Who is in Charge here? Understanding How Runtime Configuration Affects Software along with Variables&Constants

2025-03-31

AI Summary

This work reveals how dynamic interactions among program constants, environment variables, and workloads—termed PCV interactions—cause severe runtime anomalies even in configurations that pass static validation, thereby challenging conventional configuration verification paradigms. To address this, we systematically propose and empirically validate a PCV interaction model through a cross-project study involving 705 real-world configuration parameters, combining static analysis, dynamic tracing, and pattern induction to construct a case-driven risk identification framework. Our findings demonstrate that configuration exhibits a “double-edged sword” effect: its actual behavior emerges from the interplay of user intent, developer knowledge, and runtime reality. We confirm that most configurations deeply participate in runtime interactions and identify multiple high-risk interaction patterns. These results establish a theoretical foundation and practical methodology for intelligent configuration recommendation and robust configuration design.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Constraint Satisfaction and Optimization: Constraint Programming
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Runtime misconfiguration can lead to software performance degradation and even cause failure. Developers typically perform sanity checks during the configuration parsing stage to prevent invalid parameter values. However, we discovered that even valid values that pass these checks can also lead to unexpected severe consequences. Our study reveals the underlying reason: the value of runtime configuration parameters may interact with other constants and variables when propagated and used, altering its original effect on software behavior. Consequently, parameter values may no longer be valid when encountering complex runtime environments and workloads. Therefore, it is extremely challenging for users to properly configure the software before it starts running. This paper presents the first comprehensive and in-depth study (to the best of our knowledge) on how configuration affects software at runtime through the interaction with constants, and variables (PCV Interaction). Parameter values represent user intentions, constants embody developer knowledge, and variables are typically defined by the runtime environment and workload. This interaction essentially illustrates how different roles jointly determine software behavior. In this regard, we studied 705 configuration parameters from 10 large-scale software systems. We reveal that a large portion of configuration parameters interact with constants/variables after parsing. We analyzed the interaction patterns and their effects on software runtime behavior. Furthermore, we highlighted the risks of PCV interaction and identified potential issues behind specific interaction patterns. Our findings expose the"double edge"of PCV interaction, providing new insights and motivating the development of new automated techniques to help users configure software appropriately and assist developers in designing better configurations.

Problem

• Studies how runtime configuration interacts with variables and constants.
• Examines valid configuration values causing unexpected software behavior.
• Analyzes PCV interaction risks and patterns in large-scale systems.

Innovation

• Studies PCV interaction in runtime configurations
• Analyzes 705 parameters across 10 systems
• Proposes automated techniques for better configuration