Interaction-Aware Recommendation Systems for Software Developers

The primary objective is integrate developer activity modeling into recommendation systems for software developers. Software development is a complex cognitive task. We can reduce software developers’ cognitive load by providing effective tools. These tools should be capable of recognizing developers’ activities and thereby make recommendations, such as, best IDE commands or plugins to choose from and situated learning of best practices.

2019

Using Automated Prompts for Student Reflection on Computer Security Concepts

Hui Chen, Agnieszka Ciborowska, and Kostadin Damevski

In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education, Aberdeen, Scotland Uk, 2019

DOI Bib

@inproceedings{Chen2019UAP_3304221_3319731,
  author = {Chen, Hui and Ciborowska, Agnieszka and Damevski, Kostadin},
  title = {Using Automated Prompts for Student Reflection on Computer Security Concepts},
  booktitle = {Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education},
  series = {ITiCSE '19},
  year = {2019},
  isbn = {978-1-4503-6301-3},
  location = {Aberdeen, Scotland Uk},
  pages = {506--512},
  numpages = {7},
  url = {http://doi.acm.org/10.1145/3304221.3319731},
  doi = {10.1145/3304221.3319731},
  acmid = {3319731},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {automated reflection, reflection, reflection prompt}
}

2018

Predicting Future Developer Behavior in the IDE Using Topic Models

Kostadin Damevski, Hui Chen, David C. Shepherd, and 2 more authors

IEEE Transactions on Software Engineering, Nov 2018

DOI Bib

@article{DAMEVSKI_8024001,
  author = {Damevski, Kostadin and Chen, Hui and Shepherd, David C. and Kraft, Nicholas A. and Pollock, Lori},
  journal = {IEEE Transactions on Software Engineering},
  title = {Predicting Future Developer Behavior in the IDE Using Topic Models},
  year = {2018},
  volume = {44},
  number = {11},
  pages = {1100-1111},
  keywords = {program debugging;recommender systems;software engineering;future developer behavior;early software command recommender systems;negative user reaction;unusually complex applications;command recommendations;recommendation generation;user experience;command recommenders;future task context;debug OR;future development commands;software development interaction data;predicting future IDE commands;empirically-interpretable observations;Natural languages;Data models;Analytical models;Predictive models;Visualization;Adaptation models;Data analysis;Command recommendation systems;IDE interaction data},
  doi = {10.1109/TSE.2017.2748134},
  url = {https://dx.doi.org/10.1109/TSE.2017.2748134},
  issn = {0098-5589},
  month = nov
}

2016

Interactive Exploration of Developer Interaction Traces Using a Hidden Markov Model

Kostadin Damevski, Hui Chen, David Shepherd, and 1 more author

In Proceedings of the 13th International Workshop on Mining Software Repositories, Austin, Texas, Nov 2016

DOI Bib

@inproceedings{Damevski_2016_IED_2901739_2901741,
  author = {Damevski, Kostadin and Chen, Hui and Shepherd, David and Pollock, Lori},
  title = {Interactive Exploration of Developer Interaction Traces Using a
          Hidden Markov Model},
  booktitle = {Proceedings of the 13th International Workshop on Mining
          Software Repositories},
  series = {MSR '16},
  year = {2016},
  isbn = {978-1-4503-4186-8},
  location = {Austin, Texas},
  pages = {126--136},
  numpages = {11},
  url = {http://doi.acm.org/10.1145/2901739.2901741},
  doi = {10.1145/2901739.2901741},
  acmid = {2901741},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {IDE usage data, field studies, hidden-markov model}
}

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2019

2018

2016