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AI-as-a-Judge for software quality assessment: Consistency, attitude effects, and complexity sensitivity in multi-agent code evaluation

Sherlinford Chou
Aug 2026 · Software quality journal · Vol 34 · 0 citations · 42 references
DOI Semantic Scholar
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#software testing Preprint Aug 2026

Evaluating Inference-Time Defenses Against Package Hallucination in LLM-Generated Code

LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.

Albérick Euraste Djiré, Iyiola E. Olatunji, Melissa Tessa et al. · 1 citation
#software testing Review Aug 2026

ChatGPT Solves All Tested Qiskit Homework Assignments

This study examined whether introductory Qiskit homework could remain autogradable while requiring students to run, review, and discuss results rather than banning AI.

A. Kaltchenko, Gurnivaj Tiwana · 0 citations
#software testing Sep 2026

Mixed Reality Glasses Image Translocation for Binocular Diplopia.

This prototype MRG image translocation software was helpful to 69% of patients with binocular diplopia, but limited by large angle strabismus because of the limited instrument field of view.

Edsel B Ing, Kevin Sha, Sarosh Dandoti et al. · 0 citations
#software testing Open access Aug 2026

Multi-Disease Prediction Using Machine Learning: A Web-Based Diagnostic Support System for Diabetes, Heart Disease, and Parkinson\'s Disease

A diagnostic support system based on a unified web platform that classifies patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical supervised learning algorithms is presented.

Vedamurthy D R, Dr. Anup Ritti, A. Bibi et al. · 0 citations
#software testing Preprint Aug 2026

Benchmarking the Titans: A Multi-Dimensional Empirical Evaluation of LLM Code Generation Quality in the .NET Ecosystem

Evaluating Large Language Model (LLM) code generation quality requires examining not just whether the generated code is correct, but whether it is maintainable, efficient, and stylistically sound, all of which are qualities of direct importance to software engineering practitioners. Existing benchmarks reduce evaluation to a single Pass@k metric, which obscures critical trade-offs between functional correctness and structural quality. A further limitation is the near-exclusive focus on Python, leaving enterprise-relevant ecosystems such as C# and .NET without dedicated evaluation. This paper presents an automated, multi-dimensional evaluation framework for C# code generation, applying it to four state-of-the-art LLMs: GPT, Gemini, Claude, and Grok. We conduct a controlled experiment across 85 algorithmic tasks derived from HumanEval, generating and evaluating 340 solutions in total, in which each solution is assessed across three independent dimensions: functional correctness via automated unit testing, static code quality via Roslyn AST analysis, and runtime efficiency via adversarial BenchmarkDotNet profiling. Our central finding reveals a substantial gap between correctness and quality attributes (Pearson r = 0.075), demonstrating that Pass@k rankings systematically misrepresent the full LLM performance profile in software engineering contexts. We further characterize GPT's bimodal failure behavior.

Seyed Mohammad Mahdi Ghalandarian, Majid Bazargani, Masoumeh Taromirad · 0 citations

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