A compositional algebraic decision diagram algorithm quantifies sensitivity in decision tree ensembles with certified error and confidence bounds, outperforming model counters on benchmarks.
In: Proceedings of the 2017 11th Joint Meeting on Foundations of Soft- ware Engineering
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CodeQL detected 171 CVEs total, with 83 caught by a prior version before the fix; detections were often actionable within the vulnerable file but not stable across tool versions.
Ethics testing is introduced as a systematic approach to generate tests that identify software harms induced by unethical behavior in generative AI outputs.
Ground-truth evaluation of eight debloaters shows dynamic tools remove up to 94% of code that should be kept and static tools show high false retention from over-approximation.
CIR is a cross-platform container image format for Python/R-style apps that defers dependency assembly to deployment, cutting image size by 95% and deployment time by 40-60% versus traditional bundled images.
FlaXifyer applies few-shot learning on pre-trained language models to categorize intermittent CI job failures from logs at 84.3% Macro F1 and 92.0% Top-2 accuracy using 12 examples per category, with LogSift reducing log review effort by 74.4%.
Proposes the CBDT framework as a minimum viable digital twin for CI builds to enable real-time monitoring, ML modeling, and prescriptive optimization of build duration, failures, and flakiness.
citing papers explorer
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Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach
A compositional algebraic decision diagram algorithm quantifies sensitivity in decision tree ensembles with certified error and confidence bounds, outperforming model counters on benchmarks.
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Longitudinal Analyses of SAST Tools: A CodeQL Case Study
CodeQL detected 171 CVEs total, with 83 caught by a prior version before the fix; detections were often actionable within the vulnerable file but not stable across tool versions.
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Ethics Testing: Proactive Identification of Generative AI System Harms
Ethics testing is introduced as a systematic approach to generate tests that identify software harms induced by unethical behavior in generative AI outputs.
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Revisiting Code Debloating with Ground Truth-based Evaluation
Ground-truth evaluation of eight debloaters shows dynamic tools remove up to 94% of code that should be kept and static tools show high false retention from over-approximation.
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CIR: Lightweight Container Image for Cross-Platform Deployment
CIR is a cross-platform container image format for Python/R-style apps that defers dependency assembly to deployment, cutting image size by 95% and deployment time by 40-60% versus traditional bundled images.
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Predicting Intermittent Job Failure Categories for Diagnosis Using Few-Shot Fine-Tuned Language Models
FlaXifyer applies few-shot learning on pre-trained language models to categorize intermittent CI job failures from logs at 84.3% Macro F1 and 92.0% Top-2 accuracy using 12 examples per category, with LogSift reducing log review effort by 74.4%.
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Towards Build Optimization Using Digital Twins
Proposes the CBDT framework as a minimum viable digital twin for CI builds to enable real-time monitoring, ML modeling, and prescriptive optimization of build duration, failures, and flakiness.