Search Results for author:
Found 19 papers, 8 papers with code
What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts
Building LLM-powered software requires developers to communicate their requirements through natural language, but developer prompts are frequently underspecified, failing to fully capture many user-important requirements.
From Hazard Identification to Controller Design: Proactive and LLM-Supported Safety Engineering for ML-Powered Systems
Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large.
Beyond Accuracy, SHAP, and Anchors -- On the difficulty of designing effective end-user explanations
Transparency and explainability methods aim to provide some help in understanding models, but it remains challenging for developers to design explanations that are understandable to target users and effective for their purpose.
FairSense: Long-Term Fairness Analysis of ML-Enabled Systems
Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years.
Orbit: A Framework for Designing and Evaluating Multi-objective Rankers
In this work, we introduce Orbit, a conceptual framework for Objective-centric Ranker Building and Iteration.
Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products
Large Language Models (LLMs) are increasingly embedded into software products across diverse industries, enhancing user experiences, but at the same time introducing numerous challenges for developers.
What Is Wrong with My Model? Identifying Systematic Problems with Semantic Data Slicing
Machine learning models make mistakes, yet sometimes it is difficult to identify the systematic problems behind the mistakes.
(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIs
Large Language Models (LLMs) are increasingly integrated into software applications.
Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs
Current model testing work has mostly focused on creating test cases.
A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners
Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing challenges.
MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodology in place to do so.
Capabilities for Better ML Engineering
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences.
Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability
The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impedes model accountability and inadvertently abets inappropriate or misuse of models.
Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists.
Feature Interactions on Steroids: On the Composition of ML Models
The lack of specifications is a key difference between traditional software engineering and machine learning.
Understanding the Nature of System-Related Issues in Machine Learning Frameworks: An Exploratory Study
Machine learning (ML) frameworks and the systems developed using them differ greatly from traditional frameworks.
Teaching Software Engineering for AI-Enabled Systems
Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components.
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots
Modern cyber-physical systems (e. g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time.
Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis
Modern software systems provide many configuration options which significantly influence their non-functional properties.
