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log-based-testing.md

Enhancing Software Verification: Leveraging Business-Specific Signals in Application Logs for Decoupled Testing

Executive Summary

Traditional software testing methodologies, particularly state-based and collaboration-based verification, frequently encounter a significant challenge: tight coupling between tests and the underlying implementation details. This coupling leads to brittle tests that require extensive maintenance during refactoring, undermining development efficiency and confidence in the codebase. This report explores an alternative paradigm: leveraging business-specific signals embedded within application logs for test verification.

While logs have historically been relegated to debugging and operational monitoring, this analysis demonstrates their potential as a robust, decoupled mechanism for behavioral testing. The report investigates the reasons for the historical underutilization of logs in testing, primarily identifying technical hurdles such as data volume, noise, and parsing complexity, alongside perceptual barriers that view logs solely as diagnostic tools.

However, advancements in observability-driven development (ODD), event stream processing, and AI-powered log analysis are transforming this landscape. Modern approaches advocate for strategic log instrumentation, treating certain log entries as first-class outputs of business logic. Real-world examples, notably Klarna's snabbkaffe framework, illustrate the successful application of trace-based testing, where structured log events serve as verifiable behavioral assertions.

The report concludes with actionable recommendations for organizations considering this approach. These include designing logs for testability, integrating log verification into automated pipelines with custom assertions, and adopting distributed tracing practices. By embracing these methodologies, teams can achieve more flexible, behavior-centric testing strategies that significantly reduce coupling, enhance maintainability, and improve the overall quality of complex, distributed software systems.

1. Introduction: The Evolving Landscape of Software Testing and Coupling Challenges

The efficacy of software testing is paramount to delivering reliable and maintainable systems. However, as software architectures grow in complexity, particularly with the proliferation of microservices and distributed systems, traditional testing paradigms reveal inherent limitations, most notably the issue of tight coupling to implementation details. This section delineates conventional verification approaches and examines the pervasive problem of test-implementation coupling.

1.1. Traditional Verification: State vs. Collaboration Testing

Software testing commonly employs two primary verification strategies: state-based testing and collaboration-based testing. Each approach serves a distinct purpose in validating software correctness.

State-based testing focuses on asserting the final state of an object or system after an operation has been performed.1 This involves checking that the system's internal data or its external outputs (e.g., return values) match predefined expectations.3 For instance, a test might add an item to a shopping cart and then verify that the cart's total value or the number of items has been updated correctly. This method primarily concerns itself with the

outcome or result of an action.1

Conversely, collaboration-based testing, often referred to as interaction testing, verifies that an object under test correctly interacts with its dependencies, known as collaborators.1 This typically involves using test doubles like mock objects, which meticulously record method calls and assert whether expected interactions, including the sequence and arguments of these calls, took place.1 An example would be testing an order processing service to ensure it correctly invokes a

PaymentGateway's processPayment() method with the appropriate transaction details. This approach scrutinizes how components communicate and collaborate.1

The fundamental difference in focus between these two paradigms is critical. State-based testing emphasizes the end result, while collaboration-based testing focuses on the intermediate interactions. The user's inquiry implicitly seeks a behavior-based verification method that avoids the pitfalls of both state-based (which can be brittle due to internal state changes) and traditional interaction-based testing (which can be brittle due to specific method call changes). Logs, when designed as business signals, could offer a more abstract behavioral verification.

1.2. The Problem of Test-Implementation Coupling and its Impact

A significant challenge in software development is the tight coupling of tests to the implementation details of the code they are designed to verify. Coupling, in software engineering, refers to the degree of interdependence between software modules.4 When modules are highly coupled, changes in one can significantly affect others, making the system difficult to modify and test.4 This principle extends directly to the relationship between production code and its test suite.

When tests are tightly coupled to implementation details—such as internal methods, specific data structures, or the exact sequence of interactions—they become brittle.1 This brittleness manifests as tests breaking frequently even when the external behavior of the application remains unchanged, simply because internal code has been refactored or optimized.7 For example, if a test relies on asserting the precise order of internal method calls (a form of collaboration verification), and a developer refactors these internal calls to improve efficiency without altering the overall business outcome, the test will fail. This necessitates modifying the test code, often leading to more test code changes than production code changes.8 This phenomenon is commonly referred to as the "refactoring tax," which slows down development, discourages necessary code improvements, and can undermine confidence in the codebase.5

The causes of this coupling are varied. State-based tests can inadvertently couple to implementation details if the internal state being asserted is not a stable, externally observable behavior but rather an internal data representation prone to change.3 Similarly, collaboration-based testing, particularly with mocks, can lead to brittle tests if the mocks are overly specific or tightly bound to the mock implementation details, breaking with minor code or data configuration changes.1 Mocking concrete classes instead of interfaces can exacerbate this issue, as the mock might inadvertently execute parts of the real implementation during testing.2 The user's inquiry about using logs for testing is a direct response to this "refactoring tax," seeking a verification mechanism that is less sensitive to internal structural changes, thereby allowing developers to refactor without constantly rewriting tests.

The table below provides a comparative overview of state-based, behavior/interaction-based, and the proposed log-based testing, highlighting their primary purposes, focus, typical tools, and inherent coupling risks.

Aspect State-Based Testing Behavior/Interaction-Based Testing Log-Based (Proposed)
Primary Purpose Mimic behavior of components with predetermined responses 1 Record and validate interactions between components 1 Verify business-specific signals emitted by the application
Focus Outcome and behavior of object under test 1 How components collaborate 1 Observable business events/signals
Typical Tools/Techniques Direct assertion on object state, return values 2 Mocks, Spies 1 Log analysis tools, custom assertions on log streams
Coupling Risk to Impl. Can be coupled if asserting on internal state 3 High risk if asserting on specific method calls/order 1 Low coupling if logs represent abstract business events 9
Refactoring Impact Changes to internal data structures/algorithms may break tests 3 Changes to internal interaction patterns break tests 5 Robust to internal refactoring as long as business signals are consistent
Example Assertion assert_equal(result, expected_value) mock_service.expect_call(method, args) assert_log_contains("order_processed", correlation_id)

1.3. The "Test the Behavior, Not the Implementation" Principle in Context

The principle of "test the behavior, not the implementation" is a cornerstone of robust software testing. This widely accepted guideline advocates for validating the observable outcomes and behaviors of a system, rather than its internal workings, specific algorithms, or architectural choices.7 The fundamental goal is to ensure that tests remain stable and reliable even when the underlying code is refactored, optimized, or rewritten, provided that the external, verifiable behavior of the system remains consistent.12 Tests should primarily confirm what the code is intended to

do, provide rapid and accurate feedback, and ultimately facilitate easier maintenance throughout the software lifecycle.7

A common misconception arises when pursuing high test coverage percentages. While a high code coverage is often associated with superior code quality, the measurement itself does not inherently determine quality.13 It is possible for a codebase to have numerous tests and high coverage, yet still be genuinely untested, making refactoring or feature addition difficult.7 This situation arises when tests are tightly coupled to implementation details, leading to a false sense of security regarding the code's reliability.7 The actual quality of the code is not solely dependent on the quantity of tests, but rather on

what is being tested. Testing implementation details, rather than the observable behavior, can undermine confidence and negate the benefits of testing if tests must be constantly rewritten with every internal code change.7 This highlights that

what is being tested (implementation details versus behavior) is more important than how much is being tested. The user's search for log-based testing aligns with a deeper understanding of quality, moving beyond superficial metrics like code coverage towards tests that genuinely validate behavior and support maintainability. It implies a need for tests that are robust to internal changes, focusing on the external contract or observable business signals.

