Clinical evaluation of novel deep learning‐based auto‐segmentation software: Utility and potential pitfalls

Model-Based Agentic Software Engineering

MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.

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.

From design to verification: applying the software quality and test plans to the SST telescope control system for CTA

HawkEye: Web Vulnerability Analysis and Security Audit Tool

HawkEye is introduced, a modular, web-based vulnerability auditing platform designed to streamline security analysis by integrating multiple scanning tools within a unified dashboard and illustrates how consolidated reporting improves vulnerability prioritization for development teams.

Application for Tracking Bugs in Software Development: Improving Project Management Efficiency

By streamlining workflows and fostering collaboration, this platform offers a scalable, cost- effective solution for SMEs and contributes to software engineering by demonstrating how integrated technologies can modernize development processes in resource limited contexts, with potential for broader adoption in Albania and beyond.

Prospective evaluation of hearing aid fitting adjusted toward one-third functional gain in patients with sensorineural hearing loss.

OBJECTIVE To prospectively evaluate whether modifying DSL version 5-based hearing aid (HA) fittings by adjusting gain on HA fitting software so that measured functional gain (FG) approached a one-third gain (1/3G) target could provide appropriate fitting outcomes in patients with sensorineural hearing loss. METHODS Twenty-four patients (48 ears) with bilateral sensorineural hearing loss underwent initial HA fitting using the DSL version 5 prescription formula. FG was measured at 250-4000 Hz, and HA gain was adjusted on HA fitting software so that FG approached the target 1/3 G. Speech discrimination scores at 65 and 80 dB SPL were evaluated after a two-week trial period using the 67-S Japanese monosyllable word list. Based on speech discrimination test results, ears were classified as well-fitting or non-well-fitting. FG values were compared between the two groups. RESULTS Twenty-one patients (42 ears) completed the study. Thirty-one ears (73%) were classified as well-fitting. Although HA gain was adjusted toward the target 1/3 G, measured FG values at 250 and 500 Hz remained lower than the target values. In well-fitting ears, low-frequency FG values were lower than the target 1/3 G, whereas FG at 2000 Hz was close to the target value. In contrast, non-well-fitting ears showed low-frequency FG values closer to the target 1/3 G, whereas FG values at 2000 and 4000 Hz remained below the target values. CONCLUSIONS Although HAs adjusted toward a 1/3 G target did not achieve the intended FG values, particularly at low frequencies, relatively favorable fitting outcomes were obtained in approximately three-quarters of the ears. In well-fitting ears, low-frequency FG remained below the target 1/3 G, whereas FG in the mid-frequency range around 2000 Hz was close to the target value. These findings provide a basis for future prospective studies to clarify how these FG characteristics should be applied to optimize HA adjustment.