The authors derive the first bit-accurate arithmetic models for matrix multiply-accumulate operations on ten GPU architectures spanning NVIDIA Volta to Blackwell and AMD CDNA1 to CDNA3.
In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, Pennsylvania) (ICSE ’22)
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EvoRepair is the first experience-based self-evolving agent framework for automated vulnerability repair, reporting 90.46% overall success on PATCHEVAL and SEC-bench benchmarks.
LLMs achieve strong results on syntax parsing tasks but show limited and variable performance on dynamic reasoning, with a clear performance hierarchy across model scales.
QTyBERT matches or exceeds BERT-based log anomaly detection effectiveness while reducing embedding generation time to near static word embedding levels.
Causal Software Engineering is proposed as a paradigm that applies causal models and counterfactual reasoning to inform high-stakes decisions throughout software development and operations.
MultiVul uses multimodal contrastive learning to align code and comment representations, yielding up to 27% F1 gains on vulnerability detection benchmarks over prompting and code-only baselines.
EnergyTrackr detects statistically significant energy regressions in Java commits from 3,232 changes across three projects and identifies recurring code anti-patterns such as missing early exits.
STAF applies sentence embeddings from transformers to classify SCA findings, reaching 89% F1 and beating prior filters by 11% within projects and 6% across projects.
citing papers explorer
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Bit-Accurate Modeling of GPU Matrix Multiply-Accumulate Units: Demystifying Numerical Discrepancy and Accuracy
The authors derive the first bit-accurate arithmetic models for matrix multiply-accumulate operations on ten GPU architectures spanning NVIDIA Volta to Blackwell and AMD CDNA1 to CDNA3.
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EvoRepair: Enhancing Vulnerability Repair Agents Through Experience-Based Self-Evolution
EvoRepair is the first experience-based self-evolving agent framework for automated vulnerability repair, reporting 90.46% overall success on PATCHEVAL and SEC-bench benchmarks.
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Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs
LLMs achieve strong results on syntax parsing tasks but show limited and variable performance on dynamic reasoning, with a clear performance hierarchy across model scales.
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A Comparative Study of Semantic Log Representations for Software Log-based Anomaly Detection
QTyBERT matches or exceeds BERT-based log anomaly detection effectiveness while reducing embedding generation time to near static word embedding levels.
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Causal Software Engineering: A Vision and Roadmap
Causal Software Engineering is proposed as a paradigm that applies causal models and counterfactual reasoning to inform high-stakes decisions throughout software development and operations.
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Learning Generalizable Multimodal Representations for Software Vulnerability Detection
MultiVul uses multimodal contrastive learning to align code and comment representations, yielding up to 27% F1 gains on vulnerability detection benchmarks over prompting and code-only baselines.
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Systematic Detection of Energy Regression and Corresponding Code Patterns in Java Projects
EnergyTrackr detects statistically significant energy regressions in Java commits from 3,232 changes across three projects and identifies recurring code anti-patterns such as missing early exits.
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Towards Better Static Code Analysis Reports: Sentence Transformer-based Filtering of Non-Actionable Alerts
STAF applies sentence embeddings from transformers to classify SCA findings, reaching 89% F1 and beating prior filters by 11% within projects and 6% across projects.