2. Logs as Business-Specific Signals: A Paradigm Shift in Verification

Moving beyond their conventional role in debugging, application logs possess significant untapped potential as a primary source for test verification. This section explores the broader concept of observability, the specific nature of business-specific signals within logs, and the potential benefits of this paradigm shift for decoupling tests from implementation.

2.1. Understanding Observability: Logs, Metrics, and Traces

Observability is defined as the ability to infer the internal state of a system by examining the data it outputs.14 This capability is increasingly crucial for modern applications, which often feature complex, distributed architectures with numerous interconnected components.14 Unlike traditional monitoring, which typically reacts to predefined conditions and known metrics, observability adopts a proactive stance, providing continuous, real-time insights throughout the system's lifecycle, from development to production.14 This allows for deeper investigation into unexpected behaviors and facilitates robust root cause analysis.16

The concept of observability is built upon three fundamental pillars:

  • Logs: These capture detailed events and actions within a system, offering a granular, chronological view of what transpires during execution.14 Logs are records of events, actions, and errors.19 They are invaluable for pinpointing where failures occur and providing the necessary context for effective debugging.14
  • Metrics: These are quantitative measurements that track system performance, resource utilization, and overall health.14 Examples include load times, memory consumption, and throughput, which help assess the system's operational efficiency.14
  • Traces: Traces provide a visual representation of a user's journey or a request's flow through a distributed system.14 By assigning a unique identifier to a request, traces track its progression across multiple services, enabling the identification of issues at each stage of the system's workflow.20

The distinction between observability and monitoring is subtle yet significant. While monitoring focuses on tracking known metrics and established baselines, observability infers internal system state from external outputs (logs, traces, metrics), enabling deeper investigation into unexpected behaviors.14 Observability is proactive, allowing teams to ask arbitrary questions about system behavior, particularly concerning issues they did not anticipate.16

The user's query implicitly touches upon observability. If logs are part of a comprehensive observability strategy, they are no longer merely "debug printfs" but structured, intentional signals of system behavior. This recontextualizes logs from a purely operational tool to a potential testing asset, especially in distributed systems where traditional state and collaboration checks are more challenging to implement effectively.20 Adopting an observability-driven development (ODD) mindset 22 is thus a prerequisite for effective log-based testing. It means instrumenting code not just for production monitoring but also for testability, making business-specific signals explicit and verifiable.

The table below summarizes the three pillars of observability and their distinct roles in enhancing software testing.

Pillar Definition Role in Testing Contribution to Decoupling
Logs Detailed records of events, actions, and errors within a system.14 Provide detailed event context for debugging test failures 14; Verify business-specific signals (Type A logs) 9; Offer audit trails for functional flows.24 Type A logs provide a high-level behavioral output, reducing coupling to internal state or specific interactions.
Metrics Quantitative measurements of system performance, resource utilization, and health.14 Quantify test performance (e.g., load times, throughput) 14; Identify performance bottlenecks during testing 26; Track system health and resource consumption.14 Offer performance-based verification independent of implementation details.
Traces Visual representations of user journeys and system interactions across services.14 Visualize end-to-end user journeys and data flow across distributed services 20; Identify latency issues and critical paths in distributed test scenarios 20; Correlate events across microservices using unique identifiers.20 Provide end-to-end flow verification without deep internal inspection of each service, reducing coupling to individual component implementations.

2.2. The Concept of Business-Specific Signals in Application Logs

The effectiveness of using logs for testing hinges on a crucial distinction: not all log entries are created equal for verification purposes. A critical differentiation must be made between various types of logs:

  • Type A Logs: These are logs whose absence would break the program's semantics, meaning the application would no longer function "as expected" for end-users.9 Such logs are intrinsically part of the functional output of the system and, as such,
    should be subjected to testing.9 These often represent explicit business-specific events or critical system states.
  • Type B Logs: These logs have no semantic impact if they are missing; their sole purpose is to aid in understanding program operations, primarily for debugging or troubleshooting.9 These logs generally should
    not be tested directly, as their content or presence is an implementation detail that can change without altering core functionality.
  • Type C Logs: This category includes logs that, if present, could "break" the application in a broader sense, such as logging sensitive data like Personally Identifiable Information (PII).9 These logs should be tested for their
    absence or for the correct masking of sensitive data.

For testing purposes, the primary focus should be on Type A logs, which capture significant and noteworthy business events.23 These are not merely verbose debug statements but deliberate signals indicating that a specific business process step has occurred or a critical state has been reached. Such logs can and should contain structured data, such as a

correlation_id (as highlighted in the user's query), which enables the tracing of specific transactions or user flows across various system components.19

To facilitate effective verification, logs intended for testing must be strategically designed. They should be structured, consistent, and contain relevant, concise data, including timestamps, test parameters, and clear indications of pass/fail status.23 Ideally, they should provide a coherent "narrative" of events leading to a particular outcome, making it easier to understand the flow of control and data.23

The user's query highlights "business-specific signals...at the debug level of logs," implying a deliberate design choice. If a log entry, particularly one containing a correlation_id, signifies a critical business event (e.g., "OrderProcessed," "PaymentAuthorized"), it effectively becomes an observable output of the system's behavior, akin to an API response or a database state change. This redefines the log from an internal implementation detail to an external, verifiable contract of behavior. This redefinition is crucial for how logs are viewed in testing; instead of parsing arbitrary log lines, tests would assert on the presence and content of pre-defined business events within the log stream. This directly addresses the coupling problem by testing the intended behavior (e.g., "payment was authorized") rather than the internal mechanisms that achieved it.

2.3. Potential Benefits of Log-Based Verification for Decoupling

Adopting a strategy of log-based verification, particularly by focusing on business-specific signals, offers several compelling advantages that address the limitations of traditional testing methods and promote decoupled, flexible software design.

