Improving program navigation with an active help system

Viriyakattiyaporn, Petcharat; Murphy, Gail C.

doi:10.1145/1923947.1923951

Cited by 17 publications

(7 citation statements)

References 22 publications

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“…We are aware of three fully implemented ICRSs that have been developed in the past. Spyglass [15] observes and analyzes user behavior to check whether certain steps could be replaced by IDE commands. The input data is the IDE command execution history.…”

Section: A Ide Command Recommender Systemsmentioning

confidence: 99%

“…Regarding the ICRSs utility, Spyglass evaluation showed that an ICRS can make developers aware of available tools to a similar degree to a tutorial, but with less effort [15]. Murphy-Hill et al showed that it is feasible to automatically recommend useful IDE commands to software developers [9].…”

Section: A Ide Command Recommender Systemsmentioning

confidence: 99%

“…ICRS research community has designed, implemented, and tested several algorithms (see [7]- [12]), graphical user interfaces (GUIs) (see [13]- [15]), and even complete systems for recommending IDE commands in different settings (see [14]- [16]). However, these RSs have not been integrated in popular IDEs, yet; arguably because the accuracy and acceptance of the recommendations are still too low to enable practical usage.…”

Section: Introductionmentioning

confidence: 99%

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IDE Interaction Support With Command Recommender Systems

Gašparić

Ricci

2020

IEEE Access

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Software developers' knowledge of integrated development environment (IDE) directly impacts on their productivity. IDE command recommender systems aim at identifying and convincingly presenting to software developers functionality that can help them to accomplish their daily tasks, without overloading them with well known or useless information. Command recommendation requires the estimation of both the utility of commands and the acceptance of the user for new command recommendations. In this paper, we focus on how and when such recommendations should be presented. We performed a long-term user study and our results show that IDE command recommendation must be presented with adequate descriptions of the commands and good usage examples. It seems that a higher frequency of recommendation notifications could be useful, but it should not be too intrusive, especially while developers are focusing on more demanding tasks. To improve recommendation acceptance rate, researchers should also focus on context-aware algorithms and tailor command recommendation timing.

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Section: A Ide Command Recommender Systemsmentioning

confidence: 99%

Section: A Ide Command Recommender Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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IDE Interaction Support With Command Recommender Systems

Gašparić

Ricci

2020

IEEE Access

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“…In most domains, such as that of text collections or electronic retail, there are sets of frequently co-occurring items [3,19,46,52]. This phenomenon is even more noticeable in the domain of IDE commands, where certain sets of commands have to be used to complete specific tasks [46,56].…”

Section: Codismentioning

confidence: 99%

What to Learn Next: Recommending Commands in a Feature-Rich Environment

Zolaktaf

Murphy

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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Despite an abundance of commands to make tasks easier to perform, the users of feature-rich applications, such as development environments and AutoCAD applications, use only a fraction of the commands available due to a lack of awareness of the existence of many commands. Earlier work has shown that command recommendation can improve the usage of a range of commands available within such applications. In this thesis, we address the command recommendation problem, in which, given the command usage history of a set of users, the objective is to predict a command that is likely useful for the user to learn. We investigate two approaches to address the problem. The first approach is built upon the hypothesis that users of feature-rich applications who have similar features tend to use the same commands, and also, a specific user tends to use commands with similar features. Building on this hypothesis, we describe a supervised learning framework that exploits features from a user-command network to predict new links among users and commands. The second approach is built upon three hypotheses. First, we hypothesize that in feature-rich applications there exists co-occurrence patterns between commands. Second, we hypothesize that users of feature-rich applications have prevalent discovery patterns. Finally, we hypothesize that users need different recommendations based on the time elapsed between their last activity and the time of recommendation. To generate recommendations, we obtain co-occurrence and discovery patterns from the command usage history of a large set of users of the same feature-rich application. Subsequently, for each user, we produce recommendations based on the user's command usage history, co-occurrence and discovery patterns, and time elapsed since the last command usage. We

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“…For example, Sage pops up a tip teaching the user about the Toggle-Comment feature (hotkey: Ctrl+/), if it detects that a programmer is manually commenting several lines of code. Sage focuses on hotkey usage; Spyglass also monitors an IDE user, but focuses on code navigation tasks [33]. Sage also records the number of times a user could have used a feature but did not.…”

Section: Illustrative Examplementioning

confidence: 99%

Capturing and Exploiting IDE Interactions

Schleck

Barr

et al. 2014

Proceedings of the 2014 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming &Amp; Software

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Integrated development environments (IDEs) dominate the production and maintenance of software. Developers interact intensively with their IDEs while working. These interactions reflect a developer's thought process and work habits. By capturing and exploiting comprehensive, fine-grained IDE interactions, we can build intelligent IDEs that improve programmer productivity. This next generation of IDEs will incorporate a general framework to capture and exploit IDE interactions, and create an ecosystem of developer-aware applications and plugins. IDE++ realizes this framework on top of the popular Eclipse IDE and can be downloaded from the Eclipse marketplace. To demonstrate IDE++'s comprehensive and granular capture of interactions, we capture, then faithfully replay, a developer's IDE actions on six nontrivial programming tasks. We built four applications upon IDE++ to illustrate 1) the need for capturing comprehensive, finegrained IDE interactions, and 2) the promise of developeraware IDEs.

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Improving program navigation with an active help system

Cited by 17 publications

References 22 publications

IDE Interaction Support With Command Recommender Systems

IDE Interaction Support With Command Recommender Systems

What to Learn Next: Recommending Commands in a Feature-Rich Environment

Capturing and Exploiting IDE Interactions

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