Zobrazeno 1 - 10
of 261
pro vyhledávání: '"DI RUSCIO, Davide"'
Technical debt (TD) is a term used to describe the additional work and costs that emerge when developers have opted for a quick and easy solution to a problem, rather than a more effective and well-designed, but time-consuming approach. Self-Admitted
Externí odkaz:
http://arxiv.org/abs/2411.05457
Model-Driven Engineering (MDE) has seen significant advancements with the integration of Machine Learning (ML) and Deep Learning (DL) techniques. Building upon the groundwork of previous investigations, our study provides a concise overview of curren
Externí odkaz:
http://arxiv.org/abs/2410.17370
Autor:
Nguyen, Phuong T., Di Rocco, Juri, Di Sipio, Claudio, Shakya, Mudita, Di Ruscio, Davide, Di Penta, Massimiliano
In the GitHub ecosystem, workflows are used as an effective means to automate development tasks and to set up a Continuous Integration and Delivery (CI/CD pipeline). GitHub Actions (GHA) have been conceived to provide developers with a practical tool
Externí odkaz:
http://arxiv.org/abs/2407.16946
Stack Overflow is a prominent Q and A forum, supporting developers in seeking suitable resources on programming-related matters. Having high-quality question titles is an effective means to attract developers' attention. Unfortunately, this is often
Externí odkaz:
http://arxiv.org/abs/2406.15633
Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made on Artificial Intelligence (AI) t
Externí odkaz:
http://arxiv.org/abs/2405.18539
When simplicity meets effectiveness: Detecting code comments coherence with word embeddings and LSTM
Publikováno v:
EASE 2024
Code comments play a crucial role in software development, as they provide programmers with practical information, allowing them to understand better the intent and semantics of the underpinning code. Nevertheless, developers tend to leave comments u
Externí odkaz:
http://arxiv.org/abs/2405.16272
Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may nee
Externí odkaz:
http://arxiv.org/abs/2405.13185
Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems. With the increasing use of machine learning in software systems, researchers have been developing techniques to automa
Externí odkaz:
http://arxiv.org/abs/2404.09919
Motivated by recent work on lifelong learning applications for language models (LMs) of code, we introduce CodeLL, a lifelong learning dataset focused on code changes. Our contribution addresses a notable research gap marked by the absence of a long-
Externí odkaz:
http://arxiv.org/abs/2312.12492
Autor:
Clerissi, Diego, Di Rocco, Juri, Di Ruscio, Davide, Di Sipio, Claudio, Ihirwe, Felicien, Mariani, Leonardo, Micucci, Daniela, Rossi, Maria Teresa, Rubei, Riccardo
IoT systems complexity and susceptibility to failures pose significant challenges in ensuring their reliable operation Failures can be internally generated or caused by external factors impacting both the systems correctness and its surrounding envir
Externí odkaz:
http://arxiv.org/abs/2309.02985