One of the most significant benefits is reduced coupling between tests and the internal implementation details of the software.7 By asserting on the presence and content of high-level business events recorded in logs, tests become less dependent on the specific method signatures, internal object states, or the exact sequence of internal interactions. This allows developers greater freedom to refactor, optimize, or even completely rewrite internal components without necessitating changes to the test suite, as long as the observable business behavior remains consistent.7 This flexibility directly combats the "refactoring tax" discussed earlier.

Furthermore, logs excel at providing a behavioral focus for testing. They can capture the complete sequence and outcome of business events, enabling comprehensive verification of complex, end-to-end behaviors that span multiple modules or services.31 A high-level test designed to describe a specific business flow can swiftly detect unexpected changes in behavior, such as a request firing twice or a malformed payload, simply by examining the structured log output.31 This moves testing closer to the "black-box" ideal, where the internal workings are less relevant than the observable stream of business events.

Logs are inherently designed for debugging and root cause analysis.23 When a log-based test fails, the detailed log entries, especially when enriched with correlation IDs, provide immediate and precise context for diagnosing the root cause of the failure.14 This accelerates issue resolution, as developers can quickly pinpoint the exact point of failure within the system's execution flow. Logs serve as a verifiable side-effect of a system's operation. Unlike return values or direct state changes, logs are often a

stream of events, which can be particularly powerful for verifying temporal aspects or sequences of operations in complex systems, without directly inspecting internal state or mocking every collaborator. The correlation_id is key here, linking disparate log entries to a single business transaction. This positions log-based testing as a powerful, non-intrusive way to verify complex behavioral patterns, especially in asynchronous or distributed systems.

Finally, log-based verification is particularly advantageous for testing distributed systems and event-driven architectures. In such environments, where state is distributed, interactions are asynchronous, and components are loosely coupled, traditional state or collaboration verification becomes exceedingly difficult and brittle.20 Logs and traces, especially when correlated by unique identifiers, become critical for understanding system behavior and verifying complex flows that span across numerous services and network boundaries.20 This approach allows for a more "black-box" verification, where the internal workings are less relevant than the observable stream of business events.

3. Why Log-Based Testing Is Not Mainstream: Challenges and Misconceptions

Despite the compelling potential benefits, log-based testing has not yet become a mainstream verification strategy. Its widespread adoption has been hindered by a combination of significant technical hurdles, deeply ingrained perceptual barriers, and considerable cost implications.

3.1. Technical Hurdles: Data Volume, Noise, Parsing, and Real-time Analysis

One of the most formidable challenges in leveraging logs for testing is the sheer data deluge generated by modern applications, particularly in distributed environments.18 Systems can produce gigabytes of log data per hour, comprising billions of data points, making real-time processing and analysis an overwhelming task.29 This massive scale makes it difficult to manage and extract meaningful information efficiently.

Compounding the volume issue is the problem of noise and irrelevance. Logs often contain a substantial amount of extraneous or low-value data, making it akin to searching for a "needle in a haystack" to find the specific log message that indicates a problem or a critical business event.23 Without effective filtering and clear logging objectives, tests can drown in irrelevant data.

The parsing complexity of raw log messages further complicates matters. Many logs are unstructured or follow inconsistent formats, rendering them difficult to automatically parse into structured data suitable for programmatic analysis and assertion.11 Extracting meaningful insights often requires advanced analytics, machine learning algorithms, or sophisticated log parsers.29

Real-time analysis and correlation across disparate log sources present another significant technical challenge. Identifying patterns, detecting anomalies, and correlating events across different services in real-time is complex, especially in multi-cloud or highly distributed environments where differing time zones and logging standards can lead to inconsistencies.18

Moreover, performance overhead can be a concern. Collecting fine-grained metrics and implementing extensive instrumentation, such as adding tracing to every function call, can degrade database or overall system performance, creating a trade-off between observability and runtime efficiency.41

These cumulative technical challenges can lead to what might be described as an "observability tax." While observability offers clear benefits, without proper tools and strategies—such as scalable infrastructure, advanced analytics, intelligent filtering, and clear objectives—it can generate overwhelming data that is difficult to translate into actionable information. This can result in "alert fatigue" and high operational costs, directly impacting its viability for testing, as tests would struggle to process and verify relevant data amidst the noise. Overcoming these challenges requires significant investment in infrastructure, specialized tools (e.g., AI/ML-driven analysis), and expertise. This explains why direct log-based testing has not been mainstream without these advanced capabilities.

The table below summarizes the key challenges in log-based testing and outlines potential mitigation strategies.

Challenge Description Mitigation Strategy
Data Deluge Modern applications generate overwhelming volumes of log data, making real-time processing difficult.29 Implement scalable infrastructures and robust data management strategies, such as distributed architectures.34
Noise/Irrelevance Logs often contain excessive irrelevant data, obscuring critical information.23 Employ intelligent filtering and prioritization techniques 28; Define clear logging objectives.19
Parsing Complexity Raw log messages are often unstructured or inconsistent, hindering automated analysis.11 Enforce structured logging formats (e.g., JSON, key-value pairs) 23; Utilize advanced log parsers.29
Real-time Analysis/Correlation Difficulty in identifying patterns, anomalies, and correlating events across diverse log sources in real-time.28 Leverage advanced analytics and machine learning algorithms for pattern recognition and anomaly detection 29; Centralize logging systems.26
Performance Overhead Extensive instrumentation and fine-grained metric collection can degrade system performance.41 Optimize logging levels for production environments 23; Prioritize essential metrics and logs for collection.14
Storage Costs Retaining large volumes of log data for extended periods, often due to compliance, incurs significant costs.34 Implement efficient data archiving solutions and tiered storage strategies.34
Multi-Cloud Complexity Challenges with integrations, data inconsistency, and security across different cloud providers.18 Adopt cloud-native logging solutions 34; Ensure log consistency through normalization techniques and frequent audits.34
Perceptual Barrier (Logs for Debugging) Logs are traditionally viewed solely as debugging tools, not as verifiable outputs for testing.24 Foster a cultural shift towards treating certain logs as first-class, verifiable business signals 9; Educate teams on the benefits of log-based verification.
Skill Gap Proper setup and analysis of observability data, especially with advanced tools, require specialized skills.14 Invest in training and documentation for teams on observability tools and practices.17

3.2. Perceptual Barriers: Logs as Debugging Tools, Not Test Assertions

Beyond the technical complexities, a significant impediment to the adoption of log-based testing lies in the prevailing perception of what logs are for. Historically, logs have been primarily conceived as a developer convenience or a tool for post-mortem analysis and troubleshooting.24 Their purpose was to help developers and support personnel understand program behavior when unexpected events occurred.27 This ingrained mental model often prevents logs from being considered a core part of a method's functional specification or a valid source for test assertions.

