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--- chaoss:gsoc-ideas [2018/02/03 15:15]
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+++ chaoss:gsoc-ideas [2018/03/21 15:56] (current)
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-[[chaoss:start|<< Back to CHAOSS start page]]
+[[chaoss:start|<< Back to CHAOSS start page]] ]
 {{:chaoss:chaoss_logo_pantone_2_.png?400|}}
 ====== Ideas for Google Summer of Code projects ======
+===== Idea #1: Support of Standard CHAOSS Formats for Description of Projects =====
-=====  Idea #1: Reporting of CHAOSS Metrics =====
+[ [[https://github.com/chaoss/grimoirelab/issues/71|Micro-tasks and place for questions]] ]
+Currently, GrimoireLab uses its own format for describing a project, including the data sources (repositories to retrieve information from), the internal organization of the project (e.g., in subprojects), and specifics about how the data is to be presented. For this information, some standard formats already exist, that can be directly used, or used with some modifications. Among them, [[https://en.wikipedia.org/wiki/DOAP|DOAP]] is one of the most interesting ones, but there are many others.
-Currently, [[https://grimoirelab.github.io/|GrimoireLab]] includes a tool for reporting: Manuscripts. This tool reads data from a GrimoireLab ElasticSearch database, and produces with it a PDF report with relevant metrics for a set of analyzed projects. Internally, Manuscripts uses some Python code to produce charts and CSV tables, which are integrated into a LaTeX document to produce the final PDF. Other approaches, such as producing Jupyter notebooks, will be explored too.
-This idea is about adding support to Manuscripts to produce reports based on the work of the CHAOSS Community. Since Manuscripts is still a moving target, this will be also a chance to participate in the general development of the tool itself, to convert it into a generic reporting system for GrimoireLab data.
-The aims of the project are as follows:
-  * Writing Python code to query GrimoireLab Elastisearch databases and obtain from it the metrics relevant for the report. Possible technologies to achieve this aim include Python Pandas.
-  * Writing Python code to produce suitable representation for those metrics, such as tables and charts.
-  * Adapting current tools to produce reports directly from data sources, by managing the GrimoireLab toolchain. Possible solutions include adding the code to Mordred, the tool orchestrating GrimoireLab tools.
-Other aims, such as producing Jupyter notebooks as a final result or an intermediate step are completely within scope.
-  * //Difficulty:// easy/medium
-  * //Requirements:// Python programming. Interest in software analytics. Willingness to understand GrimoireLab internals.
-  * //Recommended:// Experience with Python interfaces to databases would be convenient, but can be learned during the project. Experience with Latex and/or Python Jupyter Notebooks would help.
-  * //Mentors:// Jesus M. Gonzalez-Barahona, Matt Germonprez, Jordi Cabot
-===== Idea #2: Support of Standard CHAOSS Formats for Description of Projects =====
-Currently, GrimoireLab uses its own format for describing a project, including the data sources (repositories to retrieve information from), the internal organization of the project (e.g., in subprojects), and specifics about how the data is to be presented. For this information, some standard formats already exist, that can be directly used, or used with some modifications. Among them, DOAP is one of the most interesting ones, but there are many others.
 This idea is about identifying formats used by projects to describe themselves and adding support to GrimoireLab. This includes not only static formats, but also APIs.
@@ Line 47: / Line 27: @@
   * //Recommended:// Experience with Python HTTP and XML libraries would be convenient, but can be learned during the project.
   * //Mentors:// Jesus M. Gonzalez-Barahona, Valerio Cosentino
+=====  Idea #2: Reporting of CHAOSS Metrics =====
+[ [[https://github.com/chaoss/grimoirelab/issues/70|Micro-tasks and place for questions]] ]
+Currently, [[https://grimoirelab.github.io/|GrimoireLab]] includes a tool for reporting: Manuscripts. This tool reads data from a GrimoireLab ElasticSearch database, and produces with it a PDF report with relevant metrics for a set of analyzed projects. Internally, Manuscripts uses some Python code to produce charts and CSV tables, which are integrated into a LaTeX document to produce the final PDF. Other approaches, such as producing Jupyter notebooks, will be explored too.
+This idea is about adding support to Manuscripts to produce reports based on the work of the CHAOSS Community. Since Manuscripts is still a moving target, this will be also a chance to participate in the general development of the tool itself, to convert it into a generic reporting system for GrimoireLab data.
+The aims of the project are as follows:
+  * Writing Python code to query GrimoireLab Elastisearch databases and obtain from it the metrics relevant for the report. Possible technologies to achieve this aim include Python Pandas.
+  * Writing Python code to produce suitable representation for those metrics, such as tables and charts.
+  * Adapting current tools to produce reports directly from data sources, by managing the GrimoireLab toolchain. Possible solutions include adding the code to Mordred, the tool orchestrating GrimoireLab tools.
+Other aims, such as producing Jupyter notebooks as a final result or an intermediate step are completely within scope.
+  * //Difficulty:// easy/medium
+  * //Requirements:// Python programming. Interest in software analytics. Willingness to understand GrimoireLab internals.
+  * //Recommended:// Experience with Python interfaces to databases would be convenient, but can be learned during the project. Experience with Latex and/or Python Jupyter Notebooks would help.
+  * //Mentors:// Jesus M. Gonzalez-Barahona, Matt Germonprez, Jordi Cabot
 ===== Idea #3: Prototype New CHAOSS Metrics  =====
+[ [[https://github.com/OSSHealth/ghdata/issues/82|Micro-tasks and place for questions]] ]
 Create a library that can be used by CHAOSS Community Software projects like GHData to express open source software project level similarities. There are two components: A set of algorithms for integrating similarity measures on an array of project data and implementation of visualizations using our existing framework and possibly adding to the framework.
@@ Line 75: / Line 81: @@
   * //Recommended:// Experience with Python HTTP and XML libraries would be convenient, but can be learned during the project.
   * //Mentors:// Sean Goggins, Jesus M. Gonzalez-Barahona, Josianne Marsan

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