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Improving integrated development environment commands knowledge with recommender systems
Gasparic M., Gurbanov T., Ricci F.  ICSE-SEET 2018 (Proceedings of the 40th International Conference on Software Engineering: Software Engineering Education and Training, Gothenburg, Sweden, May 27-Jun 3, 2018)88-97.2018.Type:Proceedings
Date Reviewed: Feb 11 2019

Integrated development environments (IDEs) are an essential part of software developers’ toolkits, enabling them to efficiently author, manage, and test their development projects. As a developer’s familiarity with IDE commands and functionality increases, so does their productivity and quality of software development.

The focus of this work is not to compute recommendations that are most useful or relevant. Instead, Gasparic et al. focus on a broader topic: the utility of recommendation algorithms to increase developer knowledge of IDE commands and functionality. This is analogous to improving a consumer’s online shopping experience, for example, Amazon recommendations such as “customers who bought item X also bought item Y” potentially trigger additional purchases.

The authors empirically show how incorporating suggestions from recommendation algorithms regarding unexplored and relevant commands can potentially improve developer familiarity with IDE commands. The authors’ justification can be strengthened by running their experiments on programming courses available on massive open online courses (MOOCs). This approach potentially overcomes bias in the authors’ experimental setup introduced due to the small sample size (of 28 student volunteers) relative to prior studies on recommendation systems [1]. That said, an open question remains as to whether the validity of the experiments is affected due to student familiarity with the programming language used in the IDE. One approach to negate this effect would be to conduct the experiments on an IDE for an unfamiliar or esoteric programming language.

One of the authors’ observations concerns a simple recommendation algorithm called “Most Popular,” which suggests the most popular IDE commands unknown to a developer. The authors report that this algorithm provides more relevant results compared to other complex, context-sensitive algorithms. Interestingly, there is evidence that this result translates to other domains as well. For instance, prior work on movie recommendation algorithms for the Netflix challenge [2] indicates that the performance of simple algorithms that rely on the average of all movie ratings by a user and the average of all ratings for a movie is comparable to Netflix’s own recommendation algorithm, called CineMatch.

While the authors’ treatment of the role of recommendation algorithms can be improved by considering alternative experimental setups, for example, to negate effects of familiarity of the language underlying an IDE, no doubt their work is a stepping stone for research on domain-specific recommendation algorithms for improving the usability of IDEs.

Reviewer:  Abhijeet Mohapatra Review #: CR146426 (1905-0175)
1) Bobadilla, J.; Ortega, F.; Hernando, A.; Gutiérrez, A. Recommender systems survey. Knowledge-Based Systems 46, (2013), 109–132.
2) Leskovec, J.; Rajaraman, A.; Ullman, J. D. Mining of massive datasets (2nd ed.). Cambridge University Press, Cambridge, UK, 2014.
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