arXiv subjects

Barbara Russo

Publications and source records attributed to Barbara Russo.

At least 19 recordsLinked to original sources

arXiv2026

Vibe Coding: An Experiment with Test-Driven Development

Context: Conversational Large Language Models (CLLMs) can automatically generate code by collaborating with users through natural language. However, poor collaboration can lead to poor quality output. Objective: This exploratory study aims to investigate how humans and CLLMs can collaborate as peers through vibe coding, an approach that integrates principles from prompt engineering, agile design, and human-AI co-creation to enhance collaboration. Method: We designed four interaction models representing different collaboration patterns in the software development process: the solo model (human-only development), the collaborative model (human-CLLM collaboration), the fully automated model (development autonomously performed by a CLLM), and the agentic model (development autonomously performed by the MetaGPT~X platform). Based on these models, we implemented corresponding Test-Driven Development (TDD) workflows using structured prompts and Python scripts. We then conducted a controlled pre-experimental study with TDD professionals to compare the solo and collaborative workflows. In addition, we performed repeated exploratory executions of fully automated and agentic workflows on the same development tasks to obtain complementary evidence. Results: Our findings suggest that the choice of interaction model should depend on the development objective. Agentic workflows are best suited for rapid development and functionally correct production code but may introduce additional implementation complexity. However, they may also introduce additional implementation decisions that are not explicitly required by the functional specifications, resulting in untested decision points. In contrast, collaborative workflows produce higher-quality, better-organized test suites. Conclusions: Our work explored how...

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arXiv2026

Predicting known Vulnerabilities from Attack News: A Transformer-Based Approach

Identifying the vulnerabilities exploited during cyberattacks is essential for enabling timely responses and effective mitigation in software security. This paper directly examines the process of predicting software vulnerabilities, specifically Common Vulnerabilities and Exposures (CVEs), from unstructured descriptions of attacks reported in cybersecurity news articles. We propose a semantic similarity-based approach utilizing the multi-qa-mpnet-base-dot-v1 (MPNet) sentence transformer model to generate a ranked list of the most likely CVEs corresponding to each news report. To assess the accuracy of the predicted vulnerabilities, we implement four complementary validation methods: filtering predictions based on similarity thresholds, conducting manual validation, performing semantic comparisons with the first vulnerability explicitly mentioned in each report, and comparing against all CVEs referenced within the report. Experimental results, drawn from a dataset of 100 SecurityWeek news articles, demonstrate that the model attains a precision of 81 percent when employing threshold-based filtering. Manual evaluations report that 70 percent of the predictions are relevant, while comparisons with the initially mentioned CVEs reveal agreement rates of 80 percent with the first listed vulnerability and 78 percent across all referenced CVEs. In 57 percent of the news reports analyzed, at least one predicted vulnerability precisely matched a CVE-ID mentioned in the article. These findings underscore the model's potential to facilitate automated vulnerability identification from real-world cyberattack news reports.

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arXiv2026

Reading Between the Code Lines: On the Use of Self-Admitted Technical Debt for Security Analysis

Static Analysis Tools (SATs) are central to security engineering activities, as they enable early identification of code weaknesses without requiring execution. However, their effectiveness is often limited by high false-positive rates and incomplete coverage of vulnerability classes. At the same time, developers frequently document security-related shortcuts and compromises as Self-Admitted Technical Debt (SATD) in software artifacts, such as code comments. While prior work has recognized SATD as a rich source of security information, it remains unclear whether -and in what ways- it is utilized during SAT-aided security analysis. OBJECTIVE: This work explores whether and how the security-related information encoded in SATD provides complementary security insights to SATs. METHOD: We followed a mixed-methods approach comprising (i) the analysis of a manually curated, SATD-annotated vulnerability dataset using three SATs and (ii) an online survey involving 72 security-aware software practitioners. RESULTS: The selected SATs flagged 114 of the 135 validated Security-related SATD instances (SSATD), yet the overlap between SAT-derived and manually mapped Common Weakness Enumeration (CWE) identifiers was only 6.42%, indicating that both sources often expose different kinds of security information. In particular, SSATD captured several dynamic and context-dependent weakness types that SATs commonly overlook or struggle to detect. Survey responses further indicate that practitioners rely on SSATD to contextualize SAT findings by understanding their impact, root causes, and potential fixes. IMPLICATIONS: Our findings suggest that SSATD constitutes a valuable and cost-effective source of complementary security knowledge that can support the interpretation, prioritization, and further assessment of SAT findings.

