric Holk (Indiana University), John Matty, Samantha Misurda, Marcin Zalewski (Indiana University), Jonathan Chu, Scott McMillan, Andrew Lumsdaine (Indiana University)

Presented at the 2015 Supercomputing Conference, this paper shows that dynamic parallelism enables relatively high-performance graph algorithms for GPUs.

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