Hide
As a part of the Apache Software Foundation (ASF) project, Apache Creadur™, a Release Audit Tool (RAT) was developed especially in response to demand from the Apache Software Foundation and its hundreds of projects to provide a capability for release auditing that could be integrated into projects. The primary function of the RAT is automated code auditing and open-source license analysis focusing on headers. RAT is a natural language processing tool written in Java to easily run on any platform and to audit code from many source languages (e.g., C, C++, Java, Python, etc.). RAT can also be used to add license headers to codes that are not licensed.
In the summer of 2013, our team ran Apache RAT on source code produced from the Defense Advanced Research Projects Agency (DARPA) XDATA national initiative whose inception coincided with the 2012 U.S. Presidential Initiative in Big Data. XDATA brought together 24 performers across academia, private industry and the government to construct analytics, visualizations, and open source software mash-ups that were transitioned into government projects and to the defense sector. XDATA produced a large Git repository consisting of ~50,000 files and 10s of millions of lines of code. DARPA XDATA was launched to build a useful infrastructure for many government agencies and ultimately is an effort to avoid the traditional government-contractor software pipeline in which additional contracts are required to reuse and to unlock software previously funded by the government in other programs. All XDATA software is open source and is ingested into DARPA’s Open Catalog that points to outputs of the program including its source code and metrics on the repository. Because of this, one of core products of XDATA is the internal Git repository. Since XDATA brought together open source software across multiple performers, having an understanding of the licenses that the source codes used, and their compatibilities and differences was extremely important and since there repository was so large, our strategy was to develop an automated process using Apache RAT. We ran RAT on 24-core, 48 GB RAM Linux machine at the National Aeronautics and Space Administration (NASA)’s Jet Propulsion Laboratory (JPL) to produce a license evaluation of the XDATA Git repository and to provide recommendations on how the open source software products can be combined to adhere to the XDATA open source policy encouraging permissive licenses. Against our expectations, however, RAT failed to successfully and quickly audit XDATA’s large Git repository. Moreover, RAT provided no incremental output, resulting in solely a final report when a task was completed. RAT’s crawler did not automatically discern between binary file types and another file types. It seemed that RAT performed better by collecting similar sets of files together (e.g., all Javascript, all C++, all Java) and then running RAT jobs individually based on file types on smaller increments of files (e.g., 100 Java files at a time, etc). The lessons learned navigating these issues have motivated to create “DRAT”, which stands for "Distributed Release Audit Tool". DRAT directly overcomes RAT's limitations and brings code auditing and open source license analysis into the realm of Big Data using scalable open source Apache technologies. DRAT is already being applied and transitioned into the government agencies. DRAT currently exists at Github under the ALv2 under Chris Mattmann's GitHub account. Chris Mattmann was the PI of DARPA XDATA at JPL.
Show
As a part of the Apache Software Foundation (ASF) project, Apache Creadur™, a Release Audit Tool (RAT) was developed especially in response to demand from the Apache Software Foundation and its hundreds of projects to provide a capability for release auditing that could be integrated into projects. The primary function of the RAT is automated code auditing and open-source license analysis focusing on headers. RAT is a natural language processing tool written in Java to easily run on any platform and to audit code from many source languages (e.g., C, C++, Java, Python, etc.). RAT can also be used to add license headers to codes that are not licensed.
In the summer of 2013, our team ran Apache RAT on source code produced from the Defense Advanced Research Projects Agency (DARPA) XDATA national initiative whose inception coincided with the 2012 U.S. Presidential Initiative in Big Data. XDATA brought together 24 performers across academia, private industry and the government to construct analytics, visualizations, and open source software mash-ups that were transitioned into government projects and to the defense sector. XDATA produced a large Git repository consisting of ~50,000 files and 10s of millions of lines of code. DARPA XDATA was launched to build a useful infrastructure for many government agencies and ultimately is an effort to avoid the traditional government-contractor software pipeline in which additional contracts are required to reuse and to unlock software previously funded by the government in other programs. All XDATA software is open source and is ingested into DARPA’s Open Catalog that points to outputs of the program including its source code and metrics on the repository. Because of this, one of core products of XDATA is the internal Git repository. Since XDATA brought together open source software across multiple performers, having an understanding of the licenses that the source codes used, and their compatibilities and differences was extremely important and since there repository was so large, our strategy was to develop an automated process using Apache RAT. We ran RAT on 24-core, 48 GB RAM Linux machine at the National Aeronautics and Space Administration (NASA)’s Jet Propulsion Laboratory (JPL) to produce a license evaluation of the XDATA Git repository and to provide recommendations on how the open source software products can be combined to adhere to the XDATA open source policy encouraging permissive licenses. Against our expectations, however, RAT failed to successfully and quickly audit XDATA’s large Git repository. Moreover, RAT provided no incremental output, resulting in solely a final report when a task was completed. RAT’s crawler did not automatically discern between binary file types and another file types. It seemed that RAT performed better by collecting similar sets of files together (e.g., all Javascript, all C++, all Java) and then running RAT jobs individually based on file types on smaller increments of files (e.g., 100 Java files at a time, etc). The lessons learned navigating these issues have motivated to create “DRAT”, which stands for "Distributed Release Audit Tool". DRAT directly overcomes RAT's limitations and brings code auditing and open source license analysis into the realm of Big Data using scalable open source Apache technologies. DRAT is already being applied and transitioned into the government agencies. DRAT currently exists at Github under the ALv2 under Chris Mattmann's GitHub account. Chris Mattmann was the PI of DARPA XDATA at JPL.
Suitable Name Search
