arXiv subjects

Xueqi Dang

Publications and source records attributed to Xueqi Dang.

7 recordsLinked to original sources

arXiv2026

Semantic Drift in Bug Resolution: How Behavioral Signals Propagate from Reports to Tests and Patches

Desc2Fix is a framework for measuring semantic alignment between bug reports, triggering tests, and developer-written fixes. Alignment is operationalized through structured behavioral anchors (e.g., reproduction steps, API/exception cues, expected vs. actual behavior), deterministic similarity metrics (ROUGE, SBERT, CodeBERT, OpenAI embeddings), and LLM-based judgments grounded in coverage, correctness, and specificity. Our analysis covers 2,857 report-test-patch triplets from Defects4J and SWT-Bench using two widely adopted instruction-tuned LLMs from distinct model families. LLMs reliably extract structured signals (up to 90% completeness) and exhibit strong cross-model consistency, yielding a stable semantic input contract for downstream reasoning. However, alignment is highly representation-sensitive: lexical similarity alone is insufficient, full diffs provide the most stable basis for judging report-patch correspondence, and structured summaries trade surface overlap for stronger correspondence at the level of individual actions and entities. Across more than 182,000 LLM-based alignment judgments, both models exhibit systematic optimism relative to humans (1-2 points on 5-point scales) and only modest rank agreement, motivating bias-aware evaluation. Behavioral alignment is measurable but not reducible to similarity, and structured anchors combined with embedding-based proxies provide reproducible signals for ranking and filtering tests and patches. Desc2Fix could support more reliable test generation, fault localization, patch ranking, and bug report authoring.

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arXiv2026

On the Diffusion of Test Smells in LLM-Generated Unit Tests

LLMs promise to transform unit test generation from a manual burden into an automated solution. Yet, beyond metrics such as compilability or coverage, little is known about the quality of LLM-generated tests, particularly their susceptibility to test smells, design flaws that undermine readability and maintainability. This paper presents the first multi-benchmark, large-scale analysis of test smell diffusion in LLM-generated unit tests. We contrast LLM outputs with human-written suites (as the reference for real-world practices) and SBST-generated tests from EvoSuite (as the automated baseline), disentangling whether LLMs reproduce human-like flaws or artifacts of synthetic generation. Our study draws on 20,505 class-level suites from four LLMs (GPT-3.5, GPT-4, Mistral 7B, Mixtral 8x7B), 972 method-level cases from TestBench, 14,469 EvoSuite tests, and 779,585 human-written tests from 34,635 open-source Java projects. Using two complementary detection tools (TsDetect and JNose), we analyze prevalence, co-occurrence, and correlations with software attributes and generation parameters. Results show that LLM-generated tests consistently manifest smells such as Assertion Roulette and Magic Number Test, with patterns strongly influenced by prompting strategy, context length, and model scale. Comparisons reveal overlaps with human-written tests, raising concerns of potential data leakage from training corpora while EvoSuite exhibits distinct, generator-specific flaws. These findings highlight both the promise and the risks of LLM-based test generation, and call for the design of smell-aware generation frameworks, prompt engineering strategies, and enhanced detection tools to ensure maintainable, high-quality test code.

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arXiv2026

Humanizing Automatically Generated Unit Test Suites with LLM-Based Refactoring

Search-based test generation tools such as EvoSuite produce compilable and high-coverage unit tests at scale, but their suites are often hard to read and maintain. LLMs can generate more natural tests, yet direct generation remains brittle, with compilation rates of only 51-78% in our study. We introduce TestHumanizer, a hybrid SBST+LLM approach that uses LLMs as controlled refactoring layers over compilable SBST suites to improve naming, structure, and developer-oriented clarity while preserving behavior and compilation validity. We evaluate TestHumanizer on 350 classes from Defects4J and SF110. EvoSuite generates 15 suites per class, and each suite is refactored under three context configurations using gpt-4o and mistral-large-2407, yielding 31,500 refactorings. TestHumanizer reaches 88-98% compilation rates, close to EvoSuite's 100% baseline and clearly above direct LLM generation. Structural coverage is largely preserved, typically within 1-2 percentage points, and 86-95% of refactorings satisfy a composite faithful-refactoring threshold. Refactored suites also improve predicted readability, reduce control-flow and cognitive complexity, and mitigate structural smells. The summary-based setting offers the most robust trade-off, while long code-centric prompts are more prone to hallucination-induced failures. A developer study on 30 classes and 444 test methods confirms significant gains in perceived readability and willingness to adopt, with Wilcoxon p less than 0.01 and substantial inter-rater agreement. Overall, LLMs are most effective not as standalone generators but as validation-gated refinement layers over robust SBST outputs.

