ICPC 2018
Sun 27 - Mon 28 May 2018 Gothenburg, Sweden
co-located with * ICSE 2018 *
Mon 28 May 2018 11:54 - 12:11 at J1 room - Generation and Classification Chair(s): Shaowei Wang

Software development video tutorials are emerging as a new resource for developers to support their information needs. However, when trying to find the right video to watch for a task at hand, developers have little information at their disposal to quickly decide if they found the right video or not. This can lead to missing the best tutorials or wasting time watching irrelevant ones.

Other external sources of information for developers, such as StackOverflow, have benefited from the existence of informative tags, which help developers to quickly gauge the relevance of posts and find related ones. We argue that the same is valid also for videos and propose the first set of approaches to automatically generate tags describing the contents of software development video tutorials. We investigate seven tagging approaches for this purpose, some using information retrieval techniques and leveraging only the information in the videos, others relying on external sources of information, such as StackOverflow, as well as two out-of-the-box commercial video tagging approaches. We evaluated 19 different configurations of these tagging approaches and the results of a user study showed that some of the information retrieval-based approaches performed the best and were able to recommend tags that developers consider relevant for describing programming videos.

Mon 28 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

11:00 - 12:30
Generation and ClassificationTechnical Research at J1 room
Chair(s): Shaowei Wang Queen's University
11:00
17m
Full-paper
Deep Code Comment GenerationTechnical Research
Technical Research
Xing Hu Peking University, Ge Li Peking University, Xin Xia Monash University, David Lo Singapore Management University, Zhi Jin Peking University
Pre-print
11:17
10m
Short-paper
On the Naturalness of Auto-generated Code —Can We Identify Auto-Generated Code Automatically?ERA
Technical Research
Masayuki Doi Osaka University, Yoshiki Higo Osaka University, Ryo Arima , Kento Shimonaka Osaka University, Shinji Kusumoto
Pre-print
11:27
10m
Short-paper
Augmenting Source Code Lines with Sample Variable ValuesERA
Technical Research
Matúš Sulír Technical University of Košice, Jaroslav Porubän Technical University of Košice, Slovakia
Pre-print
11:37
17m
Full-paper
Automatically Classifying Posts into Question Categories on Stack OverflowTechnical Research
Technical Research
Stefanie Beyer University of Klagenfurt, Christian Macho University of Klagenfurt, Massimiliano Di Penta University of Sannio, Martin Pinzger Alpen-Adria-Universität Klagenfurt
11:54
17m
Full-paper
Automatic Tag Recommendation for Software Development Video TutorialsTechnical Research
Technical Research
Esteban Parra Florida State University, Javier Escobar-Avila Florida State University, Sonia Haiduc Florida State University
DOI Pre-print
12:11
17m
Full-paper
Classification of APIs by Hierarchical ClusteringTechnical Research
Technical Research
Johannes Härtel University of Koblenz-Landau, Germany, Hakan Aksu University of Koblenz, Ralf Laemmel University of Koblenz-Landau, Germany