ConfLogger: Config Diagnosability Tool

• ConfLogger is a configuration diagnosability tool that integrates static taint analysis with LLM-based log generation to reveal critical configuration contexts.
• It systematically identifies configuration-sensitive code segments and injects diagnostic logs to expose parameter values and interdependencies.
• Empirical evaluations demonstrate improved log injection coverage and faster fault localization, significantly enhancing troubleshooting efficiency.

ConfLogger is a configuration diagnosability enhancement tool for modern, highly-configurable software systems. By unifying configuration-aware static taint analysis with LLM-based log generation, ConfLogger systematically identifies configuration-sensitive code segments and infuses them with diagnostic log statements that directly expose key configuration contexts. This enables proactive detection and localization of configuration-related errors—including silent misconfigurations where failures manifest without clear log clues—before, during, and after software incidents.

1. Motivation and Design Principles

Complex configurable software ecosystems (including distributed platforms and decentralized Web 3.0 systems) offer broad customization via rich configuration spaces, leading to frequent configuration-related failures. Traditional diagnostic approaches rely on post-mortem behavioral analysis and are limited by the absence of explicit configuration traces and lack of actionable diagnostic messages. ConfLogger is designed to close these gaps. Its central principle is "configuration logging": at the source-code level, logs should systematically surface configuration parameter identifiers, real-time values, interdependencies, and context needed for accurate diagnosis before fatal failures or silent faults occur.

ConfLogger explicitly addresses two critical configuration deficiencies observed in the field: (a) silent failures—misconfigurations that are not logged or only weakly signaled—and (b) insufficient diagnostic messages—where existing logs lack precise configuration parameter contexts. The tool aims to "expose" actionable information in logs suitable for direct troubleshooting.

2. Methodology: Configuration-Sensitive Identification and LLM-Based Log Generation

ConfLogger operates through two tightly integrated components:

A. Configuration-Sensitive Code Identification

• Automatic Labeling: Systematically labels configuration engine classes using the official configuration documentation. This process maps configuration parameter keys to source code identifiers.
• Configuration-Aware Static Taint Analysis: Traces propagation of tainted configuration variables across the codebase, detecting their flow from declaration and getter calls to logic handling and decision points. This analysis is executed using a Program Dependence Graph (PDG) built from a Static Single Assignment (SSA) intermediate representation.
• Formal Definition: The methodology is formalized as:

$$c_{e} = c_{p} \cup \{s_{log_1}, \ldots, s_{log_{q}}\}$$,

where $c_{p}$ is the set of configuration-sensitive segments and $s_{log_{i}}$ are the new injected logging statements.

• Source Filtering Rules: Getter methods qualify as sources based on the engine type—Both-Holder, Key-Holder, Dict-Holder—ensuring only configuration-relevant flows are tracked.

B. LLM-Based Logging Statement Generation

• Contextual Analysis: Each identified configuration-sensitive block is submitted to an LLM, guided by a Chain-of-Thought reasoning framework. The LLM processes both code-specific features (e.g., tags like <code-specified>, <code-whole>, <param>) and context boundaries.
• Log Statement Generation: Decisions are made on the necessity, placement, and content of logs. Logging is strategically injected at branch points handling invalid/unset values, parameter changes, or other configuration events.
• Diagnostic Enrichment: Generated logs conform to standards such as SLF4J and include parameter names, values, constraints, and recommended resolutions, directly supporting misconfiguration diagnosis.
• Automation and Precision: The LLM component enables context-aware, non-redundant log generation, mitigating manual instrumentation limitations.

3. Experimental Evaluation

ConfLogger was empirically evaluated on eight widely-used Java systems, including Storm, HBase, Alluxio, Hadoop Common, MapReduce, Yarn, HDFS, and ZooKeeper. Evaluation metrics and results are as follows:

Scenario/Metric	ConfLogger	Baseline 1 (UniLog)	Baseline 2 (SCLogger)
Silent misconfiguration diagnosis accuracy	100%	–	–
Log injection coverage	74%	66%	57%
Precision (variable logging)	+8.6%	–	–
Recall (variable logging)	+79.3%	–	–
F1 score (variable logging)	+26.2%	–	–

• In a set of 30 silent misconfiguration cases, ConfLogger enabled the log-based diagnosis tool to reach 100% localization accuracy, with 80% of cases directly resolvable via explicit configuration information exposed in the logs.
• Coverage of existing logging points was 74%, outperforming UniLog (62%) and SCLogger (57%) by 12–30%.
• Precision, recall, and F1 scores in variable logging improved by 8.6%, 79.3%, and 26.2% respectively. These gains show ConfLogger not only logs more targets but does so diagnostically, increasing the likelihood of actionable troubleshooting.
• All metrics are contextualized as direct improvements in both form and usefulness of the log output.

