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HINNPerf: Hierarchical Interaction Neural Network for Performance Prediction of Configurable Systems

https://doi.org/10.1145/3528100
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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IPOG/IPOG‐D: efficient test generation for multi‐way combinatorial testing
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Nonparametric Statistical Inference
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Runtime recovery and manipulation of software architecture of component-based systems
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Data-efficient performance learning for configurable systems
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Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
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DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function
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Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
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Variability-aware performance prediction: A statistical learning approach
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Scalable product line configuration: A straw to break the camel's back
inferredcites →https://doi.org/10.1109/ase.2015.15openalex-snapshot
Performance Prediction of Configurable Software Systems by Fourier Learning (T)
inferredcites →https://doi.org/10.1109/ase.2019.00065openalex-snapshot
Accurate Modeling of Performance Histories for Evolving Software Systems
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.00017openalex-snapshot
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
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HDLTex: Hierarchical Deep Learning for Text Classification
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Video2vec: Learning semantic spatio-temporal embeddings for video representation
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On the value of user preferences in search-based software engineering: A case study in software product lines
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Distance-Based Sampling of Software Configuration Spaces
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DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network
inferredcites →https://doi.org/10.1109/icsme.2019.00080openalex-snapshot
Performance-Influence Model for Highly Configurable Software with Fourier Learning and Lasso Regression
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ITEM2VEC: Neural item embedding for collaborative filtering
inferredcites →https://doi.org/10.1109/splc.2011.20openalex-snapshot
Scalable Prediction of Non-functional Properties in Software Product Lines
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QoS Ranking Prediction for Cloud Services
inferredcites →https://doi.org/10.1109/tsmc.2013.2297401openalex-snapshot
An Online Performance Prediction Framework for Service-Oriented Systems
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Regression Shrinkage and Selection Via the Lasso
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An algorithm for generating t-wise covering arrays from large feature models
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Practical pairwise testing for software product lines
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Family-based performance measurement
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Performance-influence models for highly configurable systems
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Hey, you have given me too many knobs!: understanding and dealing with over-designed configuration in system software
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A detailed and real-time performance monitoring framework for blockchain systems
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Mastering uncertainty in performance estimations of configurable software systems
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Learning Deep Architectures for AI
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Scikit-learn: Machine Learning in Python
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Inverting face embeddings with convolutional neural networks
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Why Deep Neural Networks for Function Approximation?
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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
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Predicting Performance of Software Configurations: There is no Silver Bullet
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A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions
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A neural probabilistic language model

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-15981-6_1openalex-snapshot
Interpretable Configuration Optimization for Static Program Verification via Rule-Based and Counterfactual Reasoning
inferred← citeshttps://doi.org/10.1007/s10515-026-00654-5openalex-snapshot
Towards accurate and efficient prediction of unreal engine rendering performance on in-vehicle HMI platforms
inferred← citeshttps://doi.org/10.1007/s10664-022-10250-2openalex-snapshot
Mastering uncertainty in performance estimations of configurable software systems
inferred← citeshttps://doi.org/10.1109/csecs64665.2025.11009759openalex-snapshot
Predicting Execution Time for Move Method Refactoring Base on Deep Learning
inferred← citeshttps://doi.org/10.1109/icse-companion66252.2025.00027openalex-snapshot
Towards Realistic, Applicable and Feasible Configuration-Aware Performance Modeling
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00176openalex-snapshot
Analysing the Impact of Workloads on Modeling the Performance of Configurable Software Systems
inferred← citeshttps://doi.org/10.1109/qrs-c60940.2023.00028openalex-snapshot
Performance Prediction for Lock-based Programs
inferred← citeshttps://doi.org/10.1109/tse.2024.3491945openalex-snapshot
Dividable Configuration Performance Learning
inferred← citeshttps://doi.org/10.1109/tse.2025.3525955openalex-snapshot
Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning
inferred← citeshttps://doi.org/10.1145/3691620.3695490openalex-snapshot
Blackbox Observability of Features and Feature Interactions
inferred← citeshttps://doi.org/10.1145/3702986openalex-snapshot
Deep Configuration Performance Learning: A Systematic Survey and Taxonomy
inferred← citeshttps://doi.org/10.1145/3728954openalex-snapshot
Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective
inferred← citeshttps://doi.org/10.1145/3744916.3773131openalex-snapshot
Bayesian Multi-Level Performance Models for Multi-Factor Variability of Configurable Software Systems
inferred← citeshttps://doi.org/10.1145/3837763openalex-snapshot
HQP: Hybrid Queueing and Configuration-Aware Performance Modeling for Microservice Systems
inferred← citeshttps://doi.org/10.3390/app15084270openalex-snapshot
Predicting a Program’s Execution Time After Move Method Refactoring Based on Deep Learning and Feature Interaction
inferred← citeshttps://doi.org/10.3390/computers15040238openalex-snapshot
Design Behaviour and Interface Consistency in Generative No-Code Tools: A Systematic Literature Review
inferred← citeshttps://doi.org/10.5753/jserd.2026.6399openalex-snapshot
On the Effectiveness of Feedforward Neural Networks for Performance Prediction in Highly Configurable Systems: The Case of Linux Kernel Size
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