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arXiv2025

Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness

Large Language Models (LLMs) are increasingly used to refactor unit tests, improving readability and structure while preserving behavior. Evaluating such refactorings, however, remains difficult: metrics like CodeBLEU penalize beneficial renamings and edits, while semantic similarities overlook readability and modularity. We propose CTSES, a first step toward human-aligned evaluation of refactored tests. CTSES combines CodeBLEU, METEOR, and ROUGE-L into a composite score that balances semantics, lexical clarity, and structural alignment. Evaluated on 5,000+ refactorings from Defects4J and SF110 (GPT-4o and Mistral-Large), CTSES reduces false negatives and provides more interpretable signals than individual metrics. Our emerging results illustrate that CTSES offers a proof-of-concept for composite approaches, showing their promise in bridging automated metrics and developer judgments.

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arXiv2025

Human-Aligned Code Readability Assessment with Large Language Models

Code readability is crucial for software comprehension and maintenance, yet difficult to assess at scale. Traditional static metrics often fail to capture the subjective, context-sensitive nature of human judgments. Large Language Models (LLMs) offer a scalable alternative, but their behavior as readability evaluators remains underexplored. We introduce CoReEval, the first large-scale benchmark for evaluating LLM-based code readability assessment, comprising over 1.4 million model-snippet-prompt evaluations across 10 state of the art LLMs. The benchmark spans 3 programming languages (Java, Python, CUDA), 2 code types (functional code and unit tests), 4 prompting strategies (ZSL, FSL, CoT, ToT), 9 decoding settings, and developer-guided prompts tailored to junior and senior personas. We compare LLM outputs against human annotations and a validated static model, analyzing numerical alignment (MAE, Pearson's, Spearman's) and justification quality (sentiment, aspect coverage, semantic clustering). Our findings show that developer-guided prompting grounded in human-defined readability dimensions improves alignment in structured contexts, enhances explanation quality, and enables lightweight personalization through persona framing. However, increased score variability highlights trade-offs between alignment, stability, and interpretability. CoReEval provides a robust foundation for prompt engineering, model alignment studies, and human in the loop evaluation, with applications in education, onboarding, and CI/CD pipelines where LLMs can serve as explainable, adaptable reviewers.

cs.SE↗

arXiv2025

Rethinking Cognitive Complexity for Unit Tests: Toward a Readability-Aware Metric Grounded in Developer Perception

Automatically generated unit tests-from search-based tools like EvoSuite or LLMs-vary significantly in structure and readability. Yet most evaluations rely on metrics like Cyclomatic Complexity and Cognitive Complexity, designed for functional code rather than test code. Recent studies have shown that SonarSource's Cognitive Complexity metric assigns near-zero scores to LLM-generated tests, yet its behavior on EvoSuite-generated tests and its applicability to test-specific code structures remain unexplored. We introduce CCTR, a Test-Aware Cognitive Complexity metric tailored for unit tests. CCTR integrates structural and semantic features like assertion density, annotation roles, and test composition patterns-dimensions ignored by traditional complexity models but critical for understanding test code. We evaluate 15,750 test suites generated by EvoSuite, GPT-4o, and Mistral Large-1024 across 350 classes from Defects4J and SF110. Results show CCTR effectively discriminates between structured and fragmented test suites, producing interpretable scores that better reflect developer-perceived effort. By bridging structural analysis and test readability, CCTR provides a foundation for more reliable evaluation and improvement of generated tests. We publicly release all data, prompts, and evaluation scripts to support replication.

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arXiv2024

An Empirical Study of AI Techniques in Mobile Applications

The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. In this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: 1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; 2) Framework and model analysis, where we analyze AI framework usage and AI model protection; 3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R\&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

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