.png){kind=icon}
.png){kind=icon}
Return
Graal: The Quest for Source Code Knowledge
.png){kind=icon}
PRE
AI
DOI：10.1109/SCAM.2018.00021.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Source code analysis tools are designed to analyze code artifacts with different intents, which span from improving the quality and security of the software to easing refactoring and reverse engineering activities. However, most tools do not come with features to periodically schedule their analysis or to be executed on a battery of repositories, and lack support to combine their results with other analysis tools. Thus, researchers and practitioners are often forced to develop ad-hoc scripts to meet their needs. This comes at the risk of obtaining wrong results (because of the lack of testing) and of hindering replication by other research teams. In addition, the resulting scripts are often not meant to be customized nor designed for incrementality, scalability and extensibility. In this paper we present Graal, which empowers users with a customizable, scalable and incremental approach to conduct source code analysis and enables relating the obtained results with other software project data. Graal leverages on and extends the functionalities of GrimoireLab, a strong free software tool developed by Bitergia, a company devoted to offer commercial software development analytics, and part of the CHAOSS project of the Linux Foundation.
Keywords:
Software mining
empirical software engineering
open source
software development
software analytics
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
0
Papers:
2
Citations:
0