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Item type:Publication, Access status: with files , Discrete prompt optimization using genetic algorithm for secure Python code generationLarge language models (LLMs) have become powerful tools that enable novice developers to generate production-level code. However, research has highlighted the security risks associated with such code generation, due to the high volume of generated software vulnerabilities. Recent studies have explored various techniques for automatically optimizing prompts to elicit desired responses from LLMs. Among these methods, Genetic Algorithms (GAs), which search for optimal solutions by evolving an initial population of candidates through iterative mutations, have gained attention as a lightweight and effective prompt optimization approach that does not require large datasets or access to model weights. However, their potential has not yet been examined in the context of secure code generation. In this paper, we use GA to develop a discrete prompt optimization pipeline specifically designed for secure code generation. We introduce two domain-specific prompt mutation techniques and assess how incorporating these security-focused mutations alongside general-purpose techniques, such as back translation and paraphrasing, affects the security of Python code generated by LLMs. Results demonstrate that our security-specific mutation techniques led to prompts with richer security context compared to the generic mutation techniques. Furthermore, combining these techniques with generic mutations substantially reduced the number of security weaknesses in the LLM-generated code. We also observed that prompts optimized for a particular LLM tend to perform best on that same model, highlighting the importance of model-specific prompt optimization.Publicationtype: Journal Article37 19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Comparison of static analysis architecture recovery tools for microservice applications(Springer, 2025-10-01); ; ; ; Architecture recovery tools help software engineers obtain an overview of the structure of their software systems during all phases of the software development life cycle. This is especially important for microservice applications because they consist of multiple interacting microservices, which makes it more challenging to oversee the architecture. Various tools and techniques for architecture recovery (also called architecture reconstruction) have been presented in academic and gray literature sources, but no overview and comparison of their accuracy exists. This paper presents the results of a multivocal literature review with the goal of identifying architecture recovery tools for microservice applications and a comparison of the identified tools’ architectural recovery accuracy. We focused on static tools since they can be integrated into fast-paced CI/CD pipelines. 13 such tools were identified from the literature and nine of them could be executed and compared on their capability of detecting different system characteristics. The best-performing tool exhibited an overall F1-score of 0.86. Additionally, the possibility of combining multiple tools to increase the recovery correctness was investigated, yielding a combination of four individual tools that achieves an F1-score of 0.91.Publicationtype: Journal Article17 22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , APR4Vul: an empirical study of automatic program repair techniques on real-world Java vulnerabilities(Springer, 2024-02); ; ; Security vulnerability fixes could be a promising research avenue for Automated Program Repair (APR) techniques. In recent years, APR tools have been thoroughly developed for fixing generic bugs. However, the area is still relatively unexplored when it comes to fixing security bugs or vulnerabilities. In this paper, we evaluate nine state-of-the-art APR tools and one vulnerability-specific repair tool. In particular, we investigate their ability to generate patches for 79 real-world Java vulnerabilities in the Vul4J dataset, as well as the level of trustworthiness of these patches. We evaluate the tools with respect to their ability to generate security patches that are (i) testable, (ii) having the positive effect of closing the vulnerability, and (iii) not having side effects from a functional point of view. Our results show that the evaluated APR tools were able to generate testable patches for around 20% of the considered vulnerabilities. On average, nearly 73% of the testable patches indeed eliminate the vulnerabilities, but only 44% of them could actually fix security bugs while maintaining the functionalities. To understand the root cause of this phenomenon, we conduct a detailed comparative study of the general bug fix patterns in Defect4J and the vulnerability fix patterns in ExtraVul (which we extend from Vul4J). Our investigation shows that, although security patches are short in terms of lines of code, they contain unique characteristics in their fix patterns compared to general bugs. For example, many security fixes require adding method calls. These method calls contain specific input validation-related keywords, such as encode, normalize, and trim. In this regard, our study suggests that additional repair patterns should be implemented for existing APR tools to fix more types of security vulnerabilities.Publicationtype: Journal Article41 96 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Cybersecurity discussions in Stack Overflow: a developer-centred analysis of engagement and self-disclosure behaviour(2023-12-22); ; ; Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users’ engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site’s relevance and reputation into jeopardy. Hence, it is important to understand users’ participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users’ engagement and their privacy practices in SO. Particularly, it analyses developers’ self-disclosure behaviour regarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). Results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers’ engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO.Publicationtype: Journal Article43 69