This traditional view contributes to a widespread reluctance to test logs directly. Many developers and testers perceive such testing as asserting on implementation details, which is known to lead to brittle tests.9 Furthermore, the practicalities of setting up log-based tests can be cumbersome; it might involve making production code messy by injecting loggers or making tests "smelly" by replacing static loggers with mocks.27 The act of parsing continuous streams of strings and ensuring that logs are captured without being interlaced with output from other sources can also be technically challenging and difficult to manage reliably within a test framework.11

The common advice, "you should never test logs," is often misinterpreted and contributes to this perceptual barrier. A more nuanced understanding suggests that this advice applies primarily to "Type B" logs—those purely for debugging that have no semantic impact if missing.9 However, logs that are an integral part of the functional output or represent critical business events ("Type A" logs)

should be tested, as they are part of the system's observable behavior.9 The resistance to testing logs stems from the fear of coupling tests to ephemeral debug statements and the practical difficulties of asserting on unstructured text streams. Shifting to log-based verification requires a fundamental change in how developers and testers perceive and design logs. It necessitates treating certain log entries as first-class outputs of business logic, requiring careful design and standardization of log messages.23

3.3. Cost Implications of Extensive Log Management

The financial implications associated with extensive log management also act as a significant deterrent to the widespread adoption of log-based testing. Collecting, processing, and storing vast volumes of log data, particularly for extended periods, can be prohibitively expensive.34 Many compliance regulations, such as HIPAA, PCI DSS, and GDPR, mandate that organizations retain their logs for forensic analysis and auditing purposes, often for years.34 Balancing these long-term retention requirements with the associated storage costs presents a critical challenge for organizations.34

Beyond storage, the infrastructure investment required for setting up and maintaining robust observability tools is substantial. This includes solutions for log collection, aggregation, processing, analysis, and visualization.14 These tools, while powerful, often come with considerable licensing and operational costs.

Furthermore, the effective implementation and analysis of observability data, especially when incorporating advanced analytics and artificial intelligence, demand specialized skills and expertise.14 Training existing teams or hiring new talent with these specific proficiencies adds to the overall operational expenditure.

The more granular the logs—which is often desired for detailed testing and debugging—the higher the associated costs. For instance, tracking every query in a high-traffic database could generate terabytes of logs daily, leading to escalating storage and processing expenses, especially in cloud environments.41 This economic reality creates a trade-off between the desired level of detail for verification and the financial viability of capturing and analyzing that data. This acts as a significant deterrent to using logs for

all testing, pushing teams to be selective about what they log for verification purposes. Effective log-based testing therefore requires a strategic approach to logging, prioritizing "business-specific signals" (Type A logs) over verbose debug information (Type B logs) for verification purposes, to manage costs and data volume effectively. This reinforces the need for clear logging objectives.23

4. Academic Research and Emerging Methodologies

The academic community is actively exploring and developing methodologies to address the challenges and harness the opportunities presented by leveraging logs and observability for enhanced software testing. This research forms the theoretical backbone for the shift towards more decoupled and behavior-centric verification.

4.1. Observability-Driven Development and Testing

Observability-Driven Development (ODD) represents a significant paradigm shift, integrating observability practices into the early stages of the software development lifecycle (SDLC).22 This approach emphasizes instrumenting code to proactively gather detailed telemetry data—comprising logs, metrics, and traces—to gain a deep understanding of application behavior in real-time, even before deployment.22

The benefits of ODD for testing are substantial. It enhances test coverage by providing granular visibility into previously untested or complex areas of the codebase.14 By offering real-time insights and enabling root cause detection through data correlation, ODD facilitates faster debugging and more efficient issue resolution.14 This proactive stance allows development teams to identify and address potential problems much earlier in the development cycle, ideally before they impact end-users or reach production environments.22

Academic research has extensively investigated the influence of observability on test generation and fault-finding capabilities. Studies indicate that exposing internal object states through "observability transformations" can significantly increase the fault-finding potential of automatically generated tests, as measured by mutation scores.42 Conversely, low observability can lead to inconsistent test results and make test creation more difficult, as the system's internal behavior is opaque to the testing mechanisms.42

Observability is also recognized as a critical aspect of testability, which refers to the ease with which a program can be tested.42 It is fundamentally the ability to monitor a program's behavior through its outputs.42 The core tension in "test the behavior, not the implementation" is how to verify behavior without becoming a "white box" test. Research clearly demonstrates that "low observability" leads to tests failing to produce appropriate assertions and limits their fault-finding potential.42 This research proposes "observability transformation" to "expose encapsulated attributes to the test generator" and "internal object states to automated test generators." This suggests that to effectively test behavior (black box), it is sometimes necessary to

selectively expose internal signals, effectively turning them into "observable outputs" for testing purposes. This is not about testing all implementation, but making relevant implementation details (like business signals in logs) observable. This provides a theoretical underpinning for the user's query, suggesting that logs, when designed as deliberate "observable outputs" (e.g., business signals with correlation_id), can serve as the necessary "assertions" for behavioral testing, bridging the gap between abstract behavior and concrete verification points.

4.2. Event Stream Processing and Verification Strategies

Modern distributed systems frequently adopt Event-Driven Architectures (EDAs), where components communicate asynchronously via event streams.25 In patterns like Event Sourcing, changes to application state are stored as a sequence of immutable events, providing a complete, auditable history of system evolution.25

Testing such event-driven systems presents unique complexities due to their asynchronous nature, distributed state management, and reliance on precise timing.32 Traditional testing methods often struggle in these environments. Key strategies emerging from research and practice for testing EDAs include:

  • Event Contract Testing: Validating the structure, format, and compatibility of events exchanged between components ensures that systems remain compatible as they evolve.32
  • Event Recording & Playback: Capturing and replaying real-world event streams allows for simulating production-like conditions in controlled testing environments, enabling realistic behavior verification.32
  • Event Sequence Testing: This involves creating event chains and introducing controlled delays to verify that events are processed in the correct order and timing, which is crucial for uncovering subtle race conditions and concurrency bugs.32
  • State Testing for Consistency: In distributed systems, verifying consistent data and state transitions across multiple, loosely coupled components is paramount.32 Logs and traces can be instrumental in observing these distributed state changes.
  • System Simulation: Testing under production-like conditions, including network issues and high loads, helps ensure system resilience and performance.32

Logs play a vital role in EDAs, providing a comprehensive record of system events for troubleshooting, identifying and diagnosing errors, and understanding performance characteristics.33 Event logs capture key activity information 46, and event sourcing inherently provides a full log of historical events, which is invaluable for auditing and calculating point-in-time states.25