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arXiv2025

On the cybersecurity of LoRaWAN-based system: a Smart-Lighting case study

Cyber-physical systems and the Internet of Things (IoT) are key technologies in the Industry 4.0 vision. They incorporate sensors and actuators to interact with the physical environment. However, when creating and interconnecting components to form a heterogeneous smart systems architecture, these face challenges in cybersecurity. This paper presents an experimental investigation of architectural configurations for a LoRaWAN-based Smart-Lighting project, aimed at verifying and improving the system's robustness against attacks. We assess the system's robustness in a series of iterative experiments conducted both in-vitro and on-site. The results show that most attacks on a LoRaWAN network are unsuccessful, also highlighting unresolved issues with the installed products. The most successful attacks are high-power jamming attacks within a few meters of the target, which, in the case of gateways, can be mitigated through gateway redundancy.

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arXiv2025

Cross-Domain Evaluation of Transformer-Based Vulnerability Detection on Open & Industry Data

Deep learning solutions for vulnerability detection proposed in academic research are not always accessible to developers, and their applicability in industrial settings is rarely addressed. Transferring such technologies from academia to industry presents challenges related to trustworthiness, legacy systems, limited digital literacy, and the gap between academic and industrial expertise. For deep learning in particular, performance and integration into existing workflows are additional concerns. In this work, we first evaluate the performance of CodeBERT for detecting vulnerable functions in industrial and open-source software. We analyse its cross-domain generalisation when fine-tuned on open-source data and tested on industrial data, and vice versa, also exploring strategies for handling class imbalance. Based on these results, we develop AI-DO(Automating vulnerability detection Integration for Developers' Operations), a Continuous Integration-Continuous Deployment (CI/CD)-integrated recommender system that uses fine-tuned CodeBERT to detect and localise vulnerabilities during code review without disrupting workflows. Finally, we assess the tool's perceived usefulness through a survey with the company's IT professionals. Our results show that models trained on industrial data detect vulnerabilities accurately within the same domain but lose performance on open-source code, while a deep learner fine-tuned on open data, with appropriate undersampling techniques, improves the detection of vulnerabilities.

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arXiv2025

From Attack Descriptions to Vulnerabilities: A Sentence Transformer-Based Approach

In the domain of security, vulnerabilities frequently remain undetected even after their exploitation. In this work, vulnerabilities refer to publicly disclosed flaws documented in Common Vulnerabilities and Exposures (CVE) reports. Establishing a connection between attacks and vulnerabilities is essential for enabling timely incident response, as it provides defenders with immediate, actionable insights. However, manually mapping attacks to CVEs is infeasible, thereby motivating the need for automation. This paper evaluates 14 state-of-the-art (SOTA) sentence transformers for automatically identifying vulnerabilities from textual descriptions of attacks. Our results demonstrate that the multi-qa-mpnet-base-dot-v1 (MMPNet) model achieves superior classification performance when using attack Technique descriptions, with an F1-score of 89.0, precision of 84.0, and recall of 94.7. Furthermore, it was observed that, on average, 56% of the vulnerabilities identified by the MMPNet model are also represented within the CVE repository in conjunction with an attack, while 61% of the vulnerabilities detected by the model correspond to those cataloged in the CVE repository. A manual inspection of the results revealed the existence of 275 predicted links that were not documented in the MITRE repositories. Consequently, the automation of linking attack techniques to vulnerabilities not only enhances the detection and response capabilities related to software security incidents but also diminishes the duration during which vulnerabilities remain exploitable, thereby contributing to the development of more secure systems.

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arXiv2025

Leveraging multi-task learning to improve the detection of SATD and vulnerability

Multi-task learning is a paradigm that leverages information from related tasks to improve the performance of machine learning. Self-Admitted Technical Debt (SATD) are comments in the code that indicate not-quite-right code introduced for short-term needs, i.e., technical debt (TD). Previous research has provided evidence of a possible relationship between SATD and the existence of vulnerabilities in the code. In this work, we investigate if multi-task learning could leverage the information shared between SATD and vulnerabilities to improve the automatic detection of these issues. To this aim, we implemented VulSATD, a deep learner that detects vulnerable and SATD code based on CodeBERT, a pre-trained transformers model. We evaluated VulSATD on MADE-WIC, a fused dataset of functions annotated for TD (through SATD) and vulnerability. We compared the results using single and multi-task approaches, obtaining no significant differences even after employing a weighted loss. Our findings indicate the need for further investigation into the relationship between these two aspects of low-quality code. Specifically, it is possible that only a subset of technical debt is directly associated with security concerns. Therefore, the relationship between different types of technical debt and software vulnerabilities deserves future exploration and a deeper understanding.

