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

Chaopeng Luo, Yuanliang Zhang, Haochen He, Zhouyang Jia, Teng Wang, Shulin Zhou, Si Zheng, Shanshan Li

TL;DR

This paper addresses the gap in understanding how runtime configuration values interact with constants and variables to influence software behavior, beyond basic parsing checks. It conducts a source-code–level empirical study across 10 large-scale systems, tracing 705 parameters (from 3523 defaults) and uncovering 851 interaction cases, with 66.4% of parameters engaging in PCV interactions. The authors categorize seven interaction patterns, quantify their effects on performance ($38.5\%$) and reliability ($41.7\%$), and identify risks such as misconfiguration, poor logging, and limited on-the-fly updates, offering actionable directions for both users and developers. Overall, the work introduces a new PCV Interaction lens to analyze how user intent, developer knowledge, and runtime environment jointly drive software behavior, and it releases a dataset to spur automated tooling for safer and more adaptive configuration management.

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.

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

TL;DR

This paper addresses the gap in understanding how runtime configuration values interact with constants and variables to influence software behavior, beyond basic parsing checks. It conducts a source-code–level empirical study across 10 large-scale systems, tracing 705 parameters (from 3523 defaults) and uncovering 851 interaction cases, with 66.4% of parameters engaging in PCV interactions. The authors categorize seven interaction patterns, quantify their effects on performance () and reliability (), and identify risks such as misconfiguration, poor logging, and limited on-the-fly updates, offering actionable directions for both users and developers. Overall, the work introduces a new PCV Interaction lens to analyze how user intent, developer knowledge, and runtime environment jointly drive software behavior, and it releases a dataset to spur automated tooling for safer and more adaptive configuration management.

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.

Paper Structure

This paper contains 39 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. study methodology
  3. Selecting Software and Configuration Parameters
  4. Target software
  5. Configuration parameters
  6. Taint Analysis of Configuration Parameters
  7. Analyzing Interaction Cases. (RQ1)
  8. PCV Interaction Pattern (RQ2)
  9. Parameter works independently (P)
  10. Parameter interacts with Constant (P&C)
  11. Parameter is constrained by Constant (C$\rightarrow$P)
  12. Parameter and Constant take equal place (P$-$C)
  13. Parameters interacts with Variables (P&V)
  14. Parameter is constrained by Variable(V$\rightarrow$P)
  15. Parameter restrains Variable (P$\rightarrow$V)
  16. ...and 24 more sections

Figures (5)

  • Figure 1: An example of a valid parameter value causes software crash during interaction.
  • Figure 2: Examples of PCV interaction patterns. Parameters, variables, and constants are marked in orange, green, and blue respectively, and the interactions code are highlighted in gray.
  • Figure 3: PCV interaction: Patterns, Effects, and Problems (the colors represent the proportion).
  • Figure 4: Example of configuration adjustment at runtime based on historical data.
  • Figure 5: Example of elastic threshold.