Abstract
Scientific software is one of the key elements for reproducible research. However, classic publications and related scientific software are typically not (sufficiently) linked, and tools are missing to jointly explore these artefacts. In this paper, we report on our work on developing the analytics tool SciSoftX (https://labs.tib.eu/info/projekt/scisoftx/) for jointly exploring software and publications. The presented prototype, a concept for automatic code discovery, and two use cases demonstrate the feasibility and usefulness of the proposal.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Atzeni, M., Atzori, M.: Codeontology: RDF-ization of source code. In: d’Amato, C. (ed.) ISWC 2017. LNCS, vol. 10588, pp. 20–28. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68204-4_2
Baker, M.: 1,500 scientists lift the lid on reproducibility. Nat. News 533(7604), 452 (2016)
Borg, M., Runeson, P., Ardö, A.: Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability. Empir. Softw. Eng. 19(6), 1565–1616 (2014). https://doi.org/10.1007/s10664-013-9255-y
Chen, X., Hosking, J.G., Grundy, J.: Visualizing traceability links between source code and documentation. In: IEEE Symposium on Visual Languages and Human-Centric Computing, Innsbruck, Austria, pp. 119–126 (2012). https://doi.org/10.1109/VLHCC.2012.6344496
Constantin, A.: Automatic structure and keyphrase analysis of scientific publications. Ph.D. thesis, University of Manchester, UK (2014). http://www.manchester.ac.uk/escholar/uk-ac-man-scw:230124
Holzmann, H., Sperber, W., Runnwerth, M.: Archiving software surrogates on the web for future reference. In: Fuhr, N., Kovács, L., Risse, T., Nejdl, W. (eds.) TPDL 2016. LNCS, vol. 9819, pp. 215–226. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43997-6_17
Moser, M., Pichler, J.: Documentation generation from annotated source code of scientific software: position paper. In: Proceedings of the International Workshop on Software Engineering for Science, SE4Science@ICSE 2016, 14 May 2016–22 May 2016, Austin, Texas, USA, pp. 12–15. ACM (2016). https://doi.org/10.1145/2897676.2897679
Nazar, N., Hu, Y., Jiang, H.: Summarizing software artifacts: a literature review. J. Comput. Sci. Technol. 31(5), 883–909 (2016). https://doi.org/10.1007/s11390-016-1671-1
Witte, R., Li, Q., Zhang, Y., Rilling, J.: Text mining and software engineering: an integrated source code and document analysis approach. IET Softw. 2(1), 3–16 (2008). https://doi.org/10.1049/iet-sen:20070110
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Hoppe, A., Hagen, J., Holzmann, H., Kniesel, G., Ewerth, R. (2018). An Analytics Tool for Exploring Scientific Software and Related Publications. In: Méndez, E., Crestani, F., Ribeiro, C., David, G., Lopes, J. (eds) Digital Libraries for Open Knowledge. TPDL 2018. Lecture Notes in Computer Science(), vol 11057. Springer, Cham. https://doi.org/10.1007/978-3-030-00066-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-00066-0_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00065-3
Online ISBN: 978-3-030-00066-0
eBook Packages: Computer ScienceComputer Science (R0)