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arXiv2025

Optimizing Deep Learning Models to Address Class Imbalance in Code Comment Classification

Developers rely on code comments to document their work, track issues, and understand the source code. As such, comments provide valuable insights into developers' understanding of their code and describe their various intentions in writing the surrounding code. Recent research leverages natural language processing and deep learning to classify comments based on developers' intentions. However, such labelled data are often imbalanced, causing learning models to perform poorly. This work investigates the use of different weighting strategies of the loss function to mitigate the scarcity of certain classes in the dataset. In particular, various RoBERTa-based transformer models are fine-tuned by means of a hyperparameter search to identify their optimal parameter configurations. Additionally, we fine-tuned the transformers with different weighting strategies for the loss function to address class imbalances. Our approach outperforms the STACC baseline by 8.9 per cent on the NLBSE'25 Tool Competition dataset in terms of the average F1$_c$ score, and exceeding the baseline approach in 17 out of 19 cases with a gain ranging from -5.0 to 38.2. The source code is publicly available at https://github.com/moritzmock/NLBSE2025.

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arXiv2024

MADE-WIC: Multiple Annotated Datasets for Exploring Weaknesses In Code

In this paper, we present MADE-WIC, a large dataset of functions and their comments with multiple annotations for technical debt and code weaknesses leveraging different state-of-the-art approaches. It contains about 860K code functions and more than 2.7M related comments from 12 open-source projects. To the best of our knowledge, no such dataset is publicly available. MADE-WIC aims to provide researchers with a curated dataset on which to test and compare tools designed for the detection of code weaknesses and technical debt. As we have fused existing datasets, researchers have the possibility to evaluate the performance of their tools by also controlling the bias related to the annotation definition and dataset construction. The demonstration video can be retrieved at https://www.youtube.com/watch?v=GaQodPrcb6E.

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arXiv2024

Cybersecurity Defenses: Exploration of CVE Types through Attack Descriptions

Vulnerabilities in software security can remain undiscovered even after being exploited. Linking attacks to vulnerabilities helps experts identify and respond promptly to the incident. This paper introduces VULDAT, a classification tool using a sentence transformer MPNET to identify system vulnerabilities from attack descriptions. Our model was applied to 100 attack techniques from the ATT&CK repository and 685 issues from the CVE repository. Then, we compare the performance of VULDAT against the other eight state-of-the-art classifiers based on sentence transformers. Our findings indicate that our model achieves the best performance with F1 score of 0.85, Precision of 0.86, and Recall of 0.83. Furthermore, we found 56% of CVE reports vulnerabilities associated with an attack were identified by VULDAT, and 61% of identified vulnerabilities were in the CVE repository.

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arXiv2024

Where do developers admit their security-related concerns?

Developers use different means to document the security concerns of their code. Because of all of these opportunities, they may forget where the information is stored, or others may not be aware of it, and leave it unmaintained for so long that it becomes obsolete, if not useless. In this work, we analyzed different sources of code documentation from four large-scale, real-world, open-source projects in an industrial setting to understand where developers report their security concerns. In particular, we manually inspected 2.559 instances taken from source code comments, commit messages, and issue trackers. Overall, we found that developers prefer to document security concerns in source code comments and issue trackers. We also found that the longer the comments stay unfixed, the more likely they remain unfixed. Thus, to create awareness among developers, we implemented a pipeline to remind them about the introduction or removal of comments pointing to a security problem.

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arXiv2024

Generative AI for Test Driven Development: Preliminary Results

Test Driven Development (TDD) is one of the major practices of Extreme Programming for which incremental testing and refactoring trigger the code development. TDD has limited adoption in the industry, as it requires more code to be developed and experienced developers. Generative AI (GenAI) may reduce the extra effort imposed by TDD. In this work, we introduce an approach to automatize TDD by embracing GenAI either in a collaborative interaction pattern in which developers create tests and supervise the AI generation during each iteration or a fully-automated pattern in which developers only supervise the AI generation at the end of the iterations. We run an exploratory experiment with ChatGPT in which the interaction patterns are compared with the non-AI TDD regarding test and code quality and development speed. Overall, we found that, for our experiment and settings, GenAI can be efficiently used in TDD, but it requires supervision of the quality of the produced code. In some cases, it can even mislead non-expert developers and propose solutions just for the sake of the query.

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arXiv2024

Modeling Resilience of Collaborative AI Systems

A Collaborative Artificial Intelligence System (CAIS) performs actions in collaboration with the human to achieve a common goal. CAISs can use a trained AI model to control human-system interaction, or they can use human interaction to dynamically learn from humans in an online fashion. In online learning with human feedback, the AI model evolves by monitoring human interaction through the system sensors in the learning state, and actuates the autonomous components of the CAIS based on the learning in the operational state. Therefore, any disruptive event affecting these sensors may affect the AI model's ability to make accurate decisions and degrade the CAIS performance. Consequently, it is of paramount importance for CAIS managers to be able to automatically track the system performance to understand the resilience of the CAIS upon such disruptive events. In this paper, we provide a new framework to model CAIS performance when the system experiences a disruptive event. With our framework, we introduce a model of performance evolution of CAIS. The model is equipped with a set of measures that aim to support CAIS managers in the decision process to achieve the required resilience of the system. We tested our framework on a real-world case study of a robot collaborating online with the human, when the system is experiencing a disruptive event. The case study shows that our framework can be adopted in CAIS and integrated into the online execution of the CAIS activities.

