James C. Davis

Publications

• Fast and accurate incremental feedback for students' software tests using selective mutation analysis
• The impact of regular expression denial of service (ReDoS) in practice: an empirical study at the ecosystem scale
• The Case of the Poisoned Event Handler: Weaknesses in the Node.js Event-Driven Architecture
• Node.fz: Fuzzing the Server-Side Event-Driven Architecture
• An Empirical Study of GraphQL Schemas
• “If security is required”: Engineering and Security Practices for Machine Learning-based IoT Devices
• Exploiting Input Sanitization for Regex Denial of Service
• Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks
• Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks
• Poster: Establishing trust in vehicle-to-vehicle coordination: a sensor fusion approach
• An Empirical Study on the Impact of Parameters on Mobile App Energy Usage
• Why Arent Regular Expressions a Lingua Franca? An Empirical Study on the Re-use and Portability of Regular Expressions
• Reflecting on Recurring Failures in IoT Development
• Ursprung: Provenance for Large-Scale Analytics Environments
• Improving reproducibility of data science pipelines through transparent provenance capture
• Edgewise: a better stream processing engine for the edge
• A Principled Approach to GraphQL Query Cost Analysis
• Using Selective Memoization to Defeat Regular Expression Denial of Service (ReDoS)
• A replication of ‘DeepBugs: a learning approach to name-based bug detection’
• Expectations and Experiences of Short-Term Study Abroad Leadership Teams
• A Sense of Time for JavaScript and Node.js: First-Class Timeouts as a Cure for Event Handler Poisoning
• Regexes are Hard: Decision-making, Difficulties, and Risks in Programming Regular Expressions
• Testing regex generalizability and its implications: A large-scale many-language measurement study
• SoK: Analysis of Software Supply Chain Security by Establishing Secure Design Properties
• An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain
• Tree-Based Unidirectional Neural Networks for Low-Power Computer Vision
• Towards Rehosting Embedded Applications as Linux Applications
• Evolution of Winning Solutions in the 2021 Low-Power Computer Vision Challenge
• An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures
• A First Look at the General Data Protection Regulation (GDPR) in Open-Source Software
• Reflecting on the Use of the Policy-Process-Product Theory in Empirical Software Engineering
• PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
• Establishing Provenance Before Coding: Traditional and Next-Gen Signing
• On the Contents and Utility of IoT Cybersecurity Guidelines