Context-aware integrated development environment command recommender systems

Gašparić, Marko; Gurbanov, Tural; Ricci, Francesco

doi:10.1109/ase.2017.8115679

Cited by 3 publications

(8 citation statements)

References 14 publications

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“…The model consists of 13 factors that describe the situations in which developers interact with the IDE, including: development activities, characteristics of the source code under development, and state of the IDE instance, described with visible and active GUI elements. The inclusion of the model in CNTX algorithm [8], which is the main recommendation algorithm in CoRe, is supported by the model evaluation results, which show that developers execute different commands in different contexts [22].…”

Section: A Ide Command Recommender Systemsmentioning

confidence: 79%

“…RSs in other domains, such as book or movie, are different, since they operate on much larger datasets with thousands or even millions of items in the catalogue, and they still collect explicit feedback, such as star-ratings or thumbs up/down. 1 It is unlikely that minor adaptations to mainstream algorithms may suffice; in fact, existing research indicates that this is not the case [8], [9].…”

Section: Problem Statementmentioning

confidence: 99%

“…Silva et al [11] performed static source code analysis to identify and rank opportunities for refactoring command executions. Gasparic et al [8] suggested CNTX, an algorithm that observes which commands are executed in which development contexts and trains a regression model to generate recommendations. Damevski et al [7] used IDE-interaction data and applied topic modelling to predict future behaviour of developers, which was ultimately used for command recommendation generation.…”

Section: B Algorithms For Ide Command Recommender Systemsmentioning

confidence: 99%

“…• CNTX 7 : a context-aware algorithm, which was developed by us and it is described in [8]. Each user received recommendations from each algorithm for two consecutive weeks.…”

Section: Long-term User Studymentioning

confidence: 99%

“…The sequence of the algorithms was random, on the individual level, but we assured that there were only small differences between the overall numbers of participants who received recommendations in a certain chronological order. 8 Users had no information on how recommendations were generated.…”

Section: Long-term User Studymentioning

confidence: 99%

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IDE Interaction Support With Command Recommender Systems

Gašparić

Ricci

2020

IEEE Access

Self Cite

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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: 79%

Section: Problem Statementmentioning

confidence: 99%

Section: B Algorithms For Ide Command Recommender Systemsmentioning

confidence: 99%

“…• CNTX 7 : a context-aware algorithm, which was developed by us and it is described in [8]. Each user received recommendations from each algorithm for two consecutive weeks.…”

Section: Long-term User Studymentioning

confidence: 99%

Section: Long-term User Studymentioning

confidence: 99%

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IDE Interaction Support With Command Recommender Systems

Gašparić

Ricci

2020

IEEE Access

Self Cite

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Supporting Software Developers Through a Gaze-Based Adaptive IDE

Weber,

Mourao Thiel,

Mayer

2023

Mensch Und Computer 2023

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A Context Model for Code and API Recommendation Systems based on Programming Onsite Data

Zhang¹,

Tao²,

Yang³

et al. 2019

Int J Performability Eng

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Code and application programming interface (API) recommendation systems are important guarantees for efficient and accurate code reuse to improve the efficiency of software development. Context data plays a key role in code and API recommendation. A large amount of programming onsite data has been generated, but existing code and API recommendation systems rarely consider the context based on programming onsite data, which leads to low efficiency and poor accuracy of code and API recommendation. In this paper, we proposed a context model for code and API recommendation systems. Our context model is based on programming onsite data collected during programming. It includes four aspects: developer, project, time, and environment. Developer data is labeled data abstracted from information according to developers' programming habits and abilities, project data is information about the project, time data is information about temporal aspects of developers interacting with the project, and environment data is all environment elements used by developers during programming. Then, we collected programming onsite data in three ways: explicit collection, implicit collection, and reasoning. Lastly, we built the context model using a coarse-grained abstract model for recommendation. Our context model retains the key information in the code while eliminating redundant information that may affect the accuracy of the recommend task, and it can theoretically improve the efficiency and accuracy of recommendation.

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Context-aware integrated development environment command recommender systems

Cited by 3 publications

References 14 publications

IDE Interaction Support With Command Recommender Systems

IDE Interaction Support With Command Recommender Systems

Supporting Software Developers Through a Gaze-Based Adaptive IDE

A Context Model for Code and API Recommendation Systems based on Programming Onsite Data

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