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arXiv2023

CAIS-DMA: A Decision-Making Assistant for Collaborative AI Systems

A Collaborative Artificial Intelligence System (CAIS) is a cyber-physical system that learns actions in collaboration with humans in a shared environment to achieve a common goal. In particular, a CAIS is equipped with an AI model to support the decision-making process of this collaboration. When an event degrades the performance of CAIS (i.e., a disruptive event), this decision-making process may be hampered or even stopped. Thus, it is of paramount importance to monitor the learning of the AI model, and eventually support its decision-making process in such circumstances. This paper introduces a new methodology to automatically support the decision-making process in CAIS when the system experiences performance degradation after a disruptive event. To this aim, we develop a framework that consists of three components: one manages or simulates CAIS's environment and disruptive events, the second automates the decision-making process, and the third provides a visual analysis of CAIS behavior. Overall, our framework automatically monitors the decision-making process, intervenes whenever a performance degradation occurs, and recommends the next action. We demonstrate our framework by implementing an example with a real-world collaborative robot, where the framework recommends the next action that balances between minimizing the recovery time (i.e., resilience), and minimizing the energy adverse effects (i.e., greenness).

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arXiv2023

GResilience: Trading Off Between the Greenness and the Resilience of Collaborative AI Systems

A Collaborative Artificial Intelligence System (CAIS) works with humans in a shared environment to achieve a common goal. To recover from a disruptive event that degrades its performance and ensures its resilience, a CAIS may then need to perform a set of actions either by the system, by the humans, or collaboratively together. As for any other system, recovery actions may cause energy adverse effects due to the additional required energy. Therefore, it is of paramount importance to understand which of the above actions can better trade-off between resilience and greenness. In this in-progress work, we propose an approach to automatically evaluate CAIS recovery actions for their ability to trade-off between the resilience and greenness of the system. We have also designed an experiment protocol and its application to a real CAIS demonstrator. Our approach aims to attack the problem from two perspectives: as a one-agent decision problem through optimization, which takes the decision based on the score of resilience and greenness, and as a two-agent decision problem through game theory, which takes the decision based on the payoff computed for resilience and greenness as two players of a cooperative game.

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arXiv2023

The BET project: Behavior-enabled IoT

IoT is changing the way Internet is used due to the availability of a large amount of data timely collected from every-day life objects. Designing applications in this new scenario poses new challenges. This extended abstract discusses them and presents the objective of the BeT project whose main aim is to introduce a reference architecture, a conceptual framework, and related techniques to design behavior-enabled IoT systems and applications.

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arXiv2022

WeakSATD: Detecting Weak Self-admitted Technical Debt

Speeding up development may produce technical debt, i.e., not-quite-right code for which the effort to make it right increases with time as a sort of interest. Developers may be aware of the debt as they admit it in their code comments. Literature reports that such a self-admitted technical debt survives for a long time in a program, but it is not yet clear its impact on the quality of the code in the long term. We argue that self-admitted technical debt contains a number of different weaknesses that may affect the security of a program. Therefore, the longer a debt is not paid back the higher is the risk that the weaknesses can be exploited. To discuss our claim and rise the developers' awareness of the vulnerability of the self-admitted technical debt that is not paid back, we explore the self-admitted technical debt in the Chromium C-code to detect any known weaknesses. In this preliminary study, we first mine the Common Weakness Enumeration repository to define heuristics for the automatic detection and fix of weak code. Then, we parse the C-code to find self-admitted technical debt and the code block it refers to. Finally, we use the heuristics to find weak code snippets associated to self-admitted technical debt and recommend their potential mitigation to developers. Such knowledge can be used to prioritize self-admitted technical debt for repair. A prototype has been developed and applied to the Chromium code. Initial findings report that 55\% of self-admitted technical debt code contains weak code of 14 different types.

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arXiv2021

Collaborative Artificial Intelligence Needs Stronger Assurances Driven by Risks

Collaborative AI systems (CAISs) aim at working together with humans in a shared space to achieve a common goal. This critical setting yields hazardous circumstances that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements, domain-specific standards and regulations is of greatest importance. Only few scale impact has been reported so far for such systems since much work remains to manage possible risks. We identify emerging problems in this context and then we report our vision, as well as the progress of our multidisciplinary research team composed of software/systems, and mechatronics engineers to develop a risk-driven assurance process for CAISs.

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