Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Ciniselli, Matteo"'
In the rapidly evolving landscape of software engineering, the integration of Artificial Intelligence (AI) into the Software Development Life-Cycle (SDLC) heralds a transformative era for developers. Recently, we have assisted to a pivotal shift towa
Externí odkaz:
http://arxiv.org/abs/2405.12731
Code completion is a key feature of Integrated Development Environments (IDEs), aimed at predicting the next tokens a developer is likely to write, helping them write code faster and with less effort. Modern code completion approaches are often power
Externí odkaz:
http://arxiv.org/abs/2403.15149
Autor:
Mastropaolo, Antonio, Ciniselli, Matteo, Pascarella, Luca, Tufano, Rosalia, Aghajani, Emad, Bavota, Gabriele
When comprehending code, a helping hand may come from the natural language comments documenting it that, unfortunately, are not always there. To support developers in such a scenario, several techniques have been presented to automatically generate n
Externí odkaz:
http://arxiv.org/abs/2402.00519
The automation of code review has been tackled by several researchers with the goal of reducing its cost. The adoption of deep learning in software engineering pushed the automation to new boundaries, with techniques imitating developers in generativ
Externí odkaz:
http://arxiv.org/abs/2401.05136
Several code summarization techniques have been proposed in the literature to automatically document a code snippet or a function. Ideally, software developers should be involved in assessing the quality of the generated summaries. However, in most c
Externí odkaz:
http://arxiv.org/abs/2312.15475
Autor:
Ciniselli, Matteo, Pascarella, Luca, Aghajani, Emad, Scalabrino, Simone, Oliveto, Rocco, Bavota, Gabriele
The automatic generation of source code is one of the long-lasting dreams in software engineering research. Several techniques have been proposed to speed up the writing of new code. For example, code completion techniques can recommend to developers
Externí odkaz:
http://arxiv.org/abs/2302.04098
Autor:
Mastropaolo, Antonio, Pascarella, Luca, Guglielmi, Emanuela, Ciniselli, Matteo, Scalabrino, Simone, Oliveto, Rocco, Bavota, Gabriele
Software engineering research has always being concerned with the improvement of code completion approaches, which suggest the next tokens a developer will likely type while coding. The release of GitHub Copilot constitutes a big step forward, also b
Externí odkaz:
http://arxiv.org/abs/2302.00438
Deep Learning (DL) models have been widely used to support code completion. These models, once properly trained, can take as input an incomplete code component (e.g., an incomplete function) and predict the missing tokens to finalize it. GitHub Copil
Externí odkaz:
http://arxiv.org/abs/2204.06894
Autor:
Ciniselli, Matteo, Cooper, Nathan, Pascarella, Luca, Mastropaolo, Antonio, Aghajani, Emad, Poshyvanyk, Denys, Di Penta, Massimiliano, Bavota, Gabriele
Code completion aims at speeding up code writing by predicting the next code token(s) the developer is likely to write. Works in this field focused on improving the accuracy of the generated predictions, with substantial leaps forward made possible b
Externí odkaz:
http://arxiv.org/abs/2108.01585
Autor:
Ciniselli, Matteo, Cooper, Nathan, Pascarella, Luca, Poshyvanyk, Denys, Di Penta, Massimiliano, Bavota, Gabriele
Code completion is one of the main features of modern Integrated Development Environments (IDEs). Its objective is to speed up code writing by predicting the next code token(s) the developer is likely to write. Research in this area has substantially
Externí odkaz:
http://arxiv.org/abs/2103.07115