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版本更新 · v2arXiv:2502.04953

Automated Exploit and Security Test Generation: A Systematic Literature Review

Quang-Cuong Bui, Emanuele Iannone, Maria Camporese, Torge Hinrichs, Catherine Tony, László Tóth, Fabio Palomba, Péter Hegedűs 等 10 位作者

原文摘要

Static analysis techniques are often insufficient to determine whether a reported security vulnerability is exploitable and actually poses a real security risk to a software application. Dynamic analysis addresses this limitation by executing the target application with carefully crafted inputs that trigger or reproduce a given vulnerability. Over the past two decades, automatic exploit generation (AEG) and automatic security test generation (ASTG) have emerged as active research areas in cybersecurity and software engineering. This paper demystifies the field by establishing consistent terminology and presenting a systematic literature review (SLR) of research published in the last two decades. Starting from an initial set of 3,309 publications obtained from academic literature search engines, we selected and analyzed 71 papers comprising 67 distinct studies. We systematically compare existing AEG and ASTG approaches with respect to (i) the targeted software and vulnerability types, (ii) the generated artifacts, (iii) the underlying generation techniques and tools employed, (iv) the required inputs, (v) the evaluation methodologies, and (vi) implementation and tool availability.

cs.CR · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03231

Specification-Driven Governance for Fairness-by-Design in Agentic Software Engineering: How Far Can We Go?

Alfonso Cannavale, Alessandra Parziale, Valeria Pontillo, Patrizio Pelliccione, Andrea De Lucia, Fabio Palomba

原文摘要

Agentic AI is shifting developers' responsibilities from software implementation to software governance. While this paradigm improves productivity, it also raises challenges in ensuring that autonomous coding agents preserve developers' intentions. Among these, operationalizing fairness-by-design is largely unexplored, as existing approaches provide limited support for representing and propagating fairness requirements throughout agentic development workflows. In this paper, we investigate whether specification-driven development can operationalize fairness-by-design in the agentic development of machine learning systems. We study three coding agents on four machine learning applications, using four prompts that progressively operationalize fairness through nine Fairness Specification Elements, and analyze the traceability of fairness requirements across the generated artifacts, the fairness-accuracy trade-off of the resulting models, and the agents' design decisions and rationales. The results show that when fairness is operationalized, agents capture almost all fairness requirements in the specification (97.22%) and preserve most of them in the code (82.41%). However, the resulting models are not significantly fairer, as agents implement fairness as a post-hoc audit that neither shapes training nor prevents model delivery. Specifications thus make fairness traceable, but not enforceable, calling for fairness specifications that state what agents should do when requirements are not met, and for workflows that make the fairness decisions agents delegate to developers explicit.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36363

Reliability-Aware Short-Term Glycemic Risk Prediction Using an Anchor-Based Comparative Framework of Sequence, Multimodal, and State-Updating Digital-Twin Models

Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba

原文摘要

Anticipating short-term hypoglycemic and hyperglycemic risk is essential for proactive diabetes management. However, existing machine learning approaches often use inconsistent evaluation protocols and emphasize predictive performance over reliability. This study introduces an anchor-based benchmarking framework integrating longitudinal glucose sequences and multimodal patient information. Using the T1DiabetesGranada dataset, 5,503,441 standardized prediction anchors were constructed from 736 individuals with type 1 diabetes to evaluate hypoglycemia and hyperglycemia prediction at 30-, 60-, and 120-minute horizons. Three neural architectures, a Hybrid LSTM-Transformer, a Multimodal Digital Twin, and a State-Updating Digital Twin with gap-aware longitudinal state progression, were compared under identical patient-level validation protocols. Beyond predictive performance, the evaluation examined calibration, classification thresholds, explanation reliability, cross-model disagreement, and robustness to controlled perturbations. No statistically significant predictive differences were observed after Holm correction, although the models exhibited complementary reliability characteristics. The Multimodal Digital Twin achieved the lowest mean Brier score, while the State-Updating Digital Twin demonstrated the lowest mean expected calibration error (ECE-10). The Hybrid LSTM-Transformer showed strong explanation faithfulness and stability under its temporal attribution protocol. These findings highlight the importance of evaluating digital-twin models beyond predictive accuracy and emphasize the need for reliability assessment and external validation before clinical deployment.

cs.LG · cs.AI

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arXiv ↗PDF ↗