To perform various development and maintenance tasks, developers frequently seek information on various sources such as mailing lists, Stack Overflow (SO), and Quora. However, extracting and preprocessing unstructured data from various sources, building and maintaining a reusable dataset is often a time-consuming and iterative process. Additionally, the lack of tools for automating this data analysis process complicates the task to reproduce previous results or datasets.
To address these concerns we propose Makar, which provides various data extraction and preprocessing methods to support researchers in conducting reproducible multi-source studies.
Tool webpage: {https://github.com/maethub/makar}
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Makar: a framework for multi-source studies based on unstructured data
1. Makar: A Framework for Multi-source
Studies Based on Unstructured Data
Mathias Birrer, Pooja Rani, Sebastiano Panichella, Oscar Nierstrasz
University of Bern, Switzerland
2. /** Code comments */
Do developers discuss code comments?
/**
* TOD
O
*
/
public void log(String s)
{
System.out.println(s)
;
}
2
3. Developers use various discussion sources
3
Planning
Implementation
Releasing
Maintenance
Testing
5. Makar
5
Makar: A tool for Multi-source Studies
https://github.com/maethub/makar
Planning
Implementation
Releasing
Maintenance
Testing
Extracting
Processing Querying Exploring
6. Features
Extract data from di
ff
erent sources
e.g., Stack Over
fl
ow, Github, Mailing Lists
Support mapping and processing the data
Explore and perform ad-hoc searches
Extending the dataset easily
6
16. 16
Future work
Extension of data source adapters
Building a UI of the study pipeline
Development of analysis and visualisation components
Facilitation of more multi-source studies
17. Hosted on Github
https://github.com/maethub/makar
Demo at YouTube
https://youtu.be/Yqj1b4Bv-58
Replication Package at Zenodo
https://doi.org/10.5281/zenodo.4434822
Contact us
17
https://twitter.com/poojaruhal http://scg.unibe.ch/staff/Pooja-Rani
Makar: A Framework for Multi-source
Studies Based on Unstructured Data