Academic research is actively addressing how to effectively process and store log data and extract meaningful insights from it.47 Studies also explore the use of Large Language Models (LLMs) for advanced event log analysis, aiming to improve automated techniques for anomaly detection and understanding.37 Furthermore, academic papers delve into event stream processing as a computational approach to inferring event occurrences from data streams in real-time, without constant reference to a database.45

The user's mention of correlation_id and "business-specific signals" strongly aligns with event-driven architectures. Event sourcing and event stream processing, where changes are captured as a sequence of immutable events, inherently treat these events as logs, or as data directly derivable from logs.25 If business logic is expressed as events, and these events are logged, then the log

becomes the verifiable event stream. For systems designed with event sourcing or event-driven patterns, log-based testing is not merely an alternative; it is a natural and highly effective fit. The correlation_id becomes the key to linking events across a distributed trace, enabling end-to-end behavioral verification without the need for direct state inspection or complex mocking of distributed services. This is particularly relevant for microservices, where traditional integration testing can be cumbersome.

4.3. Advances in Automated Log Analysis and AI for Testing

The challenges of managing and analyzing the immense volume of log data have spurred significant advancements in automated log analysis, particularly through the application of Artificial Intelligence (AI) and Machine Learning (ML). These technologies are transforming the feasibility of using logs for testing.

Machine learning algorithms are increasingly employed to uncover valuable insights from extensive log data.29 These algorithms can categorize similar logs, streamline data organization, and significantly accelerate data processing, thereby reducing the time and manual effort required for log analysis.29

AI-driven algorithms are capable of automatically detecting anomalies in test logs, surpassing human capacity for pattern recognition and reducing the need for manual intervention.29 This ensures timely issue identification and resolution. Such systems can identify subtle, "missed" events by correlating log data with information from other sources, such as social threads or open-source repositories.35

Recent research has introduced novel frameworks, such as LogLLM, which leverage Large Language Models (LLMs) for log-based anomaly detection.37 LogLLM employs models like BERT for extracting semantic vectors from log messages and Llama for classifying log sequences, streamlining the anomaly detection process without reliance on traditional, rule-based log parsers.37 This represents a significant leap in automating the interpretation of unstructured log data.

Beyond anomaly detection, research also focuses on evaluating the quality of logging statements themselves, not just the resulting logs. Tools like AL-Bench assess both static log statements in source code and their corresponding runtime log files.38 This research has revealed significant limitations in existing logging practices, showing that poorly constructed or misleading log messages can inadvertently obscure actual program execution patterns.38 Even the best-performing tools for generating log statements may fail to compile or produce logs with low semantic similarity to expected outputs.38 This highlights that while AI can help

analyze logs, the quality and intent of the logs themselves remain paramount.

AI serves as a powerful enabler for log-based testing, significantly lowering the barrier to entry by automating complex analysis tasks. However, it is not a panacea. The principle of "garbage in, garbage out" still applies; AI amplifies the value of well-designed logs but cannot compensate for poorly conceived logging strategies. Therefore, while AI can automate analysis, it does not remove the fundamental need for developers to strategically design and instrument their code with high-quality, business-relevant log signals.

5. Real-World Applications and Case Studies

The theoretical advantages and academic advancements in log-based verification are increasingly finding practical application in industry. This section explores current industry practices, delves into Klarna's snabbkaffe framework, and examines the broader adoption of distributed tracing and observability in modern software architectures.

5.1. Industry Practices in Leveraging Logs for Operational Insights

Companies across various sectors widely utilize log analysis for gaining critical operational insights. This involves collecting, processing, and interpreting log information generated by computer systems, applications, and network devices.19 Key practices include pattern recognition, anomaly detection, correlation, and visualization through dashboards, which help in understanding events, detecting issues, and monitoring performance.28 These insights are crucial for troubleshooting application errors, detecting unauthorized access, and diagnosing connectivity problems.28

Numerous companies, including Macmillan Learning, Peloton Cycle, Creative Market, Stanley Black & Decker, Vivint Solar, Vroom, Recruiterbox, Datami, XAPPmedia, Bemobi, Molecule, Monex Insight, and Speedway Motors, leverage specialized log management tools (such as Loggly) for their monitoring and operational intelligence needs.50 These tools help them manage the vast amounts of log data and extract actionable information.

A compelling example of log-driven testing in a specialized domain is found in advanced driver-assistance systems (ADAS) and automated driving (AD) development. Here, log-based testing (Log Sim) is employed to identify, reproduce, debug, and resolve issues derived directly from real-world tests.51 This methodology allows for simulating hundreds of miles of driving for every mile actually driven, enabling large-scale evaluation, preventing regressions, and ensuring measurable performance improvements.51 This practice highlights a powerful production-testing feedback loop: if logs are already rich sources of information in production for debugging, they can be leveraged

earlier in the development cycle for testing. The correlation_id (from the user query) is a prime example of a signal valuable for both production debugging and test verification. This indicates that existing investment in production observability tools and practices can be directly leveraged and extended to enhance testing, making tests more realistic and effective at catching real-world issues.

Furthermore, the industry is increasingly adopting AI-driven log analysis to overcome the challenges of data volume and complexity. Companies are leveraging machine learning algorithms to accelerate data processing through log categorization, automatically detect issues, prioritize alerts, and identify anomalies early.29 This automation offloads repetitive tasks from engineers, allowing them to focus on more complex problem-solving and strategic work.29

5.2. Deep Dive: Klarna's Snabbkaffe for Erlang

Klarna's snabbkaffe for Erlang provides a concrete and compelling example of how log-based verification can be effectively implemented to achieve decoupled, behavior-centric testing. The framework operates on the philosophy: "If humans can find bugs by reading the logs, so can computers".52

Snabbkaffe is a trace-based test framework specifically designed for Erlang systems.52 Its methodology involves a distinct two-stage process:

  1. Manual Instrumentation: Programmers explicitly instrument their code with "trace points".52 These trace points are not arbitrary debug statements but are designed to emit structured log messages in the release build, serving as intentional, verifiable signals of system behavior.52
  2. Split Testcases: Each test case is divided into two distinct parts:
    • Run Stage: The program under test executes, and as it runs, it emits an event trace composed of these structured log messages.52
    • Check Stage: Subsequently, the generated trace is collected and validated against predefined specifications.52 This validation involves asserting on the presence, content, and sequence of the structured events within the trace.

