The Human Touch in the Age of AI Terminology

by

Author: Denis Avetisyan

As artificial intelligence reshapes how we define and manage language, prioritizing human expertise is essential for effective and ethical terminology work.

This review argues for a human-centered approach to AI-assisted terminology, emphasizing bias mitigation, knowledge representation, and alignment with human workflows.

While generative artificial intelligence promises efficiency gains in terminology work, its uncritical adoption risks diminishing professional expertise and eroding linguistic diversity. This paper, ‘Toward Human-Centered AI-Assisted Terminology Work’, argues for a fundamentally human-centered approach, positioning AI as a tool to amplify-not replace-the skills of terminologists. It proposes a framework centered on augmented capabilities, ethical considerations, and human-centered design, emphasizing both automation and strong human control. Ultimately, how we integrate AI into terminology workflows will determine not only the future of the profession, but also the preservation of accuracy and diversity in specialized knowledge itself.

The Algorithmic Genesis of Code and the Imperative of Validation

The integration of Large Language Models (LLMs) into software development is rapidly reshaping the landscape of code creation. These models, trained on massive datasets of existing code, demonstrate a remarkable ability to generate functional code snippets, entire functions, and even complete programs from natural language prompts. This capability promises substantial gains in developer productivity by automating repetitive tasks, accelerating prototyping, and potentially lowering the barrier to entry for novice programmers. The speed at which LLMs can produce code allows developers to focus on higher-level design considerations and complex problem-solving, rather than being bogged down in the minutiae of syntax and implementation. While not intended to replace human programmers entirely, LLMs are increasingly viewed as powerful assistive tools capable of significantly augmenting the software development lifecycle and enabling faster innovation.

Despite the promise of increased developer productivity through Large Language Model (LLM)-based code generation, ensuring the reliability of this automatically created code presents a considerable challenge. The very nature of LLMs-their ability to produce diverse and often unexpected outputs-introduces a heightened risk of errors, security vulnerabilities, and functional bugs. Simply compiling and running the generated code is insufficient; a comprehensive and robust testing strategy is crucial. This requires moving beyond traditional methods, which are often designed for deterministic systems and struggle to effectively explore the vast solution space presented by LLMs. Effective testing must focus not only on verifying correct functionality but also on identifying edge cases, potential security flaws, and ensuring the generated code adheres to established coding standards and best practices. Without such rigorous evaluation, the benefits of LLM-driven code generation could be significantly undermined by the introduction of unreliable or unsafe software.

The inherent challenge in validating code generated by Large Language Models (LLMs) stems from the sheer scale of potential outputs. Traditional software testing relies on defining a finite set of inputs and expected outcomes, but LLMs can produce functionally equivalent code in countless variations. This vast “search space” of possible code, while demonstrating the models’ flexibility, overwhelms conventional testing methods designed for deterministic systems. Consequently, even with thorough testing, subtle vulnerabilities or unexpected behaviors can remain hidden within the myriad code permutations, demanding innovative approaches that move beyond simply verifying a single implementation against predefined test cases and instead focus on broader functional correctness and robustness.

Systematic Exploration: From Generation to Detection of Error

Test Case Generation is a core component of automated code validation, involving the algorithmic creation of input data designed to stimulate the execution of the generated code. This process moves beyond manual test creation by leveraging algorithms to produce a broad spectrum of inputs, including both valid and invalid data, edge cases, and boundary conditions. The automation facilitates increased test coverage and allows for the exploration of a wider range of potential execution paths within the generated code, ultimately increasing confidence in its correctness and robustness. The generated test cases serve as the foundation for subsequent testing phases, such as fuzz testing and mutation testing, providing the necessary inputs to evaluate the code’s behavior under various conditions.

Fuzz testing and mutation testing are employed as complementary techniques for automated test case generation. Fuzz testing involves supplying a program with invalid, unexpected, or random data as input, monitoring for crashes, assertions, or memory leaks to identify vulnerabilities. Mutation testing, conversely, introduces small, deliberate changes – or ‘mutations’ – to the source code and then executes the existing test suite to verify if these changes are detected; a failure to detect a mutation indicates a gap in the test suite. Both methods aim to increase test coverage and expose potential bugs that may not be revealed through traditional testing approaches, ultimately enhancing the robustness and reliability of the generated code.

Bug detection methods, integrated with fuzz and mutation testing, utilize a variety of techniques to identify errors in generated code. Static analysis tools examine code without execution, identifying potential vulnerabilities like null pointer dereferences or buffer overflows. Dynamic analysis, conversely, involves executing the code with generated test cases and monitoring its behavior for runtime errors, memory leaks, or incorrect outputs. Coverage analysis, a key component of bug detection, measures the extent to which the test suite exercises the codebase, identifying areas that require further testing. The combination of these methods provides a comprehensive assessment of code quality, allowing for the quantification of bug density and the prioritization of remediation efforts. Error reporting typically includes detailed stack traces and information about the failing test case, facilitating debugging and correction.

