• Quantifying Synergy between So...

Treffer: Quantifying Synergy between Software Projects using README Files Only

Title:
Quantifying Synergy between Software Projects using README Files Only
Authors:
El Baff, Roxanne, Santhanam, Sivasurya, Hecking, Tobias
Publisher Information:
KSI Research Inc. and Knowledge Systems Institute Graduate School
Publication Year:
2021
Collection:
German Aerospace Center: elib - DLR electronic library
Subject Terms:
Institut für Softwaretechnologie, Intelligente und verteilte Systeme
Document Type:
Konferenz conference object
File Description:
application/pdf
Language:
English
Relation:
https://elib.dlr.de/141909/1/paper162.pdf; El Baff, Roxanne und Santhanam, Sivasurya und Hecking, Tobias (2021) Quantifying Synergy between Software Projects using README Files Only. In: 33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021, 33. KSI Research Inc. and Knowledge Systems Institute Graduate School. The Thirty Third International Conference on Software Engineering and Knowledge Engineering (SEKE 2021), 2021-07-01 - 2021-07-10, Pittsburgh, USA (Online). doi:10.18293/SEKE2021-162 . ISBN 1-891706-52-7. ISSN 2325-9000.
Availability:
https://elib.dlr.de/141909/
https://elib.dlr.de/141909/1/paper162.pdf
Rights:
cc_by
Accession Number:
edsbas.3B6B7DD5
Database:
BASE

Weitere Informationen

Software version control platforms, such as GitHub, host millions of open-source software projects. Due to their diversity, these projects are an appealing realm for discovering software trends. In our work, we seek to quantify synergy between software projects by connecting them via their similar as well as different software features. Our approach is based on the Literature-Based-Discovery (LBD), originally developed to uncover implicit knowledge in scientific literature databases by linking them through transitive connections. We tested our approach by conducting experiments on 13,264 GitHub (open-source) Python projects. Evaluation, based on human ratings of a subset of 90 project pairs, shows that our developed models are capable of identifying potential synergy between software projects by solely relying on their short descriptions (i.e. readme files).