28 citations · 69 across the 11 of their papers we have counts for
11 papers
Quality Model for Machine Learning Components
Grace A. Lewis, Rachel Brower-Sinning, Robert Edman +5
Despite increased adoption and advances in machine learning (ML), there are studies showing that many ML prototypes do not reach the production stage and that testing is still larg…
Manifesto from Dagstuhl Perspectives Workshop 24452 -- Reframing Technical Debt
Paris Avgeriou, Ipek Ozkaya, Heiko Koziolek +2
This is the Dagstuhl Perspectives Workshop 24452 manifesto on Reframing Technical Debt. The manifesto begins with a one-page summary of Values, Beliefs, and Principles. It then ela…
Defining a Reference Architecture for Edge Systems in Highly-Uncertain Environments
Kevin Pitstick, Marc Novakouski, Grace A. Lewis +1
Increasing rate of progress in hardware and artificial intelligence (AI) solutions is enabling a range of software systems to be deployed closer to their users, increasing applicat…
Using Quality Attribute Scenarios for ML Model Test Case Generation
Rachel Brower-Sinning, Grace A. Lewis, Sebastían Echeverría +1
Testing of machine learning (ML) models is a known challenge identified by researchers and practitioners alike. Unfortunately, current practice for ML model testing prioritizes tes…
Technical Debt Management: The Road Ahead for Successful Software Delivery
Paris Avgeriou, Ipek Ozkaya, Alexander Chatzigeorgiou +5
Technical Debt, considered by many to be the 'silent killer' of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromis…
A Synthesis of Green Architectural Tactics for ML-Enabled Systems
Heli Järvenpää, Patricia Lago, Justus Bogner +3
The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated growing interest in understanding their environmental impact and the challenges associate…
Industry Experiences with Large-Scale Refactoring
James Ivers, Robert L. Nord, Ipek Ozkaya +3
Software refactoring plays an important role in software engineering. Developers often turn to refactoring when they want to restructure software to improve its quality without cha…
Detecting Discussions of Technical Debt
Ipek Ozkaya, Zachary Kurtz, Robert L. Nord +2
Technical debt (TD) refers to suboptimal choices during software development that achieve short-term goals at the expense of long-term quality. Although developers often informally…
Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems
Grace A. Lewis, Stephany Bellomo, Ipek Ozkaya
Increasing availability of machine learning (ML) frameworks and tools, as well as their promise to improve solutions to data-driven decision problems, has resulted in popularity of…
Priority Quality Attributes for Engineering AI-enabled Systems
Lena Pons, Ipek Ozkaya
Deploying successful software-reliant systems that address their mission goals and user needs within cost, resource, and expected quality constraints require design trade-offs. The…
What to Fix? Distinguishing between design and non-design rules in automated tools
Neil A. Ernst, Stephany Bellomo, Ipek Ozkaya +1
Technical debt---design shortcuts taken to optimize for delivery speed---is a critical part of long-term software costs. Consequently, automatically detecting technical debt is a h…