Alif Al Hasan

Alif Al Hasan, Sumon Biswas

Autonomous coding agents built on large language models (LLMs) are rapidly being integrated into development workflows, yet their operational safety properties remain poorly understood beyond evaluations of explicitly malicious inputs. In practice, high-impact failures arise during benign, goal-directed use through environment breakage, fabricated success reports, etc. that current benchmarks do not capture. What categories of operational safety failures actually occur when coding agents are used for everyday development tasks and what is their impact? We present an incident-driven empirical study grounded in two complementary evidence streams. We screen 68,816 papers from 22 premier venues, curating 185 safety-relevant studies, and mine 16,586 GitHub issues from widely deployed LLM-powered coding tools, manually confirming 547 genuine safety failures. Applying systematic open coding over both corpora, we derive a multi-dimensional safety taxonomy of 33 operational risk types organized across seven dimensions, and annotate each incident with contributing factors, task context, severity, and downstream impact. Our findings show that coding-agent failures are often severe, with 326 of 547 incidents rated high or critical. The dominant risks are constraint violations, destructive operations, authorization bypasses, and deception, and over 65% of incidents arise in bug fixing and setup or configuration, patterns largely missing from prior literature. These results have direct implications for SE tool designers and benchmark developers: guardrails must go beyond adversarial-prompt defenses to enforce environmental constraints, failure transparency, and safe-halt behaviors.

Alif Al Hasan

As Large Language Models (LLMs) are integrated into global software systems, ensuring equitable safety guardrails is a critical requirement. Current fairness evaluations predominantly measure bias observationally, a methodology confounded by the inherent toxicity of topics naturally paired with specific demographics in testing datasets. This study introduces a Probabilistic Graphical Model (PGM) framework to audit LLM safety mechanisms causally. By applying Pearl's do-operator, we mathematically isolate the causal effect of injecting a cultural demographic into a prompt. We conduct a large-scale empirical analysis across seven instruction-tuned models spanning diverse origins: the United States (Llama-3.1-8B, Gemma-2-9B), Europe (Mistral-7B-v0.3), the UAE (Falcon3-7B), China (Qwen2.5-7B, DeepSeek-7B), and India (Airavata-7B). Utilizing two distinct datasets (ToxiGen and BOLD), the findings reveal a disparity between observational and interventional bias, demonstrating that standard fairness metrics can overestimate demographic bias by failing to account for context toxicity. Furthermore, the causal probabilities indicate distinct alignment trends: Western models exhibit higher causal refusal rates for specific demographic groups, whereas Eastern models demonstrate low overall intervention rates with targeted sensitivities toward regional demographics. We discuss the implications of these biases, highlighting how demographic-sensitive over-triggering restricts benign discourse in downstream applications.

Subarna Saha, Alif Al Hasan, Fariha Tanjim Shifat et al.

Novice programmers often struggle to comprehend code due to vague naming, deep nesting, and poor structural organization. While explanations may offer partial support, they typically do not restructure the code itself. We propose code refactoring as cognitive scaffolding, where cognitively guided refactoring automatically restructures code to improve clarity. We operationalize this in CDDRefactorER, an automated approach grounded in Cognitive-Driven Development that constrains transformations to reduce control-flow complexity while preserving behavior and structural similarity. We evaluate CDDRefactorER using two benchmark datasets (MBPP and APPS) against two models (gpt-5-nano and kimi-k2), and a controlled human-subject study with novice programmers. Across datasets and models, CDDRefactorER reduces refactoring failures by 54-71% and substantially lowers the likelihood of increased Cyclomatic and Cognitive complexity during refactoring, compared to unconstrained prompting. Results from the human study show consistent improvements in novice code comprehension, with function identification increasing by 31.3% and structural readability by 22.0%. The findings suggest that cognitively guided refactoring offers a practical and effective mechanism for enhancing novice code comprehension.