Abstract
The Web is an important resource for a programmer: as much as 20% of a programmer’s time is spent on the Web [2]. When a programmer searches for information on the Web, two distinct information needs arise depending on the programmer’s previous knowledge of a library’s Application Programming Interfaces (APIs): learning how to invoke a software library versus reminding the programmers themselves the details deemed not worth remembering [2].
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bajracharya, S.K., Ossher, J., Lopes, C.V.: Leveraging usage similarity for effective retrieval of examples in code repositories. In: Proc. of Int’l Sym. on Foundations of Software Eng., pp. 157–166 (2010)
Brandt, J., Guo, P.J., Lewenstein, J., Dontcheva, M., Klemmer, S.R.: Two studies of opportunistic programming: interleaving web foraging, learning, and writing code. In: Proc. of Int’l SIGCHI Conf., pp. 1589–1598 (2009)
Broder, A.: A taxonomy of web search. SIGIR Forum 36(2), 3–10 (2002)
Fischer, G., Ye, Y.: Personalizing Delivered Information in a Software Reuse Environment. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) UM 2001. LNCS (LNAI), vol. 2109, pp. 178–187. Springer, Heidelberg (2001)
Fritz, T., Ou, J., Murphy, G., Murphy-Hill, E.: A degree-of-knowledge model to capture source code familiarity. In: Proc. of Int’l Conf. on Software Eng., pp. 385–394 (2010)
Lee, U., Liu, Z., Cho, J.: Automatic identification of user goals in web search. In: Proc. of Int’l Conf. on World Wide Web, pp. 391–400 (2005)
Ma, D., Schuler, D., Zimmermann, T., Sillito, J.: Expert recommendation with usage expertise. In: Proc. of Int’l Conf. on Software Maintenance (2009)
Robillard, M.: What makes apis hard to learn? answers from developers. IEEE Software 26(6), 27–34 (2009)
Rose, D., Levinson, D.: Understanding user goals in web search. In: Proc. of Int’l Conf. on World Wide Web, pp. 13–19 (2004)
Sieg, A., Mobasher, B., Burke, R.: Web search personalization with ontological user profiles. In: Proc. of Conf. on Info. & Knowledge Mgmt., pp. 525–534 (2007)
Tintarev, N., Masthoff, J.: Designing and evaluating explanations for recommender systems. In: Recommender Systems Handbook, pp. 479–510 (2011)
Treude, C., Barzilay, O., Storey, M.: How do programmers ask and answer questions on the web? In: Proc. of Int’l Conf. on Sw. Eng, pp. 804–807 (2011)
Vig, J., Sen, S., Riedl, J.: Tagsplanations: explaining recommendations using tags. In: Proc. of Int’l Conf. on Intelligent User Interfaces, pp. 47–56 (2009)
Wiedenbeck, S.: Novice/expert differences in programming skills. Int’l. Journal of Man-Machine Studies 23(4), 383–390 (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ying, A.T.T. (2012). Facilitating Code Example Search on the Web through Expertise Personalization. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds) User Modeling, Adaptation, and Personalization. UMAP 2012. Lecture Notes in Computer Science, vol 7379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31454-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-31454-4_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31453-7
Online ISBN: 978-3-642-31454-4
eBook Packages: Computer ScienceComputer Science (R0)