4. Comparative Analysis with Existing Logging Tools

ConfLogger’s capabilities are contrasted against other automated logging tools:

• Coverage and Quality: Outperforms state-of-the-art baselines by measurable margins in log injection, precision, recall, and F1.
• Configuration Sensitivity: Baselines lack configuration-aware elements and rely on general heuristics, whereas ConfLogger explicitly couples static analysis and LLM log synthesis to focus on configuration-relevant contexts.
• Actionable Logging: ConfLogger’s logs contain configuration parameter names, constraints, runtime values, and suggestions, while existing loggers typically omit these, reducing their utility in pinpointing configuration issues.

ConfLogger systematically demonstrates that context-focused, configuration-sensitive logging approaches yield substantially higher diagnostic value.

5. User Study: Diagnostic Effectiveness

A controlled user paper involving 22 diagnostic scenarios was conducted:

• Study Design: Compared documentation-assisted participants (DA group) versus log-assisted participants (LA group using ConfLogger logs).
• Diagnostic Time: LA group solved diagnoses 1.25× faster (18.68 min versus 23.36 min).
• Accuracy Scores: LA group’s scores improved by 251.4% (from 4.38 to 15.38 on the paper rubric).
• Expertise Bridging: Even developers with only 0–1 years of experience saw substantial benefits, suggesting ConfLogger narrows the gap in diagnostic capability attributed to expertise.

These results validate ConfLogger’s enhancement of troubleshooting—both accelerating the process and raising accuracy—especially among less experienced developers.

6. Integration with Fault Localization and Related Methodologies

The ConfLogger approach complements and extends prior work on configuration-dependent fault localization (notably CoFL (Nguyen, 2019)). CoFL’s methodologies—particularly the Suspicious Partial Configuration (SPC) identification and dependency analysis—suggest further routes for ConfLogger’s evolution:

• Targeted Logging Using SPC: Logs can be further focused via SPC to minimize diagnostic noise and centralize attention on interaction-relevant features.
• Program Dependency Tracing: Adopting program dependency graph techniques would enable richer tracing of configuration-induced propagation paths, expanding contextual depth in logs.
• Autonomous Alerting and Ranking: Integrating spectrum-based suspiciousness ranking for configuration errors could lead to prioritized, actionable alerts and further reduce cognitive load during triage and debug.
• Reduced Debug Search Space: Building on CoFL’s domain narrowing techniques, ConfLogger can guide engineers to critical code regions, improving efficiency in high-dimensional configuration spaces.

These methodological synergies offer plausible future extensions and refinements to ConfLogger’s core framework.

7. Limitations and Future Directions

Several avenues for future research and enhancements are identified:

• Language Portability: Expanding static taint analysis and log generation mechanisms to programming languages beyond Java.
• Taint Analysis Optimization: Reduction of false positives in variable tracking via refined heuristic or semantic rules.
• LLM Prompt Engineering: Further mitigation of LLM hallucination risks via improved prompt design and context encoding.
• Automated Testing Tool Integration: Closer coupling with automated configuration testing workflows for full lifecycle support.
• Scalability: Addressing scalability challenges as configuration spaces grow, ensuring computational efficiency and enriching automation.

A plausible implication is that future ConfLogger iterations could deliver live, configuration-sensitive alerts and logging in real-time, supporting dynamic misconfiguration detection and facilitating robust, resilient configuration management at scale.

Summary

ConfLogger is the first tool to operationalize “configuration logging” for diagnosability in modern software systems by merging configuration-aware static analysis and context-enriched automated logging generation. Its empirical performance—marked by superior variable logging metrics, log coverage, and diagnostic accuracy—positions it as the state of the art for configuration-related issue detection and resolution. ConfLogger’s design, methodology, evaluation, and integration prospects directly address the unique challenges of misconfiguration in highly-customizable, large-scale software infrastructure (Shan et al., 28 Aug 2025, Nguyen, 2019).