This approach offers several key properties that directly address the issues of coupling and test brittleness:

  • Separation of Checks: The verification logic (checks) is explicitly separated from the program execution, which promotes cleaner test code and reduces entanglement with the system under test.52
  • Composability: The checks are designed to be independent of each other and fully composable, allowing for flexible and modular test suites.52
  • Complete History: The generated trace captures a complete history of the process execution, which is particularly beneficial for detecting complex concurrency bugs, such as livelocks, that are notoriously difficult to identify with traditional methods.52
  • Fault and Delay Injection: Snabbkaffe also supports the injection of faults and delays into the system, enabling the testing of supervision trees and rare code paths, further enhancing the robustness of the system under test.52

Klarna's practical application of this methodology is evident in its use of logs within its Merchant portal for verifying order creation and details.54 The ability to search logs by a Klarna order ID—a business-specific identifier akin to a

correlation_id—to view all related requests and details demonstrates a direct use of structured log data for functional verification.54 This provides a concrete, successful example of how log-based testing can be implemented to achieve decoupled, behavior-centric verification, especially in concurrent and distributed environments like Erlang. It shows that the "test the behavior, not the implementation" principle can be effectively applied by making behavior explicit in structured logs/traces.

5.3. Broader Adoption of Distributed Tracing and Observability in Microservices

The increasing complexity of modern software, particularly the widespread adoption of microservices architectures, has made traditional debugging and testing approaches inadequate. In such environments, a single user request often interacts with numerous small, interconnected services, making it exceedingly difficult to identify the source of issues or track the complete flow of a transaction.20 This inherent complexity has driven the broader adoption of distributed tracing and observability practices.

Distributed tracing is a method specifically designed to monitor and follow the path of a user request as it traverses multiple services within a microservices architecture.20 It achieves this by assigning a unique identifier to each request, allowing every service involved in processing that request to log details about its role, all correlated by this identifier.20

The industry has seen the emergence and widespread use of powerful tools and frameworks for distributed tracing and observability. Popular solutions include OpenTelemetry, Jaeger, Zipkin, AWS X-Ray, and Datadog APM.20 These tools provide capabilities for visualizing and analyzing trace data, which is crucial for identifying performance bottlenecks, latency issues, and errors across the distributed system.20 For instance, MicroProfile Telemetry 2.0 adopts OpenTelemetry to standardize the export of traces, metrics, and logs, further integrating these observability signals into the development ecosystem.21

The benefits of distributed tracing extend directly to testing. It enables detailed latency analysis, allowing teams to examine the duration of each span within a trace to pinpoint where most of the time is being spent.20 It facilitates critical path identification, highlighting the longest sequence of dependent operations, and aids in diagnosing failures by providing error codes and exception details within the trace.20 Performance data from runtime metrics, such as CPU usage or memory consumption, can be correlated with traces to identify underlying causes of latency.21 Major technology companies like Uber and Netflix leverage distributed tracing extensively to manage and debug their highly complex, high-volume microservices architectures.20 Dynatrace also offers distributed tracing capabilities for real-time visualization of service dependencies, further streamlining the understanding of component interactions.55

The industry's move towards distributed systems inherently necessitates observability practices, which in turn provide the rich telemetry (logs, traces) that can be leveraged for more robust, behavior-driven testing. The ability to "assign a unique identifier to the request" 20 and visualize end-to-end flows is directly analogous to the

correlation_id concept in the user's query. This indicates a natural convergence where tools built for operational observability are inherently valuable for testing, particularly for integration and end-to-end scenarios. This suggests that log-based verification is not a fringe idea but a natural evolution of testing in complex, modern architectures.

6. Recommendations for Improving Log-Based Verification

To effectively leverage logs for test verification and overcome the historical challenges, organizations should adopt a strategic and integrated approach. The following recommendations draw upon the insights from academic research and real-world applications.

6.1. Strategic Log Instrumentation and Design for Testability

The foundation of effective log-based verification lies in treating logging as a deliberate design choice rather than an afterthought. This requires careful planning and implementation of log instrumentation.

First, establish clear objectives for logging.19 To prevent the generation of noisy logs that lack value, it is crucial to define the overarching business or operational goals that logging aims to support, along with key performance indicators (KPIs).23 This clarity helps determine precisely which events are significant enough to be logged for verification purposes, distinguishing them from purely diagnostic information.23

Second, implement structured logging formats.23 Standardized formats, such as JSON or key-value pairs, ensure consistency across the application and facilitate automated parsing and efficient analysis by log management solutions.23 Unformatted logs are difficult to parse and analyze, hindering their utility for automated testing.36

Third, design logs to explicitly capture business-specific signals.9 Instead of merely logging technical details, focus on emitting events that represent significant business actions or state transitions (Type A logs). Crucially, include

correlation IDs (like the user's correlation_id) to link related events across distributed systems and provide a comprehensive narrative of a transaction's journey.20 This moves logging from a casual "printf debugging" habit to a deliberate architectural and design choice. If logs are to be used for verification, they must be designed for it, much like an API. This involves upfront planning of log content, format, and levels, ensuring that the necessary business signals are emitted in a consistent and verifiable manner. It requires a shift in developer mindset to view certain logs as part of the system's external contract.

Finally, ensure the appropriate use of log levels.23 Log levels such as INFO, WARN, ERROR, and FATAL should be used correctly to indicate the severity of an event. While higher verbosity (DEBUG, TRACE) may be temporarily enabled for troubleshooting, production environments should typically default to INFO to minimize noise.23 Implementing agile mechanisms to adjust log levels on the fly, either at the host, client, or service-wide level, can be highly beneficial for targeted debugging without overwhelming the system with unnecessary data.23

6.2. Integrating Log Verification into Automated Test Pipelines

Once logs are strategically designed for testability, the next step is to seamlessly integrate their verification into automated test pipelines. This transforms manual log review into a scalable, automated verification step.

First, leverage test automation frameworks that are capable of collecting and analyzing log data.17 This involves integrating observability tools directly into Continuous Integration/Continuous Delivery (CI/CD) pipelines to provide immediate feedback on system behavior during test execution.14

Second, develop custom assertions tailored to the specific needs of the business events captured in logs.32 This moves beyond simple string parsing to more robust validation, allowing tests to verify the presence, content, and sequence of business signals. The

correlation_id is crucial here for filtering and asserting on specific transaction flows, effectively automating the "human log reader" into test assertions.52

Third, consider decoupling test execution from traditional CI/CD pipelines by utilizing dedicated test orchestration platforms, such as Testkube.56 These platforms can provide consistent test environments, unified dashboards for results, and the flexibility to run tests on demand—either manually, as part of CI/CD, or in response to external system events.56 This approach also enables scaling tests horizontally for load generation or vertically for multi-scenario functional tests.56

Finally, incorporate error simulation and fault injection techniques into the testing process.2 By intentionally introducing errors or delays, teams can verify that the system generates appropriate error logs and handles failure scenarios gracefully. This proactive testing, combined with log verification, ensures robust error handling and resilience.

