IEEE Computer
2026
Magazine Editorials.
I write the monthly Spotlight on Transactions column for IEEE Computer and serve on the editorial team for the AI Security & Privacy section of IEEE Security & Privacy. The goal is amplification, not summary — surface the field’s most important journal papers in software engineering and put them in front of an audience that would otherwise never find them, in a form a busy reader can absorb in fifteen minutes.
Authored columns.
Editorial pieces authored for IEEE magazines — by year and venue, newest first. Generated automatically from DBLP.
IEEE Computer
2026
10 columns
IEEE Computer
2026
IEEE Computer
2026
The Open Source Bug Hunter: When Smaller Models Outperform the Giants at Finding Faults
IEEE Computer
2026
Orchestrated Entropy: Foundation Model Nondeterminism for SOC
IEEE Computer
2026
The Price of Intelligence: Can AI Afford to Be Sustainable?
IEEE Computer
2026
The Virtue of Hallucination: When AI Mistakes Make Software Safer
“LLMorpheus: Mutation Testing Using Large Language Models,” published in IEEE Transactions on Software Engineering in 2025, proposes a large language model-driven mutation testing framework that moves beyond fixed operator catalogs by using context-aware code infilling.
IEEE Computer
2026
Toward Reliable Security Operations Center Testing With Foundation Models
This article presents a practical method for using foundation models to generate realistic, structured scenarios that support repeatable testing of Security Operations Center (SOC) analytics as environments evolve.
IEEE Computer
2026
Secrets in the Synapses: When Steganography Meets Large Language Models
Digital steganography has traditionally focused on hiding information within visible media, but generative artificial intelligence is shifting the hiding place itself. Li et al., in “Steganography in Large Language Models” (IEEE Transactions on Artificial Intelligence), show how models can carry hidden information within their own parameters.
IEEE Computer
2026
LLM-Powered Security Test Generation: Oracles, Vulnerability Probes, and Adversarial Inputs
Large language models (LLMs) can synthesize test oracles from invariants where ground truth is unavailable, translate vulnerability catalogs such as CWE, OWASP, and CVE into executable probes, and generate adversarial inputs that stress both traditional software and LLM-based systems.
IEEE Computer
2026
Mind the Overlap: Trustworthy Evaluation for Large Code Models
In this month’s Spotlight on Transactions, we showcase an IEEE Transactions on Software Engineering article by López et al., exploring how hidden data leakage arising from interdataset code duplication artificially elevates benchmark performance and to outline actionable strategies for maintaining rigorous, reliable model assessment.
IEEE Computer
2026
Human or Machine? Rebuilding Trust in the Age of AI-Based Text Generation
2025
8 columns
IEEE Computer
2025
From Heuristics to Intelligence: Large Language Model-Driven Test Case Generation
IEEE Computer
2025
Prompt Alchemy: Engineering the Magic of Code
IEEE Computer
2025
Breaking Bottlenecks in Large Language Model Inference With Adaptive Cache Management
IEEE Computer
2025
Code, Chaos, and Clarity: Neurosymbolic Approaches to Trustworthy Software Automation
IEEE Computer
2025
When Databases Age: How SQL Server and MySQL Handle the Test of Time
IEEE Computer
2025
Smarter, Not Harder: Efficient Artificial Intelligence Training With Selective Data
IEEE Computer
2025
Pixels of Deception: How Evolutionary Algorithms Break AI Reliability
IEEE Computer
2025