The Linux kernel, a cornerstone of modern computing and Free Software, forms the foundation of the GNU/Linux ecosystem and critical services like the Internet. Its development model integrates diverse workflows that extend beyond technical concerns. Despite its success, the project relies on key individuals and ad-hoc tools, raising sustainability and workforce renewal concerns. Moreover, known challenges within its development model remain underexplored in academic research. This paper presents ongoing efforts to develop taxonomies that describe these workflows, aiming to identify bottlenecks and guide the creation of Free Software tools to support the kernel long-term sustainability.
Analyzing software requirements is a complex task, particularly for projects with a large volume of requirements, and when conducted manually, this task is time-consuming and prone to human errors. Moreover, once the software implements the requirements, it is essential to conduct tests to ensure the correct validation. Within a software institute, each new requirement can be assigned to a test team (teams 1 and 2) responsible for ensuring coverage by updating or creating test cases. There are instances in which a requirement is not assigned to either the team or is assigned to both. Each test team is tasked with validating a specific scope of requirements, making it crucial that each requirement is analyzed and validated by an appropriate test team. If a test team fails to validate a requirement within its scope, it can result in software vulnerability. To mitigate these issues, this paper described the use of pre-trained models, such as BERT, XLNet, and ELECTRA, to automate the process of requirement classification, thereby determining which test team should validate each new requirement. We compared the models based on accuracy, precision, recall, F1-Score, and Area Under Curve (AUC) macro metrics. Notably, the XLNet model demonstrated superior performance among the models, achieving 93.16% AUC Macro, while the BERT model achieved 91.28% AUC, and the ELECTRA model achieved 90.17% AUC.We also applied the non-parametric Friedman test to statistically validate the results, followed by the Conover squared rank test, with a significance level of 0.05. The results indicate that XLNet outperformed the BERT and ELECTRA models, exhibiting a superior capacity for assigning requirements to the correct test teams. Given the promising results of this research, this study aims to demonstrate the viability of using pre-trained models as a solution to optimize the testing process in the software industry.
An effective classification of security-related software requirements is crucial to mitigate potential threats and ensure robust system design. This study investigates the performance of Large Language Models (LLMs) in classifying security-related requirements compared to traditional Machine Learning (ML) methods. Using the SecReq, DOSSPRE and PROMISE+ datasets, we evaluated ten LLMs across various prompt engineering strategies. The results demonstrate that LLMs achieve high accuracy and outperform traditional ML-based models in several evaluation scenarios and that prompt engineering can significantly enhance the model’s ability to identify security-related requirements. This work underscores the domaingeneralization capabilities of LLMs and their potential to streamline requirements classification without the complexity of feature engineering or dataset-specific fine-tuning often required by ML-based approaches. Researchers, practitioners, and tool developers can leverage these findings to advance automated approaches in security requirements engineering.
O mercado de desenvolvimento de software está crescendo rapidamente, e projetos de software livre (SL) oferecem oportunidades valiosas para novatos. Contudo, as barreiras técnicas e não técnicas ainda dificultam as primeiras contribuições. Embora estudos busquem mitigar esses desafios, persistem as limitações. Exemplos trabalhados ajudam aprendizes a cometer menos erros, mas é difícil encontrar exemplos estruturados. Muitos desenvolvedores recorrem ao Stack Overflow, estratégia que nem sempre é eficaz. Recentemente, modelos de linguagem como o ChatGPT transformaram a busca por soluções. Considerando a reutilização frequente de códigos do Stack Overflow e o uso crescente do ChatGPT, este estudo investigou se respostas dessas fontes poderiam funcionar como exemplos trabalhados para apoiar o aprendizado e a resolução de problemas de novatos. Para isso, usamos uma issue do projeto Pandas como estudo de caso e conduzimos entrevistas individuais com o protocolo Thinking Aloud, seguidas de análise qualitativa. Os resultados sugerem que exemplos trabalhados dessas fontes podem auxiliar novatos em suas primeiras contribuições a projetos de SL.
The asynchronous evolution of test and production code can compromise software quality and maintainability. However, identifying and analyzing such co-evolution dynamics remains a complex task, often hindered by the lack of scalable tools and comprehensive visualizations. This paper presents Highlight Test Code, a web-based platform for analyzing the co-evolution of test and production code in open-source repositories. The platform implements a multi-stage pipeline encompassing repository mining, time series generation, clustering, and statistical correlation. It is built upon a curated dataset of 526 GitHub repositories across six programming languages. Highlight Test Code offers visual analytics and AI-generated insights that assist users in identifying test evolution patterns, evaluating co-evolution levels, and understanding their relationship with maintenance activities and project characteristics. The tool enables researchers and practitioners to assess testing practices at scale. Video link: https://youtu.be/U29eEg_gXXM. Software License: General Public License (GPL).