6.3. Best Practices for Distributed and Event-Driven Systems

For modern, complex architectures, particularly distributed and event-driven systems, log-based verification is not merely an alternative but arguably the most native and effective way to achieve comprehensive behavioral testing.

A critical best practice is the comprehensive integration of distributed tracing.20 Tools like OpenTelemetry enable tracking requests across multiple microservices, providing end-to-end visibility of transaction flows. This allows for the correlation of logs, metrics, and traces, offering a holistic view of system behavior and facilitating deep analysis of latency and dependencies during testing.20 The

correlation_id becomes the thread that ties together the distributed behavioral verification.

Implementing centralized logging is essential for distributed systems.26 Aggregating logs from all components into a single, unified system simplifies analysis and troubleshooting across the entire distributed environment, making it easier to track complex interactions and identify issues that span multiple services.26

For event-driven systems, it is crucial to explicitly verify event order and state consistency using logs and traces.32 This involves developing tests that assert on the sequence of events and confirm that shared data remains synchronized across distributed components, even under high-concurrency operations.

Lastly, employing chaos engineering and fault injection techniques is highly recommended.15 By deliberately injecting errors or simulating unexpected scenarios, teams can observe how the system reacts and verify that appropriate log patterns are generated, ensuring resilience and robust error handling in production. If logs are already the primary means of observing behavior in distributed production environments, then it is logical to extend their use to testing. This approach addresses challenges that traditional state and collaboration verification struggle with in these complex environments.

7. Conclusion

The landscape of software testing is continually evolving, driven by the increasing complexity of modern, distributed, and event-driven architectures. Traditional state-based and collaboration-based testing, while valuable, often introduce tight coupling to implementation details, leading to brittle tests and a significant "refactoring tax." This report has explored a compelling alternative: leveraging business-specific signals embedded within application logs for test verification.

The analysis demonstrates that while historical technical hurdles—such as the sheer volume and noise of log data, parsing complexities, and the challenge of real-time analysis—alongside perceptual barriers that confine logs to debugging, have limited their direct use in testing, these impediments are being systematically addressed. Advancements in observability-driven development (ODD), event stream processing, and AI-powered log analysis are transforming the feasibility and effectiveness of this approach.

Key to this paradigm shift is the strategic design of logs. By treating certain log entries as first-class, verifiable outputs of business logic—structured, consistent, and enriched with correlation IDs—organizations can create tests that assert on observable behaviors rather than internal implementations. This significantly reduces coupling, enhances test stability, and allows for more flexible code refactoring.

Real-world examples, such as Klarna's snabbkaffe framework, provide a clear blueprint for successful implementation. Snabbkaffe's trace-based methodology, which involves instrumenting code with explicit "trace points" and separating test execution from verification against structured log events, showcases how decoupled, behavior-centric testing can be achieved, particularly in concurrent environments. The broader industry adoption of distributed tracing further validates the inherent value of leveraging comprehensive telemetry for understanding and verifying complex system interactions.

To fully realize the benefits of log-based verification, organizations are advised to:

  1. Strategically instrument their code: Design logs with clear objectives, using structured formats and embedding business-specific signals and correlation IDs.
  2. Integrate log verification into automated pipelines: Develop custom assertions that can parse and validate these structured log events, and consider dedicated test orchestration platforms for scalable execution.
  3. Adopt best practices for distributed systems: Implement distributed tracing, centralize logging, verify event order and state consistency, and utilize fault injection to test resilience.

By embracing these recommendations, organizations can move beyond the limitations of traditional testing, establish a more robust and flexible verification strategy, and ultimately enhance the quality, maintainability, and agility of their complex software systems.

