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HyperSeq: A Hyper-Adaptive Representation for Predictive Sequencing of States

Roham Koohestani, Maliheh Izadi

TL;DR

HyperSeq addresses the environmental and computational costs of AI-assisted IDEs by modeling developers' cognitive states with Hyper-Dimensional Computing to enable predictive sequencing of actions. It introduces a two-part architecture: a base encoding $M_{base}$ and an adaptive component $M_{adap}$ that supports online learning, allowing personalization and continual improvement with $O(D \cdot |Train|)$ training and $O(D)$ per-update costs, respectively. Empirical results show average accuracies in the $40 ext{-}50\%$ range, with several configurations exceeding $70\%$, and clear improvements when adaptation is enabled, indicating strong potential for proactive, state-aware IDE features while maintaining efficiency. The work lays groundwork for deploying personalized, energy-efficient predictive tooling in software development, with future directions including telemetry integration, alternate vector-symbolic architectures, and real-world deployments to further optimize developer productivity and sustainability.

Abstract

In the rapidly evolving world of software development, the surge in developers' reliance on AI-driven tools has transformed Integrated Development Environments into powerhouses of advanced features. This transformation, while boosting developers' productivity to unprecedented levels, comes with a catch: increased hardware demands for software development. Moreover, the significant economic and environmental toll of using these sophisticated models necessitates mechanisms that reduce unnecessary computational burdens. We propose HyperSeq - Hyper-Adaptive Representation for Predictive Sequencing of States - a novel, resource-efficient approach designed to model developers' cognitive states. HyperSeq facilitates precise action sequencing and enables real-time learning of user behavior. Our preliminary results show how HyperSeq excels in forecasting action sequences and achieves remarkable prediction accuracies that go beyond 70%. Notably, the model's online-learning capability allows it to substantially enhance its predictive accuracy in a majority of cases and increases its capability in forecasting next user actions with sufficient iterations for adaptation. Ultimately, our objective is to harness these predictions to refine and elevate the user experience dynamically within the IDE.

HyperSeq: A Hyper-Adaptive Representation for Predictive Sequencing of States

TL;DR

HyperSeq addresses the environmental and computational costs of AI-assisted IDEs by modeling developers' cognitive states with Hyper-Dimensional Computing to enable predictive sequencing of actions. It introduces a two-part architecture: a base encoding and an adaptive component that supports online learning, allowing personalization and continual improvement with training and per-update costs, respectively. Empirical results show average accuracies in the range, with several configurations exceeding , and clear improvements when adaptation is enabled, indicating strong potential for proactive, state-aware IDE features while maintaining efficiency. The work lays groundwork for deploying personalized, energy-efficient predictive tooling in software development, with future directions including telemetry integration, alternate vector-symbolic architectures, and real-world deployments to further optimize developer productivity and sustainability.

Abstract

In the rapidly evolving world of software development, the surge in developers' reliance on AI-driven tools has transformed Integrated Development Environments into powerhouses of advanced features. This transformation, while boosting developers' productivity to unprecedented levels, comes with a catch: increased hardware demands for software development. Moreover, the significant economic and environmental toll of using these sophisticated models necessitates mechanisms that reduce unnecessary computational burdens. We propose HyperSeq - Hyper-Adaptive Representation for Predictive Sequencing of States - a novel, resource-efficient approach designed to model developers' cognitive states. HyperSeq facilitates precise action sequencing and enables real-time learning of user behavior. Our preliminary results show how HyperSeq excels in forecasting action sequences and achieves remarkable prediction accuracies that go beyond 70%. Notably, the model's online-learning capability allows it to substantially enhance its predictive accuracy in a majority of cases and increases its capability in forecasting next user actions with sufficient iterations for adaptation. Ultimately, our objective is to harness these predictions to refine and elevate the user experience dynamically within the IDE.

Paper Structure

This paper contains 37 sections, 1 theorem, 18 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Motivation
  3. Background Literature
  4. Hyper-dimensional Computing
  5. User-IDE interaction
  6. Next-action prediction
  7. Problem Definition
  8. Predictive Sequencing
  9. Hyper-Dimensional Computing (HDC)
  10. HyperSeq
  11. Representation
  12. Adaptiveness
  13. Evaluation Methodology
  14. Datasets
  15. Data Partitioning Strategies
  16. ...and 22 more sections

Key Result

theorem 1

Suppose we have a session of length $m$ and we wish to form every $n$-gram $G_i$ for $i=0,1,\dots,m-n$. Once $G_0$ is computed and its cost amortized, $G_{i+1}$ can be computed from $G_i$ in constant time. Consequently, forming all $n$-grams in the session costs $\mathcal{O}(m)$ (excluding operation

Figures (2)

  • Figure 1: Overall accuracies of the model across the different splitting strategies and with adaptive on or off.
  • Figure 2: Best-performing model's accuracy; Sliding window size = 30, Dimension = 20,000, Sequence Length = 3, Cyclic Shift = 4, Adaptive = True, and Split Strategy = Disjoint.

Theorems & Definitions (1)

  • theorem 1