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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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.SE 5years
2026 5representative citing papers
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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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.