Software engineering scope expands beyond executable code to semi-executable artifacts best diagnosed by the new six-ring Semi-Executable Stack model.
Wobbrock, and Katharina Reinecke
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
RealVuln benchmark finds security-specialized scanners outperform general-purpose LLMs and rule-based SAST tools on hand-labeled vulnerable Python code under F3 scoring, with all artifacts released.
Co-design with five BLV experts produced a multi-modal prototype using reference sonification, stereo/volumetric audio, and buffer aggregation that co-designers said improved accuracy and learnability on tasks like peak finding and gradient tracing.
XL-HD uses fixed Sobol projections and real-to-binary prototype optimization to deliver competitive accuracy on MNIST, UCIHAR, and ISOLET with a 0.395 mm² IMC engine at 0.40 μJ per inference.
citing papers explorer
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The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE
Software engineering scope expands beyond executable code to semi-executable artifacts best diagnosed by the new six-ring Semi-Executable Stack model.
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RealVuln: Benchmarking Rule-Based, General-Purpose LLM, and Security-Specialized Scanners on Real-World Code
RealVuln benchmark finds security-specialized scanners outperform general-purpose LLMs and rule-based SAST tools on hand-labeled vulnerable Python code under F3 scoring, with all artifacts released.
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Three Modalities, Two Design Probes, One Prototype, and No Vision: Experience-Based Co-Design of a Multi-modal 3D Data Visualization Tool
Co-design with five BLV experts produced a multi-modal prototype using reference sonification, stereo/volumetric audio, and buffer aggregation that co-designers said improved accuracy and learnability on tasks like peak finding and gradient tracing.
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XL-HD: Extended Learning in Hyperdimensional Computing via Deterministic Projections for In-Memory Accelerators
XL-HD uses fixed Sobol projections and real-to-binary prototype optimization to deliver competitive accuracy on MNIST, UCIHAR, and ISOLET with a 0.395 mm² IMC engine at 0.40 μJ per inference.