Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-22748Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Birrer, Mathias | - |
| dc.contributor.author | Rani, Pooja | - |
| dc.contributor.author | Panichella, Sebastiano | - |
| dc.contributor.author | Nierstrasz, Oscar | - |
| dc.date.accessioned | 2021-07-01T10:00:46Z | - |
| dc.date.available | 2021-07-01T10:00:46Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.isbn | 978-1-7281-9630-5 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/22748 | - |
| dc.description | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | de_CH |
| dc.description.abstract | To perform various development and maintenance tasks, developers frequently seek information on various sources such as mailing lists, Stack Overflow (SO), and Quora. Researchers analyze these sources to understand developer information needs in these tasks. 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. To evaluate Makar, we conduct a case study that analyzes code comment related discussions from SO, Quora, and mailing lists. Our results show that Makar is helpful for preparing reproducible datasets from multiple sources with little effort, and for identifying the relevant data to answer specific research questions in a shorter time compared to state-of-the-art tools, which is of critical importance for studies based on unstructured data. Tool webpage: https://github.com/maethub/makar | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | IEEE | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject.ddc | 005: Computerprogrammierung, Programme und Daten | de_CH |
| dc.title | Makar : a framework for multi-source studies based on unstructured data | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Informatik (InIT) | de_CH |
| dc.identifier.doi | 10.1109/SANER50967.2021.00069 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-22748 | - |
| zhaw.conference.details | 28th IEEE International Conference on Software Analysis, Evolution and Reengineering, Honolulu, USA, 9-12 March 2021 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 581 | de_CH |
| zhaw.pages.start | 577 | de_CH |
| zhaw.publication.status | acceptedVersion | de_CH |
| zhaw.publication.review | Peer review (Abstract) | de_CH |
| zhaw.title.proceedings | Proceedings of the 2021 IEEE SANER Conference | de_CH |
| zhaw.webfeed | Software Systems | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | No | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2021_Birrer-etal_Makar-a-framwork-for-multi-source-studies.pdf | Accepted Version | 494.8 kB | Adobe PDF |  View/Open |
Show simple item record
Birrer, M., Rani, P., Panichella, S., & Nierstrasz, O. (2021). Makar : a framework for multi-source studies based on unstructured data [Conference paper]. Proceedings of the 2021 IEEE SANER Conference, 577–581. https://doi.org/10.1109/SANER50967.2021.00069
Birrer, M. et al. (2021) ‘Makar : a framework for multi-source studies based on unstructured data’, in Proceedings of the 2021 IEEE SANER Conference. IEEE, pp. 577–581. Available at: https://doi.org/10.1109/SANER50967.2021.00069.
M. Birrer, P. Rani, S. Panichella, and O. Nierstrasz, “Makar : a framework for multi-source studies based on unstructured data,” in Proceedings of the 2021 IEEE SANER Conference, 2021, pp. 577–581. doi: 10.1109/SANER50967.2021.00069.
BIRRER, Mathias, Pooja RANI, Sebastiano PANICHELLA und Oscar NIERSTRASZ, 2021. Makar : a framework for multi-source studies based on unstructured data. In: Proceedings of the 2021 IEEE SANER Conference. Conference paper. IEEE. 2021. S. 577–581. ISBN 978-1-7281-9630-5
Birrer, Mathias, Pooja Rani, Sebastiano Panichella, and Oscar Nierstrasz. 2021. “Makar : A Framework for Multi-Source Studies Based on Unstructured Data.” Conference paper. In Proceedings of the 2021 IEEE SANER Conference, 577–81. IEEE. https://doi.org/10.1109/SANER50967.2021.00069.
Birrer, Mathias, et al. “Makar : A Framework for Multi-Source Studies Based on Unstructured Data.” Proceedings of the 2021 IEEE SANER Conference, IEEE, 2021, pp. 577–81, https://doi.org/10.1109/SANER50967.2021.00069.
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