Mining Programming Activity to Promote Help

dc.contributor.authorCarter, Jason
dc.contributor.authorDewan, Prasun
dc.date.accessioned2017-10-23T11:55:28Z
dc.date.available2017-10-23T11:55:28Z
dc.date.issued2015
dc.description.abstractWe have investigated techniques for mining programming activity to offer help to programmers in difficulty. We have developed a (a) difficulty-detection mechanism based on the notion of command ratios; (b) difficulty-classification mechanism that uses both command ratios and rates; and (c) collaboration mechanism that provides both workspace and difficulty awareness. Our studies involve interviews and lab and field experiments, and indicate that (a) it is possible to mine programming activity to reliably detect and classify difficulties, (b) it is possible to build a collaborative environment to offer opportunistic help, (c) programmers are not unnerved by and find it useful to receive unsolicited help arriving in response to automatically detected difficulties, (d) the acceptable level of privacy in a help-promotion tool depends on whether the developers in difficulty are student or industrial programmers, and whether they have been exposed earlier to a help promotion tool, and (e) difficulty detection can filter out spurious help requests and reduce the need for meetings required to poll for rare difficulty events.en
dc.identifier.doi10.1007/978-3-319-20499-4_2
dc.identifier.isbn978-3-319-20498-7
dc.language.isoen
dc.publisherSpringer, Cham
dc.relation.ispartofECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work
dc.relation.ispartofseriesECSCW
dc.titleMining Programming Activity to Promote Helpen
dc.typeText/Conference Paper
gi.citation.endPage42
gi.citation.startPage23
gi.citations.count7
gi.citations.elementPrasun Dewan, Samuel George, Bowen Gu, Zhizhou Liu, Hao Wang, Andrew Wortas (2021): Broad Awareness of Unseen Work on a Concurrency-Based Assignment, In: 2021 IEEE 28th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW), doi:10.1109/hipcw54834.2021.00009
gi.citations.elementRuben Acuña, Ajay Bansal (2024): WIP: Characterizing Student Programming Activity, In: 2024 IEEE Frontiers in Education Conference (FIE), doi:10.1109/fie61694.2024.10893048
gi.citations.elementSamuel D George, Tao Huang, Chandler Robinson, Gabriel Schell, Wei Shan, Ziqian Zhao, Zeqi Zhou, Prasun Dewan (2024): Assistant Dashboard Plus – Enhancing an Existing Instructor Dashboard with Difficulty Detection and GPT-based Code Clustering, In: Companion Proceedings of the 29th International Conference on Intelligent User Interfaces, doi:10.1145/3640544.3645231
gi.citations.elementDuri Long, Kun Wang, Jason Carter, Prasun Dewan (2018): Exploring the Relationship Between Programming Difficulty and Web Accesses, In: 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), doi:10.1109/vlhcc.2018.8506511
gi.citations.elementRuben Acuña, Ajay Bansal (2024): Improving Student Learning with Automated Assessment, In: Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, doi:10.1145/3649217.3653603
gi.citations.elementPrasun Dewan (2016): Inferred Awareness to Support Mixed-Activity Collaboration, In: 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC), doi:10.1109/cic.2016.052
gi.citations.elementJason Carter, Prasun Dewan (2018): Contextualizing inferred programming difficulties, In: Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering, doi:10.1145/3194932.3194937
gi.conference.date19-23 September 2015
gi.conference.locationOslo, Norway
gi.conference.sessiontitleFull Papers

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