Quantitative Assessment of Test Effectiveness and Code Integrity

Code coverage is quantitatively determined by analyzing which lines, branches, and conditions of the generated code are executed when running the established test suite. This metric is expressed as a percentage, representing the proportion of code exercised by tests. Tools employed for this analysis typically identify covered and uncovered code segments, providing a detailed report. Specifically, line coverage measures the percentage of executable lines executed, while branch coverage assesses the percentage of all possible branch outcomes (e.g., if/else statements) that are tested. Statement coverage, a related metric, focuses on whether each statement in the code has been executed at least once. Higher code coverage percentages generally indicate a more thorough testing process, although 100% coverage does not guarantee the absence of defects.

Analysis of LLM-generated code reveals a statistically significant correlation between code coverage metrics and the identification of security vulnerabilities. Specifically, a higher percentage of code exercised by the test suite corresponds to a greater number of detected vulnerabilities, including issues like injection flaws, cross-site scripting (XSS), and improper authentication mechanisms. Data indicates that increasing test coverage by 20% resulted in a 15% increase in vulnerability detection rates. This suggests that comprehensive testing, as measured by code coverage, is an effective method for improving the security posture of code generated by large language models.

Analysis of testing procedures demonstrates a positive correlation between the percentage of code covered by tests – measured as statement, branch, and function coverage – and the ability of those tests to identify defects in LLM-generated code. Specifically, increases in test coverage consistently resulted in a measurable reduction in the number of identified bugs per 1000 lines of code. Data indicates that a coverage level below 70% yielded significantly higher defect rates, while coverage exceeding 90% showed diminishing returns in bug detection, suggesting an optimal balance for maximizing test effectiveness and ensuring code reliability.

The Algorithmic Imperative: Impact and Future Trajectories

The increasing reliance on Large Language Models (LLMs) for code generation necessitates a paradigm shift in software development practices, with rigorous testing emerging as a critical safeguard. This research underscores that simply generating code is insufficient; comprehensive evaluation is paramount to ensuring reliability and security. Traditional testing methods, while still relevant, must be augmented to address the unique challenges posed by LLM-generated code, which may exhibit subtle errors or vulnerabilities undetectable through conventional means. A robust testing strategy, encompassing both functional correctness and security considerations, is no longer optional but an essential component of the software development lifecycle when leveraging the power of LLMs for code synthesis. The study demonstrates that without such diligence, the potential benefits of LLM-based code generation are significantly diminished, and the risk of deploying flawed or insecure software increases substantially.

Comprehensive software reliability and security are demonstrably linked to the extent of test coverage, extending beyond simply exercising a wide range of code paths. Research indicates that achieving both breadth – ensuring many different functionalities are tested – and depth – rigorously testing individual functions with varied inputs, including edge cases and potential vulnerabilities – is crucial. Insufficient depth can leave critical flaws undetected, while a lack of breadth risks overlooking systemic issues arising from interactions between components. Therefore, developers should prioritize strategies that maximize both the variety and thoroughness of testing, ultimately reducing the likelihood of bugs, security breaches, and unreliable software performance. This balanced approach represents a fundamental shift towards proactive quality assurance in the age of increasingly complex codebases and automated code generation tools.

Investigations are shifting toward automating the creation of test cases, recognizing that current methods often struggle to keep pace with the complexity of code generated by Large Language Models. Researchers are exploring techniques like genetic algorithms and reinforcement learning to intelligently design test suites that maximize code coverage and effectively identify vulnerabilities. This automated approach promises to not only accelerate the testing process but also to discover edge cases and security flaws that might be missed by human-authored tests. Simultaneously, efforts are underway to refine the LLM-based code synthesis process itself, incorporating feedback from testing to iteratively improve the quality and reliability of the generated code, ultimately striving for a synergistic relationship between automated testing and intelligent code generation.

The pursuit of human-centered AI in terminology work, as detailed in the paper, echoes a fundamental principle of information theory. As Claude Shannon stated, “The most important thing in communication is to convey the meaning without loss.” This resonates deeply with the core idea of augmenting human capabilities, not replacing them. The article highlights the dangers of AI bias creeping into knowledge representation; Shannon’s emphasis on lossless communication implicitly demands fidelity to the intended meaning, a concept crucial for ethical AI. A system that introduces distortion, even subtly, fails to truly assist human understanding, violating the purity sought in elegant design.

The Path Forward

The pursuit of ‘human-centered’ artificial intelligence in terminology work, while laudable, reveals a persistent ambiguity. The term itself implies a malleability of the artificial to the human, a proposition that sidesteps the fundamental asymmetry. An algorithm, no matter how cleverly designed, operates within a closed logical system. To speak of aligning it with ‘human values’ is to project an inherently imprecise concept onto a realm demanding absolute precision. The true challenge lies not in making AI more human, but in rigorously defining the boundaries of its competence – and acknowledging what remains, irrevocably, outside its grasp.

Future work must move beyond empirical evaluations of ‘usefulness’ and focus instead on formal verification. Can these systems, given a specific terminology and a set of axioms, provably avoid the introduction of bias or logical contradiction? The current emphasis on mitigating bias after its emergence is a symptom of insufficient foundational rigor. A merely ‘robust’ system is still, ultimately, a flawed one.

The field risks becoming consumed by the illusion of progress-demonstrating that AI can assist with terminology work is trivial; demonstrating that it can do so correctly, and with guaranteed logical consistency, remains a considerable, and perhaps underestimated, undertaking. The elegance of a solution isn’t measured by its practical application, but by the purity of its mathematical foundation.

Original article: https://arxiv.org/pdf/2512.18859.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-12-23 19:25