References

Works cited

  1. Stub vs Mock: Choosing the Right Test Double - BairesDev, accessed August 1, 2025, https://www.bairesdev.com/blog/stub-vs-mock/
  2. Software Testing - Mock Testing - GeeksforGeeks, accessed August 1, 2025, https://www.geeksforgeeks.org/software-testing/software-testing-mock-testing/
  3. Test-driven development - Wikipedia, accessed August 1, 2025, https://en.wikipedia.org/wiki/Test-driven_development
  4. Coupling and Cohesion - Software Engineering - GeeksforGeeks, accessed August 1, 2025, https://www.geeksforgeeks.org/software-engineering/software-engineering-coupling-and-cohesion/
  5. Pitfalls Of Mocking In Tests And How To Avoid It - Xebia, accessed August 1, 2025, https://xebia.com/blog/pitfalls-mocking-tests-how-to-avoid/
  6. Brittle unit tests due to need for excessive mocking - Software Engineering Stack Exchange, accessed August 1, 2025, https://softwareengineering.stackexchange.com/questions/189805/brittle-unit-tests-due-to-need-for-excessive-mocking
  7. Avoid Tight Coupling of Tests to Implementation Details | Nejc Korasa, accessed August 1, 2025, https://nejckorasa.github.io/posts/microservice-testing/
  8. Unit testing behaviours without coupling to implementation details, accessed August 1, 2025, https://softwareengineering.stackexchange.com/questions/234024/unit-testing-behaviours-without-coupling-to-implementation-details
  9. How to Write Unit Tests for Logging - Hacker News, accessed August 1, 2025, https://news.ycombinator.com/item?id=25057372
  10. Testing State Machines - ACCU, accessed August 1, 2025, https://accu.org/journals/overload/17/90/jones_1548/
  11. Should log statements be tested? - Software Engineering Stack Exchange, accessed August 1, 2025, https://softwareengineering.stackexchange.com/questions/384164/should-log-statements-be-tested
  12. Should the unit tests be independent of the implementations, accessed August 1, 2025, https://softwareengineering.stackexchange.com/questions/451083/should-the-unit-tests-be-independent-of-the-implementations
  13. Best practices for writing unit tests - .NET - Microsoft Learn, accessed August 1, 2025, https://learn.microsoft.com/en-us/dotnet/core/testing/unit-testing-best-practices
  14. What is Test Observability and How Does it Improve the Testing Process? - Opkey, accessed August 1, 2025, https://www.opkey.com/blog/what-is-test-observability-and-how-does-it-improve-the-testing-process
  15. What is Observability Testing and Why Is It So Important to Quality? - Abstracta, accessed August 1, 2025, https://abstracta.us/blog/observability-testing/what-is-observability-testing-and-why-is-it-so-important-to-quality/
  16. Monitoring and Observability for Deep Learning Microservices in Distributed Systems, accessed August 1, 2025, https://www.researchgate.net/publication/392595147_Monitoring_and_Observability_for_Deep_Learning_Microservices_in_Distributed_Systems
  17. What is Test Observability in Software Testing? - Guide - Global App Testing, accessed August 1, 2025, https://www.globalapptesting.com/blog/software-observability
  18. What is Observability: Benefits, Challenges & Best Practices - SmartBear, accessed August 1, 2025, https://smartbear.com/blog/what-is-observability/
  19. Log Monitoring 101 Detailed Guide [Included 10 Tips] - SigNoz, accessed August 1, 2025, https://signoz.io/blog/log-monitoring/
  20. Distributed Tracing in Microservices - GeeksforGeeks, accessed August 1, 2025, https://www.geeksforgeeks.org/system-design/distributed-tracing-in-microservices/
  21. Observe microservices using metrics, logs and traces with MicroProfile Telemetry 2.0, accessed August 1, 2025, https://openliberty.io/blog/2025/03/28/microprofile-telemetry-20.html
  22. Top 9 Tools for Observability-Driven Development - Tracetest, accessed August 1, 2025, https://tracetest.io/learn/top-9-tools-for-observability-driven-development
  23. Logging Best Practices: 12 Dos and Don'ts | Better Stack Community, accessed August 1, 2025, https://betterstack.com/community/guides/logging/logging-best-practices/
  24. What is a Test Log? | BrowserStack, accessed August 1, 2025, https://www.browserstack.com/guide/what-is-test-log
  25. Azure Cosmos DB design pattern: Event sourcing - Code Samples | Microsoft Learn, accessed August 1, 2025, https://learn.microsoft.com/en-us/samples/azure-samples/cosmos-db-design-patterns/event-sourcing/
  26. What is Test Observability and How it Works? - Testsigma, accessed August 1, 2025, https://testsigma.com/blog/test-observability/
  27. Never Test Logging | Java Deep - WordPress.com, accessed August 1, 2025, https://javax0.wordpress.com/2014/02/19/never-test-logging/
  28. What is log analysis? Overview and best practices - LogicMonitor, accessed August 1, 2025, https://www.logicmonitor.com/blog/log-analysis
  29. Test Insights with AI-Powered Log Analysis & Reporting - LambdaTest, accessed August 1, 2025, https://www.lambdatest.com/blog/test-insights-with-ai-log-analysis-and-reporting/
  30. Test Log Tutorial: Boost Your Testing Skills with Industry Best Practices for Success - testRigor AI-Based Automated Testing Tool, accessed August 1, 2025, https://testrigor.com/blog/test-log-tutorial/
  31. Tao on “blue team” vs. “red team” LLMs - Hacker News, accessed August 1, 2025, https://news.ycombinator.com/item?id=44711306
  32. Event-Driven Testing: Key Strategies - Optiblack, accessed August 1, 2025, https://optiblack.com/insights/event-driven-testing-key-strategies
  33. Distributed Systems Monitoring: The Essential Guide - Loggly, accessed August 1, 2025, https://www.loggly.com/use-cases/distributed-systems-monitoring-the-essential-guide/
  34. Log monitoring for IT: Challenges and best practices | ManageEngine, accessed August 1, 2025, https://www.manageengine.com/products/eventlog/log-monitoring.html
  35. Overcoming the Biggest Challenge in Log Analysis Using Logz.io Cognitive Insights, accessed August 1, 2025, https://logz.io/blog/overcoming-biggest-challenge-log-analysis-cognitive-insights/
  36. Risk of Security and Monitoring Logging Failures | USA, accessed August 1, 2025, https://www.softwaresecured.com/post/risk-of-security-and-monitoring-logging-failures
  37. LogLLM: Log-based Anomaly Detection Using Large Language Models - arXiv, accessed August 1, 2025, https://arxiv.org/html/2411.08561v1
  38. AL-Bench: A Benchmark for Automatic Logging - arXiv, accessed August 1, 2025, https://arxiv.org/pdf/2502.03160
  39. lemur: log parsing with entropy sampling - arXiv, accessed August 1, 2025, https://arxiv.org/pdf/2402.18205
  40. LogUpdater: Automated Detection and Repair of Specific Defects in Logging Statements - arXiv, accessed August 1, 2025, https://www.arxiv.org/pdf/2408.03101
  41. What are the limitations of database observability? - Milvus, accessed August 1, 2025, https://milvus.io/ai-quick-reference/what-are-the-limitations-of-database-observability
  42. Increasing the Effectiveness of Automatically Generated Tests by Improving Class Observability - Alexandre Bergel, accessed August 1, 2025, http://bergel.eu/MyPapers/Gali25-ClassObservability.pdf
  43. Controllability and Observability - Auburn University, accessed August 1, 2025, https://www.eng.auburn.edu/~agrawvd/COURSE/E7250_05/REPORTS_TERM/Kantipudi_Tmeas.pdf
  44. Understanding Event Sourcing with Marten, accessed August 1, 2025, https://martendb.io/events/learning
  45. (PDF) Event Stream Processing with Multiple Threads - ResearchGate, accessed August 1, 2025, https://www.researchgate.net/publication/319488633_Event_Stream_Processing_with_Multiple_Threads
  46. LLM-based event log analysis techniques: A survey - arXiv, accessed August 1, 2025, https://arxiv.org/html/2502.00677v1
  47. Log-based software monitoring: a systematic mapping study - PMC - PubMed Central, accessed August 1, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC8114802/
  48. Redefining Event Detection and Information Dissemination: Lessons from X (Twitter) Data Streams and Beyond - MDPI, accessed August 1, 2025, https://www.mdpi.com/2073-431X/14/2/42
  49. Implementing a Real-time Complex Event Stream Processing System to Help Identify Potential Participants in Clinical and Translational Research Studies, accessed August 1, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC3041381/
  50. Logging Case Studies | Log Analysis | Log Monitoring by Loggly, accessed August 1, 2025, https://www.loggly.com/resources/types/case-studies/
  51. Log Sim | Log-based testing for ADAS and AD - Applied Intuition, accessed August 1, 2025, https://www.appliedintuition.com/products/log-sim
  52. kafka4beam/snabbkaffe: Distributed trace-based test ... - GitHub, accessed August 1, 2025, https://github.com/kafka4beam/snabbkaffe
  53. snabbkaffe - Hex.pm, accessed August 1, 2025, https://hex.pm/packages/snabbkaffe
  54. Test cases - Klarna Docs, accessed August 1, 2025, https://docs.klarna.com/resources/developer-tools/testing-payments/test-cases/
  55. Distributed tracing best practices for the software development lifecycle - Dynatrace, accessed August 1, 2025, https://www.dynatrace.com/news/blog/distributed-tracing-best-practices/
  56. Decouple Test Execution from CI/CD: 6 Reasons to Do It Now - Testkube, accessed August 1, 2025, https://testkube.io/learn/stop-running-tests-with-your-ci-cd-tool
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